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Yu TP, Zhang MX, Zhang JY, Gong J, Zhou Q, Chen N. [Pilocytic astrocytoma with KRAS gene mutation: a clinicopathological analysis of two cases]. Zhonghua Bing Li Xue Za Zhi 2024; 53:477-479. [PMID: 38678329 DOI: 10.3760/cma.j.cn112151-20231009-00241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/29/2024]
Affiliation(s)
- T P Yu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - M X Zhang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - J Y Zhang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - J Gong
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Q Zhou
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - N Chen
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
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2
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Wang L, Liu H, Wu Q, Liu Y, Yan Z, Chen G, Shang Y, Xu S, Zhou Q, Yan T, Cheng X. miR-451a was selectively sorted into exosomes and promoted the progression of esophageal squamous cell carcinoma through CAB39. Cancer Gene Ther 2024:10.1038/s41417-024-00774-8. [PMID: 38649419 DOI: 10.1038/s41417-024-00774-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/06/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
Exosomes are emerging mediators of cell-cell communication, which are secreted from cells and may be delivered into recipient cells in cell biological processes. Here, we examined microRNA (miRNA) expression in esophageal squamous cell carcinoma (ESCC) cells. We performed miRNA sequencing in exosomes and cells of KYSE150 and KYSE450 cell lines. Among these differentially expressed miRNAs, 20 of the miRNAs were detected in cells and exosomes. A heat map indicated that the level of miR-451a was higher in exosomes than in ESCC cells. Furthermore, miRNA pull-down assays and combined exosomes proteomic data showed that miR-451a interacts with YWHAE. Over-expression of YWHAE leads to miR-451a accumulation in the exosomes instead of the donor cells. We found that miR-451a was sorted into exosomes. However, the biological function of miR-451a remains unclear in ESCC. Here, Dual-luciferase reporter assay was conducted and it was proved that CAB39 is a target gene of miR-451a. Moreover, CAB39 is related to TGF-β1 from RNA-sequencing data of 155 paired of ESCC tissues and the matched tissues. Western Blot and qPCR revealed that CAB39 and TGF-β1 were positively correlated in ESCC. Over-expression of CAB39 were cocultured with PBMCs from the blood from healthy donors. Flow cytometry assays showed that apoptotic cells were significantly reduced after CAB39 over-expression and significantly increased after treated with TGF-β1 inhibitors. Thus, our data indicate that CAB39 weakens antitumor immunity through TGF-β1 in ESCC. In summary, YWHAE selectively sorted miR-451a into exosomes and it can weaken antitumor immunity promotes tumor progression through CAB39.
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Affiliation(s)
- Lu Wang
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Huijuan Liu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Qinglu Wu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Yiqian Liu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Zhenpeng Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Guohui Chen
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Yao Shang
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Songrui Xu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Qichao Zhou
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Ting Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
| | - Xiaolong Cheng
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
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Wang L, Shan K, Yi Y, Yang H, Zhang Y, Xie M, Zhou Q, Shang M. Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake. Sci Total Environ 2024; 922:171009. [PMID: 38402991 DOI: 10.1016/j.scitotenv.2024.171009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/05/2024] [Accepted: 02/14/2024] [Indexed: 02/27/2024]
Abstract
Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the time-series sequence. This study introduces a hybrid framework for near-real-time CyanoHABs predictions in a cyanobacterium Microcystis-dominated lake - Lake Dianchi, China. The proposed model was validated using hourly Chlorophyll-a (Chl a) concentrations and algal cell densities. Our results demonstrate that applying decomposition-based singular spectrum analysis (SSA) significantly enhances the prediction accuracy of subsequent CyanoHABs models, particularly in the case of temporal convolutional network (TCN). Comparative experiments revealed that the SSA-TCN model outperforms other SSA-based deep learning models for predicting Chl a (R2 = 0.45-0.93, RMSE = 2.29-5.89 μg/L) and algal cell density (R2 = 0.63-0.89, RMSE = 9489.39-16,015.37 cells/mL) at one to four steps ahead predictions. The forecast of bloom intensities achieved a remarkable accuracy of 98.56 % and an average precision rate of 94.04 % ± 0.05 %. In addition, scenarios involving various input combinations of environmental factors demonstrated that water temperature emerged as the most effective driver for CyanoHABs predictions, with a mean RMSE of 2.94 ± 0.12 μg/L, MAE of 1.55 ± 0.09 μg/L, and R2 of 0.83 ± 0.01. Overall, the newly developed approach underscores the potential of a well-designed hybrid deep-learning framework for accurately predicting sensor-based algal parameters. It offers novel perspectives for managing CyanoHABs through online monitoring and artificial intelligence in aquatic ecosystems.
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Affiliation(s)
- Lan Wang
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; School of Artificial Intelligence, Chongqing University of Education, Chongqing 400065, China
| | - Kun Shan
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
| | - Yang Yi
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Hong Yang
- Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK
| | - Yanyan Zhang
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Mingjiang Xie
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Qichao Zhou
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China
| | - Mingsheng Shang
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
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Zhou Q, Xiao H, Zhang L, Zhang HT, Meng J. [Non-steroidal anti-inflammatory drugs-exacerbated respiratory disease: a case report]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2024; 59:383-388. [PMID: 38622023 DOI: 10.3760/cma.j.cn115330-20231108-00194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Affiliation(s)
- Q Zhou
- Department of Otorhinolaryngology, Allergy Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - H Xiao
- Department of Otorhinolaryngology, Allergy Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - L Zhang
- Department of Otorhinolaryngology, Allergy Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - H T Zhang
- Department of Otorhinolaryngology, Allergy Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - J Meng
- Department of Otorhinolaryngology, Allergy Center, West China Hospital, Sichuan University, Chengdu 610041, China
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Peng JR, Li YF, Zhou Q, Yuan GJ, Chen GX. [Invisible orthodontic treatment for unilateral condylar hypertrophy in a patient with openbite after condylectomy: a case report]. Zhonghua Kou Qiang Yi Xue Za Zhi 2024; 59:255-258. [PMID: 38432657 DOI: 10.3760/cma.j.cn112144-20230923-00166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Affiliation(s)
- J R Peng
- Outpatient Department of Zhongshang Square, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Y F Li
- Outpatient Department of Zhongshang Square, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Q Zhou
- Outpatient Department of Zhongshang Square, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - G J Yuan
- Outpatient Department of Zhongshang Square, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - G X Chen
- Outpatient Department of Zhongshang Square, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
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Yang X, Zhou Y, Yu Z, Li J, Yang H, Huang C, Jeppesen E, Zhou Q. Influence of hydrological features on CO 2 and CH 4 concentrations in the surface water of lakes, Southwest China: A seasonal and mixing regime analysis. Water Res 2024; 251:121131. [PMID: 38246081 DOI: 10.1016/j.watres.2024.121131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/03/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024]
Abstract
Due to the large spatiotemporal variability in the processes controlling carbon emissions from lakes, estimates of global lake carbon emission remain uncertain. Identifying the most reliable predictors of CO2 and CH4 concentrations across different hydrological features can enhance the accuracy of carbon emission estimates locally and globally. Here, we used data from 71 lakes in Southwest China varying in surface area (0.01‒702.4 km2), mean depth (< 1‒89.6 m), and climate to analyze differences in CO2 and CH4 concentrations and their driving mechanisms between the dry and rainy seasons and between different mixing regimes. The results showed that the average concentrations of CO2 and CH4 in the rainy season were 23.9 ± 18.8 μmol L-1 and 2.5 ± 4.9 μmol L-1, respectively, which were significantly higher than in the dry season (10.5 ± 10.3 μmol L-1 and 1.8 ± 4.2 μmol L-1, respectively). The average concentrations of CO2 and CH4 at the vertically mixed sites were 24.1 ± 21.8 μmol L-1 and 2.6 ± 5.4 μmol L-1, being higher than those at the stratified sites (14.8 ± 13.4 μmol L-1 and 1.7 ± 3.5 μmol L-1, respectively). Moreover, the environmental factors were divided into four categories, i.e., system productivity (represented by the contents of total nitrogen, total phosphorus, chlorophyll a and dissolved organic matter), physicochemical factors (water temperature, Secchi disk depth, dissolved oxygen and pH value), lake morphology (lake area, water depth and drainage ratio), and geoclimatic factors (altitude, wind speed, precipitation and land-use intensity). In addition to the regression and variance partitioning analyses between the concentrations of CO2 and CH4 and environmental factors, the cascading effects of environmental factors on CO2 and CH4 concentrations were further elucidated under four distinct hydrological scenarios, indicating the different driving mechanisms between the scenarios. Lake morphology and geoclimatic factors were the main direct drivers of the carbon concentrations during the rainy season, while they indirectly affected the carbon concentrations via influencing physicochemical factors and further system productivity during the dry season; although lake morphology and geoclimatic factors directly contributed to the carbon concentrations at the vertically mixed and stratified sites, the direct effect of system productivity was only observed at the stratified sites. Our results emphasize that, when estimating carbon emissions from lakes at broad spatial scales, it is essential to consider the influence of precipitation-related seasons and lake mixing regimes.
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Affiliation(s)
- Xiaoying Yang
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China; Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
| | - Yongqiang Zhou
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Zhirong Yu
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China
| | - Jingyi Li
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China
| | - Hong Yang
- Department of Geography and Environmental Science, University of Reading, Whiteknights, Reading RG6 6AB, United Kingdom
| | - Changchun Huang
- School of Geography, Nanjing Normal University, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
| | - Erik Jeppesen
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China; Department of Ecoscience, Aarhus University, Aarhus 8000, Denmark
| | - Qichao Zhou
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China; Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed, Yunnan Research Academy of Eco-environmental Sciences, Kunming 650034, China.
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Zhou Q, Xu LE, Lin LL, Huang XR, Chi WZ, Lin J, Lin P. Clinical study of tirofiban compared to low molecular weight heparin in the antithrombotic treatment of progressive pontine infarction. Eur Rev Med Pharmacol Sci 2024; 28:2186-2191. [PMID: 38567581 DOI: 10.26355/eurrev_202403_35722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
OBJECTIVE To investigate the efficacy and safety of tirofiban and low molecular weight heparin (LMWH) in the treatment of patients undergoing acute progressive pontine infarction. PATIENTS AND METHODS Patients with acute progressive pontine infarction who were hospitalized in the Neurology Department from June 2021 to June 2023 were included in the study and randomly divided into two groups, namely the experimental group (tirofiban group) and the control group (LMWH group). All patients in both groups were required to receive conventional comprehensive treatment and dual antiplatelet therapy with aspirin + clopidogrel at the beginning of admission. The National Institutes of Health Stroke Scale (NIHSS) score and Barthel Index (BI) were used to evaluate the neurological deficits on the first day of admission, the next day with stroke progression, and at discharge after treatment with tirofiban and LMWH, respectively in the two groups. The modified Rankin Scale was employed to assess prognosis on the 90th day after treatment. Clinical adverse events were followed up for 90 days, comparing the clinical efficacy and safety of the two treatment methods. RESULTS There was no statistical significance in NIHSS score and Barthel Index between the tirofiban group and the LMWH group on the first day of admission and the next day with stroke progression (p > 0.05). After stroke progression, tirofiban and LMWH were separately used for treatment in the two groups. We found that the NIHSS score of the tirofiban group was lower than that of the LMWH group, and the Barthel Index score was higher than that of the LMWH group at discharge (p < 0.05). After three months of follow-up, the mRS score of the tirofiban group was dramatically higher than that of the LMWH group (p < 0.05). No significant harmful or adverse reactions, such as bleeding events, were found in the two groups (p > 0.05). CONCLUSIONS Tirofiban may be more effective and safer than LMWH in controlling the progression of acute pontine infarction, but further and large-sample studies are still needed to confirm this finding.
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Affiliation(s)
- Q Zhou
- Department of Neurology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
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Mei T, Zhou Q, Gong Y. Comparison of the Efficacy and Safety of Perioperative Immunochemotherapeutic Strategies for Resectable Non-small Cell Lung Cancer: a Systematic Review and Network Meta-analysis. Clin Oncol (R Coll Radiol) 2024; 36:107-118. [PMID: 38151439 DOI: 10.1016/j.clon.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/07/2023] [Accepted: 12/19/2023] [Indexed: 12/29/2023]
Abstract
AIMS The aim of this network meta-analysis was to elucidate the efficacy and safety of various immune checkpoint inhibitors (ICIs) used in combination with chemotherapy for the treatment of non-small cell lung cancer (NSCLC). MATERIALS AND METHODS Data from randomised controlled trials comparing perioperative ICI-chemotherapy and chemotherapy alone were acquired from the EMBASE, Web of Science, Cochrane Library databases, PubMed, and meeting abstracts from inception until August 2023. The endpoints for this analysis were pathological complete response, event-free survival and treatment-related adverse events of any grade or adverse events of grade 3 or higher. RESULTS In total, six randomised controlled trials with 2538 NSCLC patients were selected for this network meta-analysis. Compared with other ICIs, toripalimab + chemotherapy demonstrated increased pathological complete response rates and prolonged event-free survival in NSCLC. In patients with negative/low PD-L1 expression or squamous cell pathology, toripalimab + chemotherapy was the most effective regimen. In contrast, nivolumab + chemotherapy was preferable for patients with high PD-L1 expression or non-squamous cell pathology. Among the analysed regimens, toripalimab + chemotherapy presented the highest risk of adverse events of any grade, whereas nivolumab + chemotherapy showed the highest risk of grade 3-4 adverse events. Conversely, durvalumab + chemotherapy exhibited the lowest risk of grade 3-4 adverse events. CONCLUSIONS Among the evaluated perioperative immunochemotherapy regimens, toripalimab + chemotherapy indicated a significantly increased survival benefit for most resectable NSCLC patients. However, for high PD-L1 expression and non-squamous NSCLC patients, nivolumab + chemotherapy provided the most potent outcomes. Perioperative durvalumab + chemotherapy is a relatively safe treatment. The findings of this investigation are expected to assist clinicians in making informed decisions among promising treatment options.
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Affiliation(s)
- T Mei
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, PR China; Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China
| | - Q Zhou
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China.
| | - Y Gong
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, PR China.
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Du ZQ, Jiang Y, Lu RR, Zhou Q, Zhu HH, Shen Y. Clinical pharmacist intervention in contraindications of the co-administration of cefoperazone and ambroxol hydrochloride injection. Eur Rev Med Pharmacol Sci 2024; 28:1610-1613. [PMID: 38436193 DOI: 10.26355/eurrev_202402_35490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
BACKGROUND Clinical pharmacists identified contraindications in two cases concerning the co-administration of cefoperazone and ambroxol hydrochloride injection, prompting a thorough investigation. CASE PRESENTATION Clinically, two cases of contraindications for the co-administration of cefoperazone and ambroxol hydrochloride injection were discovered. After the intervention and analysis by clinical pharmacists, the possible reason could be the precipitation of free alkali due to the immediate administration of ambroxol after the infusion of cefoperazone. Clinical pharmacists suggested avoiding the co-administration of the two and recommended flushing the intravenous lines with 5% glucose injection or 0.9% sodium chloride injection during intravenous infusion to prevent direct drug interaction causing precipitation, thereby reducing the occurrence of adverse events. No adverse events occurred after the intervention, and no harm was caused to the patients. CONCLUSIONS The co-administration of cefoperazone and ambroxol hydrochloride injection can lead to the precipitation of free alkali, posing a risk of adverse events. Clinical pharmacists' intervention could prevent this interaction. This practice has been shown to be effective, with no subsequent adverse events reported.
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Affiliation(s)
- Z-Q Du
- Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China.
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Zhou Q, Zhu D, Wang YT, Dong WY, Yang J, Wen J, Liu J, Yang N, Zhao D, Hua XW, Tang YD. [The association between body mass index and in-hospital major adverse cardiovascular and cerebral events in patients with acute coronary syndrome]. Zhonghua Xin Xue Guan Bing Za Zhi 2024; 52:42-48. [PMID: 38220454 DOI: 10.3760/cma.j.cn112148-20230915-00165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Objective: To assess the association between body mass index (BMI) and major adverse cardiovascular and cerebrovascular events (MACCE) among patients with acute coronary syndrome (ACS). Methods: This was a multicenter prospective cohort study, which was based on the Improving Care for Cardiovascular Disease in China (CCC) project. The hospitalized patients with ACS aged between 18 and 80 years, registered in CCC project from November 1, 2014 to December 31, 2019 were included. The included patients were categorized into four groups based on their BMI at the time of admission: underweight (BMI<18.5 kg/m2), normal weight (BMI between 18.5 and 24.9 kg/m2), overweight (BMI between 25.0 and 29.9 kg/m2), and obese (BMI≥30.0 kg/m2). Multivariate logistic regression models was used to analyze the relationship between BMI and the risk of in-hospital MACCE. Results: A total of 71 681 ACS inpatients were included in the study. The age was (63.4±14.7) years, and 26.5% (18 979/71 681) were female. And the incidence of MACCE for the underweight, normal weight, overweight, and obese groups were 14.9% (322/2 154), 9.5% (3 997/41 960), 7.9% (1 908/24 140) and 7.0% (240/3 427), respectively (P<0.001). Multivariate logistic regression analysis showed a higher incidence of MACCE in the underweight group compared to the normal weight group (OR=1.30, 95%CI 1.13-1.49, P<0.001), while the overweight and obese groups exhibited no statistically significant difference in the incidence of MACCE compared to the normal weight group (both P>0.05). Conclusion: ACS patients with BMI below normal have a higher risk of in-hospital MACCE, suggesting that BMI may be an indicator for evaluating short-term prognosis in ACS patients.
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Affiliation(s)
- Q Zhou
- Department of Cardiology, Fuwai Hospital and Cardiovascular Institute, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - D Zhu
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Research Unit of Medical Science Research Management/Basic and Clinical Research of Metabolic Cardiovascular Diseases, Chinese Academy of Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China
| | - Y T Wang
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Research Unit of Medical Science Research Management/Basic and Clinical Research of Metabolic Cardiovascular Diseases, Chinese Academy of Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China
| | - W Y Dong
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Research Unit of Medical Science Research Management/Basic and Clinical Research of Metabolic Cardiovascular Diseases, Chinese Academy of Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China
| | - J Yang
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Research Unit of Medical Science Research Management/Basic and Clinical Research of Metabolic Cardiovascular Diseases, Chinese Academy of Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China
| | - J Wen
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Research Unit of Medical Science Research Management/Basic and Clinical Research of Metabolic Cardiovascular Diseases, Chinese Academy of Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China
| | - J Liu
- Center of Clinical and Epidemiology Researches, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - N Yang
- Center of Clinical and Epidemiology Researches, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - D Zhao
- Center of Clinical and Epidemiology Researches, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - X W Hua
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Research Unit of Medical Science Research Management/Basic and Clinical Research of Metabolic Cardiovascular Diseases, Chinese Academy of Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China
| | - Y D Tang
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Research Unit of Medical Science Research Management/Basic and Clinical Research of Metabolic Cardiovascular Diseases, Chinese Academy of Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China
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11
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Liu X, Onda M, Schlomer J, Bassel L, Kozlov S, Tai CH, Zhou Q, Liu W, Tsao HE, Hassan R, Ho M, Pastan I. Tumor resistance to anti-mesothelin CAR-T cells caused by binding to shed mesothelin is overcome by targeting a juxtamembrane epitope. Proc Natl Acad Sci U S A 2024; 121:e2317283121. [PMID: 38227666 PMCID: PMC10823246 DOI: 10.1073/pnas.2317283121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/27/2023] [Indexed: 01/18/2024] Open
Abstract
Despite many clinical trials, CAR-T cells are not yet approved for human solid tumor therapy. One popular target is mesothelin (MSLN) which is highly expressed on the surface of about 30% of cancers including mesothelioma and cancers of the ovary, pancreas, and lung. MSLN is shed by proteases that cleave near the C terminus, leaving a short peptide attached to the cell. Most anti-MSLN antibodies bind to shed MSLN, which can prevent their binding to target cells. To overcome this limitation, we developed an antibody (15B6) that binds next to the membrane at the protease-sensitive region, does not bind to shed MSLN, and makes CAR-T cells that have much higher anti-tumor activity than a CAR-T that binds to shed MSLN. We have now humanized the Fv (h15B6), so the CAR-T can be used to treat patients and show that h15B6 CAR-T produces complete regressions in a hard-to-treat pancreatic cancer patient derived xenograft model, whereas CAR-T targeting a shed epitope (SS1) have no anti-tumor activity. In these pancreatic cancers, the h15B6 CAR-T replicates and replaces the cancer cells, whereas there are no CAR-T cells in the tumors receiving SS1 CAR-T. To determine the mechanism accounting for high activity, we used an OVCAR-8 intraperitoneal model to show that poorly active SS1-CAR-T cells are bound to shed MSLN, whereas highly active h15B6 CAR-T do not contain bound MSLN enabling them to bind to and kill cancer cells.
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Affiliation(s)
- X.F. Liu
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - M. Onda
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - J. Schlomer
- Center for Advanced Preclinical Research, Frederick National Lab for Cancer Research Center for Cancer Research, National Cancer Institute, NIH, Frederick, MD 21701
| | - L. Bassel
- Center for Advanced Preclinical Research, Frederick National Lab for Cancer Research Center for Cancer Research, National Cancer Institute, NIH, Frederick, MD 21701
| | - S. Kozlov
- Center for Advanced Preclinical Research, Frederick National Lab for Cancer Research Center for Cancer Research, National Cancer Institute, NIH, Frederick, MD 21701
| | - C.-H. Tai
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - Q. Zhou
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - W. Liu
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - H.-E. Tsao
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - R. Hassan
- Thoracic and Gastrointestinal Malignancies Branch, National Cancer Institute, NIH, Bethesda, MD20892
| | - M. Ho
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - I. Pastan
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
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12
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Wu YL, Zhou Q. [Clinical pathway in Chinese county for lung cancer diagnosis and treatment (2023 edition)]. Zhonghua Zhong Liu Za Zhi 2024; 46:19-39. [PMID: 38246778 DOI: 10.3760/cma.j.cn112152-20230928-00162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Lung cancer (LC) is the leading cause of death among patients with cancer both in worldwide and China. China accounts for 11.4% of the total number of cancer cases and 18.0% of the total number of cancer deaths in the world. Standardizing the diagnosis and treatment of LC is a key measure to improve the survival rate of LC patients and reduce the mortality rate. However, county hospitals generally face the problem of inaccessibility to advanced diagnostic and treatment technologies. Therefore, when developing quality control standards and clinical diagnosis and treatment specifications, it is necessary to combine the actual situation of county hospitals and formulate specific recommendations. The recommendations of treatment measures also need to consider the approval status of indications and whether it is included in the National Reimbursement Drug List (NRDL), to ensure the access to medicines. In order to solve the above problems, based on existing guidelines at home and abroad and the clinical work characteristics of county hospitals, the first clinical pathway in Chinese county for LC diagnosis and treatment (2023 edition) was compiled. This pathway elaborated on the imaging diagnosis, pathological diagnosis, molecular testing, and precision medicine based on histological-pathological types, tumor-node-metastasis (TNM) classification, and molecular classification, developed different diagnosis and treatment processes for different types of LC patients. Simultaneously, according to the actual work situation of county hospitals, the diagnosis and treatment recommendations in clinical scenarios are divided into basic strategies and optional strategies for elaboration. The basic strategies are the standards that county hospitals must meet, while the optional strategies provide more choices for hospitals, which are convenient for county doctors to put into clinical practice. All the recommended diagnostic and treatment plans strictly refer to existing guidelines and consensus, ensuring the scientificity.
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Affiliation(s)
- Y L Wu
- Guangdong Provincial People's Hospital, Guangdong Lung Cancer Institute, Guangzhou, 519041, China
| | - Q Zhou
- Department of Pulmonary Medicine Ⅱ, Guangdong Provincial People's Hospital, Guangzhou 519041, China
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13
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Chen Y, Liu J, Zhou Q, Liu L, Wang D. A study on rapid simulation of mine roadway fires for emergency decision-making. Sci Rep 2024; 14:1674. [PMID: 38243052 PMCID: PMC10799019 DOI: 10.1038/s41598-024-51900-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/10/2024] [Indexed: 01/21/2024] Open
Abstract
In traditional mine fire simulation, the FDS simulation software has been verified by large-scale and full-size fire experiments. The resulting calculations closely align with real-world scenarios, making it a valuable tool for simulating mine fires. However, when a fire occurs in a mine, utilizing FDS software to predict the fire situation in the mine entails a sequence of steps, including modeling, environmental parameter setting, arithmetic, and data processing, which takes time in terms of days, thus making it difficult to meet the demand for emergency decision-making timelines. To address the need for rapid predictions of mine tunnel fire development, a method for swiftly estimating environmental parameters and the concentration of causative factors at various times and locations post-fire has been devised. FDS software was employed to simulate numerous roadway fires under diverse conditions. Parameters such as fire source intensity, roadway cross-sectional area, roadway wind speed, roadway inclination angle, time, and others were utilized as the input layer for a neural network. In contrast, wind flow temperature, carbon monicide (CO) concentration, fire wind pressure, visibility, and others were designated as the output layer for training the neural network model. This approach established a fire prediction model to resolve issues related to time-consuming numerical simulations and the inability to provide a rapid response to disaster emergencies. The trained neural network model can instantaneously predict the environmental parameters and concentrations of the causative factors at different times and locations. The model exhibits an average relative error of 12.12% in temperature prediction, a mean absolute error of 0.87 m for visibility, a mean absolute error of 3.49 ppm for CO concentration, and a mean absolute error of 16.78 Pa for fire wind pressure. Additionally, the mean relative error in density is 2.9%. These predictions serve as crucial references for mine fire emergency decision-making.
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Affiliation(s)
- Yangqin Chen
- College of Safety Science and Engineering, Liaoning Technical University, Huludao, 125105, Liaoning, China
- Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao, 125105, Liaoning, China
| | - Jian Liu
- College of Safety Science and Engineering, Liaoning Technical University, Huludao, 125105, Liaoning, China.
- Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao, 125105, Liaoning, China.
| | - Qichao Zhou
- Software (Artificial Intelligence) Institute, Liaoning Technical University, Huludao, 125105, Liaoning, China
| | - Li Liu
- College of Safety Science and Engineering, Liaoning Technical University, Huludao, 125105, Liaoning, China
- Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao, 125105, Liaoning, China
| | - Dong Wang
- Liaoning Academy of Mineral Resources Development and Utilization Technical and Equipment Research Institute, Liaoning Technical University, Fuxin, 123000, China
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Wang L, Liu H, Liu Y, Guo S, Yan Z, Chen G, Wu Q, Xu S, Zhou Q, Liu L, Peng M, Cheng X, Yan T. Potential markers of cancer stem-like cells in ESCC: a review of the current knowledge. Front Oncol 2024; 13:1324819. [PMID: 38239657 PMCID: PMC10795532 DOI: 10.3389/fonc.2023.1324819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/01/2023] [Indexed: 01/22/2024] Open
Abstract
In patients with esophageal squamous cell carcinoma (ESCC), the incidence and mortality rate of ESCC in our country are also higher than those in the rest of the world. Despite advances in the treatment department method, patient survival rates have not obviously improved, which often leads to treatment obstruction and cancer repeat. ESCC has special cells called cancer stem-like cells (CSLCs) with self-renewal and differentiation ability, which reflect the development process and prognosis of cancer. In this review, we evaluated CSLCs, which are identified from the expression of cell surface markers in ESCC. By inciting EMTs to participate in tumor migration and invasion, stem cells promote tumor redifferentiation. Some factors can inhibit the migration and invasion of ESCC via the EMT-related pathway. We here summarize the research progress on the surface markers of CSLCs, EMT pathway, and the microenvironment in the process of tumor growth. Thus, these data may be more valuable for clinical applications.
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Affiliation(s)
- Lu Wang
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Huijuan Liu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yiqian Liu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Shixing Guo
- Clinical Laboratory Medicine Centre, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Zhenpeng Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Guohui Chen
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Qinglu Wu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Songrui Xu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Qichao Zhou
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Lili Liu
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Meilan Peng
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaolong Cheng
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ting Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
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Zhang M, Lu Y, Sun H, Hou C, Zhou Z, Liu X, Zhou Q, Li Z, Yin Y. CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study. Transl Oncol 2024; 39:101804. [PMID: 37839176 PMCID: PMC10587766 DOI: 10.1016/j.tranon.2023.101804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/11/2023] [Accepted: 10/09/2023] [Indexed: 10/17/2023] Open
Abstract
PURPOSE To evaluate and validate CT-based models using pre- and posttreatment deep learning radiomics features and hematological biomarkers for assessing esophageal squamous cell carcinoma (ESCC) pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT). MATERIAL AND METHODS This retrospective study recruited patients with biopsy-proven ESCC who underwent nCRT from two Chinese hospitals between May 2017 and May 2022, divided into a training set (hospital I, 111 cases), an internal validation set (hospital I, 47 cases), and an external validation set (hospital II, 33 cases). We used minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) as feature selection methods and three classifiers as model construction methods. The assessment of models was performed using area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA). RESULTS A total 190 patients were included in our study (60.8 ± 7.08 years, 133 men), and seventy-seven of them (40.5 %) achieved pCR. The logistic regression (LR)-based combined model incorporating neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio, albumin, and radscores performed well both in the internal and external validation sets with AUCs of 0.875 and 0.857 (95 % CI, 0.776-0.964; 0.731-0.984, P <0.05), respectively. DCA demonstrated that nomogram was useful for pCR prediction and produced clinical net benefits. CONCLUSION The incorporation of radscores and hematological biomarkers into LR-based model improved pCR prediction after nCRT in ESCC. Enhanced pCR predictability may improve patients selection before surgery, providing clinical application value for the use of active surveillance.
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Affiliation(s)
- Meng Zhang
- Shandong University Cancer Center, Shandong University, Jinan, Shandong, China; Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yukun Lu
- Department of Radiation Oncology, Anyang Tumor Hospital, Anyang, Henan, China
| | - Hongfu Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Chuanke Hou
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing, China
| | - Zichun Zhou
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong, China
| | - Xiao Liu
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - Qichao Zhou
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - Zhenjiang Li
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
| | - Yong Yin
- Shandong University Cancer Center, Shandong University, Jinan, Shandong, China; Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
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16
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Peng X, Zhou Q, Wang CQ, Zhang ZM, Luo Z, Xu SY, Feng B, Fang ZF, Lin Y, Zhuo Y, Jiang XM, Zhao H, Tang JY, Wu D, Che LQ. Dietary supplementation of proteases on growth performance, nutrient digestibility, blood characteristics and gut microbiota of growing pigs fed sorghum-based diets. Animal 2024; 18:101052. [PMID: 38181459 DOI: 10.1016/j.animal.2023.101052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 12/02/2023] [Accepted: 12/04/2023] [Indexed: 01/07/2024] Open
Abstract
Low-tannin sorghum is an excellent energy source in pig diets. However, sorghum contains several anti-nutritional factors that may have negative effects on nutrient digestibility. The impacts of proteases on growth performance, nutrient digestibility, blood parameters, and gut microbiota of growing pigs fed sorghum-based diets were studied in this study. Ninety-six pigs (20.66 ± 0.65 kg BW) were allocated into three groups (eight pens/group, four pigs/pen): (1) CON (control diet, sorghum-based diet included 66.98% sorghum), (2) PRO1 (CON + 200 mg/kg proteases), (3) PRO2 (CON + 400 mg/kg proteases) for 28 d. No differences were observed in growth performance and apparent total tract digestibility (ATTD) of nutrients between CON and PRO1 groups. Pigs fed PRO2 diet had increased (P < 0.05) BW on d 21 and 28, and increased (P < 0.05) average daily gain during d 14-21 and the overall period compared with pigs fed CON diet. In addition, pigs fed PRO2 diet had improved (P < 0.05) ATTD of gross energy, CP, and DM compared with pigs fed CON and PRO1 diets. Pigs fed PRO2 diet had lower (P < 0.05) plasma globulin (GLB) level and higher (P < 0.05) plasma glucose, albumin (ALB) and immunoglobulin G levels, and ALB/GLB ratio than pigs fed CON and PRO1 diets. Furthermore, pigs fed PRO2 diet had decreased (P < 0.05) the relative abundance of Acidobacteriota at the phylum level and increased (P < 0.05) the relative abundance of Prevotella_9 at the genus level. The linear discriminant analysis effect size analysis also showed that pigs fed PRO2 diet had significantly enriched short-chain fatty acid-producing bacteria, such as Subdoligranulum and Parabacteroides. In conclusion, protease supplementation at 400 mg/kg improved the growth performance of growing pigs fed sorghum-based diets, which may be attributed to the improvement of nutrient digestibility, host metabolism, immune status and associated with the altered gut microbiota profiles.
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Affiliation(s)
- X Peng
- Laboratory for Bio-feed and Molecular Nutrition, College of Animal Science and Technology, Southwest University, Chongqing 400715, China; Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - Q Zhou
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - C Q Wang
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - Z M Zhang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Z Luo
- Kemin (China) Technologies Co., Ltd., Sanzao, Zhuhai 519040, China
| | - S Y Xu
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - B Feng
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - Z F Fang
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - Y Lin
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - Y Zhuo
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - X M Jiang
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - H Zhao
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - J Y Tang
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - D Wu
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China
| | - L Q Che
- Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu 611130, China.
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Zhao HY, Han JT, Hu DH, Zhou Q, Zhu C, Xu J, Zhang BW, Qi ZS, Liu JQ. [A randomized controlled trial on the effect of exercise prescription based on a progressive mode in treating elderly patients with lower limb dysfunction after deep burns]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2023; 39:1122-1130. [PMID: 38129298 DOI: 10.3760/cma.j.cn501225-20230721-00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Objective: To explore the effect of exercise prescription based on a progressive mode in treating elderly patients with lower limb dysfunction after deep burns. Methods: A randomized controlled trial was conducted. From January 2021 to January 2023, 60 elderly patients with lower limb dysfunction after deep burns who met the inclusion criteria were admitted to the First Affiliated Hospital of Air Force Medical University. The patients were divided into conventional rehabilitation group (30 cases, 17 males and 13 females, aged (65±3) years) and combined rehabilitation group (30 cases, 16 males and 14 females, aged (64±3) years) according to the random number table. For patients in both groups, the red-light treatment was started after the lower limb wounds healed or when the total area of scattered residual wounds was less than 1% of the total body surface area. After 2 weeks of red-light treatment, the patients in conventional rehabilitation group were given conventional rehabilitation treatments, including joint stretching, resistance, and balance training; in addition to conventional rehabilitation treatments, the patients in combined rehabilitation group were given exercise prescription training based on a progressive mode three times a week, mainly including dumbbell press, Bobath ball horizontal support, and high-level pulldown trainings. The training time for patients in both groups was 12 weeks. Before training (after 2 weeks of red-light treatment) and after 12 weeks of training, the upper limb and lower limb motor functions of the patients were evaluated using the simple Fugl-Meyer scale, the physical fitness of patients was evaluated using the simple physical fitness scale, and the patient's risk of falling was evaluated by the time consumed for the timed up and go test. The adverse events of patients that occurred during training were recorded. After 12 weeks of training, a self-designed satisfaction survey was conducted to investigate patients' satisfaction with the training effect. Data were statistically analyzed with independent sample t test, paired sample t test, Mann-Whitney U test, Wilcoxon signed rank test, and chi-square test. Results: Before training, the scores of upper limb and lower limb motor functions of patients between the two groups were similar (P>0.05). After 12 weeks of training, the scores of upper limb motor function of patients in conventional rehabilitation group and combined rehabilitation group were significantly higher than those before training (with t values of -11.42 and -13.67, respectively, P<0.05), but there was no statistically significant difference between the two groups (P>0.05). The score of lower limb motor function of patients in combined rehabilitation group was 28.9±2.6, which was significantly higher than 26.3±2.6 in conventional rehabilitation group (t=-3.90, P<0.05), and the scores of lower limb motor function of patients in conventional rehabilitation group and combined rehabilitation group were significantly higher than those before training (with t values of -4.14 and -6.94, respectively, P<0.05). Before training, the individual and total scores of physical fitness of patients between the two groups were similar (P>0.05). After 12 weeks of training, the balance ability score, walking speed score, chair sitting score, and total score of physical fitness of patients in conventional rehabilitation group and combined rehabilitation group were significantly increased compared with those before training (with Z values of -4.38, -3.55, -3.88, -4.65, -4.58, -4.68, -4.42, and -4.48, respectively, P<0.05), and the balance ability score, walking speed score, chair sitting score, and total score of physical fitness of patients in combined rehabilitation group were significantly increased compared with those in conventional rehabilitation group (with Z values of -3.93, -3.41, -3.19, and -5.33, P<0.05). Before training, the time consumed for the timed up and go test for patient's risk of falling in the two groups was close (P>0.05). After 12 weeks of training, the time consumed for the timed up and go test for patient's risk of falling in combined rehabilitation group was (28.0±2.1) s, which was significantly shorter than (30.5±1.8) s in conventional rehabilitation group (t=4.94, P<0.05). Moreover, the time consumed for the timed up and go test for patient's risk of falling in both conventional rehabilitation group and combined rehabilitation group was significantly shorter than that before training (with t values of 14.80 and 15.86, respectively, P<0.05). During the training period, no adverse events such as muscle tissue strain, edema, or falling occurred in any patient. After 12 weeks of training, the satisfaction score of patients with the training effect in combined rehabilitation group was 13.5±1.2, which was significantly higher than 8.5±1.4 in conventional rehabilitation group (t=21.78, P<0.05). Conclusions: The exercise prescription training based on a progressive mode can significantly promote the recovery of lower limb motor function and physical fitness of elderly patients with lower limb dysfunction after deep burns, and effectively reduce the patient's risk of falling without causing adverse events during the training period, resulting in patient's high satisfaction with the training effect.
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Affiliation(s)
- H Y Zhao
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - J T Han
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - D H Hu
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - Q Zhou
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - C Zhu
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - J Xu
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - B W Zhang
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - Z S Qi
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - J Q Liu
- Department of Burns and Cutaneous Surgery, Burn Center of PLA, the First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
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Meng L, Xue J, Zhao C, Huang T, Yang H, Zhao K, Yu Z, Yuan L, Zhou Q, Kellerman AM, McKenna AM, Spencer RGM, Huang C. N-containing dissolved organic matter promotes dissolved inorganic carbon supersaturation in the Yangtze River, China. Water Res 2023; 247:120808. [PMID: 37924684 DOI: 10.1016/j.watres.2023.120808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/24/2023] [Accepted: 10/28/2023] [Indexed: 11/06/2023]
Abstract
Dissolved inorganic carbon (DIC) represents a major global carbon pool and the flux from rivers to oceans has been observed to be increasing. The effect of weathering with respect to increasing DIC has been widely studied in recent decades; however, the influence of dissolved organic matter (DOM) on increasing DIC in large rivers remains unclear. This study employed stable carbon isotopes and Fourier transform ion cyclotron mass spectrometry (FT-ICR MS) to investigate the effect of the molecular composition of DOM on the DIC in the Yangtze River. The results showed that organic matter is an important source of DIC in the Yangtze River, accounting for 40.0 ± 12.1 % and 32.0 ± 7.2 % of DIC in wet and dry seasons, respectively, and increased along the river by approximately three times. Nitrogen (N)-containing DOM, an important composition in DOM with a percentage of ∼40 %, showed superior oxidation state than non N-containing DOM, suggesting that the presence of N could improve the degradable potential of DOM. Positive relationship between organic sourced DIC (DICOC) and N-containing DOM formulae indicated that N-containing DOM is crucial to facilitate the mineralization of DOM to DICOC. N-containg molecular formular with low H/C and O/C ratio were positively correlated with DICOC further verified these energy-rich and biolabile compounds are preferentially decomposed by bacteria to produce DIC. N-containing components significantly accelerated the degradation of DOM to DICOC, which is important for understanding the CO2 emission and carbon cycling in large rivers.
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Affiliation(s)
- Lize Meng
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, PR China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China
| | - Jingya Xue
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, PR China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China
| | - Chu Zhao
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, PR China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China
| | - Tao Huang
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, PR China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China.
| | - Hao Yang
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, PR China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China
| | - Kan Zhao
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China
| | - Zhaoyuan Yu
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, PR China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China
| | - Linwang Yuan
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, PR China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China
| | - Qichao Zhou
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, PR China
| | - Anne M Kellerman
- Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
| | - Amy M McKenna
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL 32310, USA; Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA
| | - Robert G M Spencer
- Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
| | - Changchun Huang
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, PR China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, PR China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, PR China; School of Geography, Nanjing Normal University, Nanjing 210023, PR China.
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Yan ZC, Jiang N, Zhang HX, Zhou Q, Liu XL, Sun F, Yang RM, He HB, Zhao ZG, Zhu ZM. [Efficacy and feasibility of catheter-based adrenal ablation on Cushing's syndrome associated hypertension]. Zhonghua Xin Xue Guan Bing Za Zhi 2023; 51:1152-1159. [PMID: 37963750 DOI: 10.3760/cma.j.cn112148-20230801-00045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Objective: To explore the value of catheter-based adrenal ablation in treating Cushing's syndrome (CS)-associated hypertension. Methods: A clinical study was conducted in patients with CS, who received catheter-based adrenal ablation between March 2018 and July 2023 in Daping Hospital. Parameters monitored were blood pressure (outpatient and 24-hour ambulatory), body weight, clinical characteristics, serum cortisol and adrenocorticotropic hormone (ACTH) at 8 am, 24-hour urinary free cortisol (24 h UFC), fasting blood glucose and postoperative complications. Procedure effectiveness was defined as blood pressure returning to normal levels (systolic blood pressure<140 mmHg (1 mmHg=0.133 kPa) and diastolic blood pressure<90 mmHg), cortisol and 24 h UFC returning to normal and improvement of clinical characteristics. The parameters were monitored during follow up in the outpatient department at 1, 3, 6, and 12 months after catheter-based adrenal ablation. Results: A total of 12 patients (aged (40.0±13.2) years) were reviewed. There were 5 males, with 5 cases of adenoma and 7 with hyperplasia from imaging studies. Catheter-based adrenal ablation was successful in all without interruption or surgical conversion. No postoperative complication including bleeding, puncture site infection, adrenal artery rupture or adrenal bleeding was observed. The mean follow up was 28 months. Compared to baseline values, body weight declined to (59.48±11.65) kg from (64.81±10.75) kg (P=0.008), fasting blood glucose declined to (4.54±0.83) mmol from (5.53±0.99) mmol (P=0.044), outpatient systolic blood pressure declined to (128±21) mmHg from (140±19) mmHg (P=0.005), diastolic blood pressure declined to (78±10) mmHg from (86±11) mmHg (P=0.041), and the mean ambulatory daytime diastolic blood pressure declined to (79±12) mmHg from (89±8) mmHg (P=0.034). Catheter-based adrenal ablation in 8 patients was defined as effective with their 24 h UFC significantly reduced after the procedure (1 338.41±448.06) mmol/L from (633.66±315.94) mmol/L, P=0.011). The change of 24 h UFC between the effective treatment group and ineffective group was statistically significant (P=0.020). The postoperative systolic blood pressure in the treated adenoma group was significantly lower than those of hyperplasia group (112±13) mmHg vs. (139±20) mmHg, P=0.026). Conclusions: For patients with CS-associated hypertension who are unwilling or unable to undergo surgical treatment, catheter-based adrenal ablation could improve the blood pressure and cortisol level. Catheter-based adrenal ablation could be a safe, effective, and minimally invasive therapy. However, our results still need to be validated in further large-scale studies.
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Affiliation(s)
- Z C Yan
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - N Jiang
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - H X Zhang
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - Q Zhou
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - X L Liu
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - F Sun
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - R M Yang
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - H B He
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - Z G Zhao
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
| | - Z M Zhu
- Department of Hypertension and Endocrinology, Center for Hypertension and Cardiometabolic Diseases, Daping Hospital, Army Medical University, Chongqing Institute of Hypertension, Chongqing 400042, China
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20
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Wen CJ, Wang MH, Yu P, Zhou Q. [Advances in clinical significance and detection methods research of high density lipoprotein subfractions]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:1901-1907. [PMID: 38008584 DOI: 10.3760/cma.j.cn112150-20230220-00134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/28/2023]
Abstract
High density lipoprotein (HDL) is an important biochemical index of clinical cardiovascular disease. Many new studies have demonstrated abnormalities of plasma HDL subfractions in patients with this disease,and their clinical significance is greater than the overall abnormalities of HDL. Therefore,the HDL subfraction as an important factor in cardiovascular disease has attracted extensive research and attention. This article summarizes current research on HDL subfractions,their measurements and their relationships with atherosclerosis and coronary artery disease.
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Affiliation(s)
- C J Wen
- Jinyu School of Laboratory Medicine,Guangzhou Medical University, Guangzhou 510260,China
| | - M H Wang
- Laboratory Department, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260,China
| | - P Yu
- Laboratory Department, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260,China
| | - Q Zhou
- Laboratory Department, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260,China
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He YZ, Zhou Q, Deng WY, Huang LY, Lu YY, Ruan YY, Du H. Clinical characteristics and prognostic factors of surgical treatment in children with brainstem tumor. Eur Rev Med Pharmacol Sci 2023; 27:10926-10934. [PMID: 38039022 DOI: 10.26355/eurrev_202311_34460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
OBJECTIVE Brainstem tumors present a significant challenge in surgical treatment, and the prognostic factors in children are lacking. This study aimed to investigate clinical characteristics and prognostic factors of surgical treatment in children with brainstem tumors. PATIENTS AND METHODS 50 children with brainstem tumors who underwent surgical treatment, including frameless- or frame-based stereotactic biopsy and resection, were included and followed up for clinical and biological analysis. Factors of outcomes were assessed by univariate and multivariate analysis. RESULTS 27 cases (54.0%) underwent resection in all children with brainstem tumors. The rate of resection reached as high as 81.8% in children with non-diffuse intrinsic pontine glioma (DIPG), while in children with DIPG, biopsy was performed in the majority, and resection was obtained in the minority with focal necrosis. A rare complication was found following the surgery. Multivariate analysis considered World Health Organization (WHO) grade 3-4, with hazard ratio (HR)=4.48, 95% confidence interval (CI) of 2.84-8.69, p=0.001, H3K27M mutation (HR=2.50, 95% CI 1.73-5.69, p=0.015), and hydrocephalus (HR=2.17, 95% CI 1.08-5.32, p=0.014) as independent adverse prognostic factors. For Kaplan-Meier analysis, children with WHO grade 3-4, Ki-67 LI ≥ 20%, TP53 mutation, H3K27M mutation, DIPG, and hydrocephalus had significantly decreased overall survival (OS). CONCLUSIONS A high rate of resection has been obtained in non-DIPG, and surgical intervention is remarkably safe and efficient for children with brainstem tumors. WHO grade 3-4, H3K27M mutation, and hydrocephalus indicate poor prognosis in children with brainstem tumors.
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Affiliation(s)
- Y-Z He
- Department of Neurosurgery, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Li Z, Raldow AC, Weidhaas JB, Zhou Q, Qi XS. Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT. Cancers (Basel) 2023; 15:5142. [PMID: 37958316 PMCID: PMC10647315 DOI: 10.3390/cancers15215142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/07/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023] Open
Abstract
Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.
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Affiliation(s)
- Zirong Li
- Manteia Medical Technologies Co., Milwaukee, WI 53226, USA;
| | - Ann C. Raldow
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
| | - Joanne B. Weidhaas
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
| | - Qichao Zhou
- Manteia Medical Technologies Co., Milwaukee, WI 53226, USA;
| | - X. Sharon Qi
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
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Ding J, Li Z, Lin Y, Huang C, Chen J, Hong J, Fei Z, Zhou Q, Chen C. Radiomics-clinical nomogram based on pretreatment 18F-FDG PET-CT radiomics features for individualized prediction of local failure in nasopharyngeal carcinoma. Sci Rep 2023; 13:18167. [PMID: 37875498 PMCID: PMC10598204 DOI: 10.1038/s41598-023-44933-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/13/2023] [Indexed: 10/26/2023] Open
Abstract
To explore the prognostic significance of PET/CT-based radiomics signatures and clinical features for local recurrence-free survival (LRFS) in nasopharyngeal carcinoma (NPC). We retrospectively reviewed 726 patients who underwent pretreatment PET/CT at our center. Least absolute shrinkage and selection operator (LASSO) regression and the Cox proportional hazards model were applied to construct Rad-score, which represented the radiomics features of PET-CT images. Univariate and multivariate analyses were used to establish a nomogram model. The concordance index (C-index) and calibration curve were used to evaluate the predictive accuracy and discriminative ability. Receiver operating characteristic analysis was performed to stratify the local recurrence risk of patients. The nomogram was validated by evaluating its discrimination ability and calibration in the validation cohort. A total of eight features were selected to construct Rad-score. A radiomics-clinical nomogram was built after the selection of univariate and multivariable Cox regression analyses, including the Rad-score and maximum standardized uptake value (SUVmax). The C-index was 0.71 (0.67-0.74) in the training cohort and 0.70 (0.64-0.76) in the validation cohort. The nomogram also performed far better than the 8th T-staging system with an area under the receiver operating characteristic curve (AUC) of 0.75 vs. 0.60 for 2 years and 0.71 vs. 0.60 for 3 years. The calibration curves show that the nomogram indicated accurate predictions. Decision curve analysis (DCA) revealed significantly better net benefits with this nomogram model. The log-rank test results revealed a distinct difference in prognosis between the two risk groups. The PET/CT-based radiomics nomogram showed good performance in predicting LRFS and showed potential to identify patients at high-risk of developing NPC.
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Affiliation(s)
- Jianming Ding
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Zirong Li
- Manteia Technologies Co., Ltd, 1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen, China
| | - Yuhao Lin
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Chaoxiong Huang
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Jiawei Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Jiabiao Hong
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Zhaodong Fei
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China.
| | - Qichao Zhou
- Manteia Technologies Co., Ltd, 1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen, China.
| | - Chuanben Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China.
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Liu X, Li ZR, Qi X, Zhou Q. Objective Boundary Generation for Gross Target Volume and Organs at Risk Using 3D Multi-Modal Medical Images. Int J Radiat Oncol Biol Phys 2023; 117:e476. [PMID: 37785510 DOI: 10.1016/j.ijrobp.2023.06.1689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Accurate delineation of Gross Target Volume (GTV) and Organs at Risk (OARs) in medical images is an essential but challenging step in radiotherapy. Deep-learning based automated delineation methods, which learn from manual annotations, are currently prevalent in academic research. However, the limited resolution of medical images and the fuzzy boundaries of lesions and organs present a challenge to the precision of manual annotations. By leveraging the complementary information from multi-modal medical images, this study proposed a novel method to generate objective boundaries of GTV and OARs. MATERIALS/METHODS We present a novel method of objective boundary generation, inspired by image matting primarily used for 2D RGB natural images, to process 3D grayscale medical images. The proposed method has the following advantages. 1) It allows for flexible input modalities and assigns weights to each modality according to their relative significance when computing information flows in the matting algorithm. 2) It computes 3D spatial information flow among voxels, which has more advantages over its 2D counterpart. 3) It has a closed-form solution that generates deterministic results. To evaluate the characteristics of the generated boundaries, patients with stage I nasopharyngeal carcinoma (NPC) were studied, with CT images and multi-modal MR images (T1, T1C, T2) aligned using deformable registration. Region of Interests (ROIs), i.e., GTV and parotid gland, were used, with a rough trimap marking extremely few foreground voxels, many background voxels, and a large unknown region. The proposed algorithm leverages the connection between each voxel and its nearest neighbors in the feature space, to propagate the opacity information. RESULTS We evaluated the results by employing both qualitative and quantitative methods. Using qualitative evaluation, experienced clinicians confirmed that the results were in agreement with the input data, especially for areas where borders were visible in most modalities (e.g., between air and tumor). For more challenging regions, where boundaries were unclear in the images, the results displayed fine-grained opacity transitions indicating the confidence of each voxel belonging to the ROI. When compared to the delineations made by clinicians, we found our results are usually more compact. We define a precision metric that evaluates the ratio of the matted foreground inside clinicians' delineations versus the entire matted foreground. Using a threshold of 0.4, our binarized result scored 0.95 for GTV and 0.92 for parotid gland. CONCLUSION The proposed method demonstrated satisfactory results on challenging ROIs. The objective boundaries generated by this method have advantages in many aspects, including improvement of delineation protocols, enhancement of manual annotation consistency, and increase of deep-learning based automated delineation accuracy.
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Affiliation(s)
- X Liu
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - Z R Li
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - X Qi
- Dept. of Radiation Oncology, UCLA, Los Angeles, CA
| | - Q Zhou
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
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Kong L, Li Z, Liu Y, Zhang J, Chen M, Zhou Q, Qi X, Deng XW, Peng Y. A Generalized Deep Learning Method for Synthetic CT Generation. Int J Radiat Oncol Biol Phys 2023; 117:e472. [PMID: 37785502 DOI: 10.1016/j.ijrobp.2023.06.1681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The application of deep learning to generate synthetic CT (sCT) has been widely studied in radiotherapy. Existing methods generally involve data from two different image modalities, such as CBCT-CT or MRI-CT, the quality of sCT is adversely affected by source image quality. We propose a unique method of synthesizing MRI and CBCT into sCT based on single-modal CT for training, and call it SmGAN. MATERIALS/METHODS We used planning CT of a group of 35 head and neck cases to as training data. We then applied two different spatial transformations to the planning CT image to produce the transformed CT1 and CT2. And We used a random style enhancement technique (Shuffle Remap) to modify the image distribution of CT1 which we termed CT1+E. CT1+E was used to simulate the patient's "image of the day" while CT2 to simulate the "planning image". After feeding both CT1+E and CT2 into the generator, we obtained the sCT predicted by the generator. The generator was trained using the Mean Absolute Error (MAE) loss between sCT and CT1. In the actual clinical process, we use the patient's CBCT or MRI instead of CT1+E and the patient's planning CT instead of CT2 as the input of the generator. After processing, we get an sCT that can maintain the spatial position of the image taken on the day, while presenting features similar to the planning CT. The evaluation data we have includes 10 pairs of MRI-Def_CT and 10 pairs of CBCT-Def_CT Head and Neck patients. Def_CT is obtained from the planning CT based on the spatial position deformation of MRI and CBCT. To evaluate the accuracy of sCT based on MRI and CBCT with Def CT, we use a range of metrics, including Hounsfield Unit (HU) difference, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and gamma pass rate. All results will be benchmarks against the advanced method RegGAN for comparison. RESULTS Compared to RegGAN, the results of SmGAN were significantly better. The mean absolute errors within the body were (44.7±216.2 HU vs. 36.7±131.4 HU) and (64.9±123.7 HU vs. 58.2±152.8 HU) for the CBCT-SCT and MRI-SCT, respectively (Table 1). In addition, experimental results show that SmGAN also outperforms RegGAN in dose calculation accuracy. For example, under the 10% threshold, SmGAN's gamma pass rate of 1mm and 1% is 0.926±0.02, compared with gamma rate of 0.896±0.02 for RegGAN. CONCLUSION We proposed a generalized deep learning model for synthetic CT generation, based on CBCT or MRI images. The proposed algorithm achieved high accuracy of dosimetric metrics, as well as excellent IMRT QA verification results. Compared to other existing synthetic CT generation methods, the proposed SmGAN required a single-modal image for training, which is considered as a major breakthrough in the industry, and is expected to have wide spread of clinical applications.
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Affiliation(s)
- L Kong
- Manteia Technologies Co., Ltd, Xiamen, 361001, People's Republic of China, Xiamen, Fujian, China
| | - Z Li
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Y Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - J Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - M Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Q Zhou
- Manteia Technologies Co., Ltd., Xiamen, China
| | - X Qi
- Dept. of Radiation Oncology, UCLA, Los Angeles, CA
| | - X W Deng
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Y Peng
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
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Liu Y, Chen Z, Zhou Q, Shang X, Zhao W, Zhang G, Xu S. A Feasibility Study of Dose Band Prediction in Radiotherapy: Predicting a Dose Spectrum. Int J Radiat Oncol Biol Phys 2023; 117:e691. [PMID: 37786031 DOI: 10.1016/j.ijrobp.2023.06.2164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Current deep learning-based dose prediction methods can only predict a specific dose distribution. If the predicted dose is inaccurate, no more options can be selected. We proposed a novel dose prediction method named dose band prediction, which outcomes a spectrum of predicted dose distribution for planning and quality assurance (QA). MATERIALS/METHODS Upper-Band and Lower-Band losses were involved in 3D convolution neural networks to establish the Upper-Band Network (UBN) and Lower-Band Network (LBN). Each voxel's ideal dose spectrum (dose band) was defined by the maximum/minimum rational dose predicted by UBN/LBN. 130 NPC cases with Tomotherapy (dataset 1), 49 cervix cases with IMRT (dataset 2) and 43 cervix cases with VMAT (dataset 3) were enrolled to establish and evaluate our dose band prediction method. RESULTS The dose band prediction method can successfully predict a spectrum of doses. Upper-Band/Lower-Band presents maximum/minimum rational dose; Middle-Line presents the average of Upper-Band and Lower-Band. The clinical implement dose was used as the reference dose. We evaluated the maximum interval between the reference and Upper-Band/Middle-Line/Lower-Band doses, and the percentage dose difference was used as the evaluation method. The differences in PTV for Upper-Band, Middle-Line and Lower-Band in dataset 1 were within 2.47%, 0.54%, and 2.8%; in dataset 2, they were within 0.37%, 1.15%, and 2.69%; in dataset 3, they were within 0.96%, 0.35%, and 1.66%. The mean difference of OARs for the Upper-Band, Middle-Line and Lower-Band in dataset 1 were within 8.13%, 4.97%, and 8.19%; in dataset 2, they were within 8.8%, 4.48%, and 5.52%; in dataset 3, they were within 4.01%, 3.13%, and 5.79% (shown in Table 1). CONCLUSION Dose Band prediction achieved high-accuracy dose prediction by the Middle-Line. More importantly, the Upper-Band/Lower-Band provided a spectrum of possible rational doses. Our Dose Band prediction method is based on a specific loss function, so it can easily be applied in various network and patient cases. Dose Band prediction towards a more robust plan QA and planning assistance. Table 1. The maximum interval of doses (percentage dose difference, %).
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Affiliation(s)
- Y Liu
- School of physics, Beijing University, Beijing, China; Department of Radiation Oncology, PLA General Hospital, Beijing, China
| | - Z Chen
- Manteia Technologies Co., Ltd, Xiamen, China
| | - Q Zhou
- Manteia Technologies Co., Ltd, Xiamen, China
| | - X Shang
- School of physics, Beijing University, Beijing, China; Department of Radiation Oncology, PLA General Hospital, Beijing, China
| | - W Zhao
- School of physics, Beijing University, Beijing, China
| | - G Zhang
- School of physics, Beijing University, Beijing, China
| | - S Xu
- National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Hebei, China; National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Li ZR, Weidhaas JB, Raldow A, Zhou Q, Qi X. Early Prediction of Radiation Treatment Response via Longitudinal Analysis of CBCT Radiomic Features for Locally Advanced Rectal Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e474-e475. [PMID: 37785506 DOI: 10.1016/j.ijrobp.2023.06.1686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Patients respond to the same radiation treatment course differently due to inter- and intra- patient variability in radiosensitivity. Despite widespread use of AI/ML in radiation oncology, there is a lack of monitoring strategies used during treatment courses to evaluate early predictors of treatment response in a systematic fashion. This work advances a straightforward, yet effective, method for the early detection of treatment response through systematically analyzing daily CBCT radiomic features. The goal is to aid clinicians in assessing the treatment efficacy routinely with a view towards optimizing personalized treatment. MATERIALS/METHODS We included a cohort of 30 patients diagnosed with locally advanced rectal cancer who underwent neo-adjuvant fractionated radiation treatment (RT) with a prescription dose of 50.4 Gy (28 fractions), followed by total mesorectal excision surgery after completion of ChemoRT. Daily IGRT imaging was acquired prior to each fraction resulting in a total of 840 CBCTs. Patients were divided into responder (14 patients) and non-responder (16 patients) groups based on post-RT pathological response. Mutual information algorithms were utilized to rigorously register daily CBCT images to the planning CT, and longitudinal radiomic features of the target were extracted from the daily CBCTs during the entire treatment course. All longitudinal features for a given patient were standardized with Z-Score normalization, followed by linear fitting using the least square method, resulting in radiomic feature trends (RFT) represented by slope values. Statistical significance was established via a two-sample U test and P-value with a threshold of 0.05. Logistic regression was performed to eliminate RFT with accuracy rates lower than 0.5. The final trending model was developed using random forest. For each patient at fraction N, our investigation involved independent 27 group experiments, where each experiment considered image group from fraction #1 to N, to confirm the effectiveness and stability of the model. RESULTS The proposed RFT demonstrated a high level of precision and consistency for post-RT response based on longitudinal CBCT images for LARC patients. The trending model yielded an accuracy of 0.9556, 95% CI (0.94, 0.972) when each daily image was considered, the prediction consistency was 0.964. Given the first 14 experiments (considering group images of fraction #1-15), the prediction accuracy was 0.9357, 95% CI (0.915, 0.956) and the prediction consistency was 0.952. CONCLUSION A strategy for monitoring and early prediction of LARC patients' radioresponse was evaluated via longitudinal CBCT assessment. Our trending models demonstrate a significant difference between the responder vs non-responder groups as early as the 15th fraction. Our strategy achieved superior accuracy and consistency to predict post-RT response of LARC patients.
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Affiliation(s)
- Z R Li
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - J B Weidhaas
- Department of Radiation Oncology, UCLA, Los Angeles, CA
| | - A Raldow
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - Q Zhou
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - X Qi
- Dept. of Radiation Oncology, UCLA, Los Angeles, CA
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Wang J, He Q, Li ZR, Huang N, Huang R, Wang JY, Zhou Q, Wang XH, Han F. The Lyman Normal Tissue Complication Probability Model and Risk Prediction for Temporal Lobe Injury after Re-Irradiation in Patients with Recurrent Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e587. [PMID: 37785777 DOI: 10.1016/j.ijrobp.2023.06.1932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The risk of temporal lobe injury (TLI) in recurrent nasopharyngeal carcinoma (rNPC) patients with intensity-modulated radiation therapy (IMRT) is high. We aimed to construct the normal tissue complication probability (NTCP) model for TLI of rNPC and establish a risk predictive model. MATERIALS/METHODS We retrospectively analyzed 103 patients with rNPC who had received two courses of IMRT in our institution. The 206 temporal lobes (TLs) of these patients were randomly divided into a training (n = 144) and validation group (n = 62). We determined the mean value of the following parameters to construct the Lyman NTCP model: TD50(1) (the dose with a 50% probability of complications to an organ when all volumes are irradiated), m [steepness of the dose-response at TD50(1)], and n (the parameter related to volume effect). The most predictive dosimetric parameter and clinical variables were integrated in Cox proportional hazards models. A nomogram was developed for predicting risk of TLs. RESULTS The parameters of the fitted NTCP model were TD50(1) = 107.84 Gy (95% confidence interval (CI), [97.15, 118.54]), m = 0.16 (95% CI, [0.14, 0.19]), and n = 0.04 (95% CI, [0.01, 0.06]). The cumulative dose delivered to 0.1 cm3 of temporal lobe volume (D0.1cc-c) was the most predictive dosimetric parameter for TLI. The Kaplan-Meier curves showed a significant difference in 2-year TLI-free survival among different risk groups according to the total score of nomograms. CONCLUSION The TD50(1) of TLI in patients with rNPC is 107.84 Gy in Lyman NTCP model. The nomogram model can accurately predict the risk of TLI for individual.
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Affiliation(s)
- J Wang
- Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Q He
- Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Z R Li
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - N Huang
- Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - R Huang
- Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - J Y Wang
- Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Q Zhou
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - X H Wang
- Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - F Han
- Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
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Du L, Lei Q, Zhou Q, Du Y, Lin X, Guo J, Li C, Luo Q, Fan C, Guo Q. Effect of MTA3 Inhibition of Glutamine Synthetase-Mediated Glutaminolysis on Radiosensitivity of Patients with Esophageal Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e227-e228. [PMID: 37784918 DOI: 10.1016/j.ijrobp.2023.06.1138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Metastasis-associated protein 3 (MTA3) can serve as a tumor suppressor in many cancer types. However, the role of MTA3 in radiosensitivity of patients with esophageal squamous cell cancer (ESCC) remains unclear. We thus investigated the function of MTA3 in radiosensitivity for ESCC, one of the most common digestive cancers. MATERIALS/METHODS The colony formation assay and nude mice xenograft tumor assay were performed to investigate the effect of MTA3 on radiosensitivity in ESCC. Glutamine consumption assay kit and glutamate production assay kit were used to assess the glutaminolysis. Glutaminase (GLS) Activity Assay Kit and Glutamine Synthetase (GS) Activity Assay Kit were used to analyze the activity of specific metabolic enzymes dominate glutaminolysis. The regulatory mechanism of glutaminolysis by MTA3 was confirmed using Chromatin immunoprecipitation assay and Gaussia luciferase assay. The expression levels of MTA3 and GS in ESCC primary tissues were evaluated using immunohistochemistry. Survival curves were plotted with the Kaplan-Meier method and compared by log-rank test. RESULTS The colony formation assay showed that MTA3 depletion and overexpression caused significantly higher and lower clonogenic survival after different doses of irradiation (IR), respectively. When these cells were subcutaneously injected into nude mice, the tumors derived from the cells with MTA3 overexpression and MTA3 knockdown were significantly smaller and bigger after IR, respectively. These findings suggest that MTA3 can enhance radiosensitivity in vitro and in vivo. Meanwhile, overexpressed and knockdown MTA3 can repress and expedite glutamine consumption and glutamate production uniformly, respectively. To determine how MTA3 acts on glutaminolysis, the activity of two specific metabolic enzymes dominate this metabolism, GS and GLS, were evaluated. It found that overexpressed and knockdown MTA3 can restrain and enhance the activity of GS, respectively, but have less effect on GLS. Moreover, the decreased radiosensitivity mediated by MTA3 knockdown is significantly increased when treated with GS inhibitor, suggesting that GS plays a crucial role in MTA3-mediated radiosensitivity enhancement. Mechanistically, Chromatin immunoprecipitation assay and Gaussia luciferase assay showed that MTA3 was recruited to the promoter of GS and suppressed GS transcription. However, knockdown of GATA3 abolished MTA3's repressive effect on GS and inhibited the MTA3's occupation on the promoter region of GS. These results collectively demonstrated that, in ESCC cells, MTA3 is recruited by GATA3 to inhibit GS expression, then ultimately represses glutaminolysis and enhances radiosensitivity. Finally, we showed that the ESCC patients in the MTA3low/GShigh group is significantly associated with shorter overall survival. CONCLUSION MTA3 is capable of enhancing radiosensitivity through downregulating GS and MTA3low/GShigh might be a potential prognostic factor for ESCC patients.
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Affiliation(s)
- L Du
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - Q Lei
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - Q Zhou
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - Y Du
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - X Lin
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - J Guo
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - C Li
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - Q Luo
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - C Fan
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - Q Guo
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
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Zhang W, Ma Y, Ibrahim G, Qi X, Zhou Q. Unsupervised Domain Adaptation of Auto-Segmentation on Multi-Source MRIs. Int J Radiat Oncol Biol Phys 2023; 117:e497. [PMID: 37785564 DOI: 10.1016/j.ijrobp.2023.06.1736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Deep learning has achieved great success in medical image segmentation. Most existing deep learning (DL) approaches make no adjustments to the model prior to inference. These models can perform well on the data of the same distribution, but their performance usually degrades when applied to the images from different source, i.e., different scanners. To tackle the problem caused by domain shift, we proposed an unsupervised domain adaptation (UDA) method based on entropy minimization and physical consistency constraints. MATERIALS/METHODS The proposed method combines feature-level and instance-level domain adaptation techniques to transfer knowledge from the source to the target domain. Specifically, the feature-level adaptation technique uses a graph-based entropy minimization to reduce the discrepancy between the source and target domains. The instance-level adaptation technique employs a novel consistency loss to regularize the physical consistency of the same object, such as volume, length, and centroid, thus improving the segmentation accuracy of the target domain. A collection of 93 abdominal MR images, comprising 45 cases from a 0.35T MRI scanner (TRUFI) and 48 cases from a 1.5T MRI scanner (T2), was utilized to evaluate the effectiveness of the proposed method. The contours of 6 organs-at-risk were delineated by a senior radiation oncologist, serving as the ground truth. Three models, the source model (SRC) trained on the source domain, the target model (TGT) trained on the target domain, and the UDA model adapted from the source domain to the target domain, were compared on the target domain using the Dice Similarity Coefficient (DSC). RESULTS In the experiment of 0.35T-to-1.5T, the proposed UDA method outperformed the source model, achieving an average DSC score of 0.82 ± 0.11, compared to 0.58 ± 0.23 (SRC) and 0.85 ± 0.09 (TGT), respectively. In the inverse experiment 1.5T-to-0.35T, the UDA model achieved an average DSC score of 0.79±0.13, compared to DSCs of 0.52 ± 0.25 and 0.81 ± 0.11 for the SRC and TGT respectively. The UDA method yielded a significant improvement of 46%, compared to the SRC. Particularly, OARs (organ at risk) with higher deformability such as the stomach and duodenum achieved a 58% and 63% improvement in performance, respectively. CONCLUSION This work presents a compelling approach of UDA for auto-segmentation on multi-source MRIs. Experimental results demonstrate that the UDA effectively improve the segmentation performance of the source model in the target domain, resulting in a more robust segmentation model.
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Affiliation(s)
- W Zhang
- Manteia Technologies Co., Ltd., Xiamen, China
| | - Y Ma
- Manteia Technologies Co., Ltd., Xiamen, China
| | - G Ibrahim
- Dept. of Radiation Oncology, UCLA, Los Angeles, CA
| | - X Qi
- Dept. of Radiation Oncology, UCLA, Los Angeles, CA
| | - Q Zhou
- Manteia Technologies Co., Ltd., Xiamen, China
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31
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Ablikim M, Achasov MN, Adlarson P, Ahmed S, Albrecht M, Aliberti R, Amoroso A, An Q, Bai Y, Bakina O, Ferroli RB, Balossino I, Ban Y, Begzsuren K, Berger N, Bertani M, Bettoni D, Bianchi F, Bloms J, Bortone A, Boyko I, Briere RA, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang WL, Chelkov G, Chen G, Chen HS, Chen ML, Chen SJ, Chen XR, Chen YB, Chen ZJ, Cheng WS, Cibinetto G, Cossio F, Dai HL, Dai JP, Dai XC, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du SX, Fang J, Fang SS, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Fritsch M, Fu CD, Gao YN, Gao Y, Gao Y, Garzia I, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Greco M, Gu LM, Gu MH, Gu S, Gu YT, Guan CY, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Han TT, Hao XQ, Harris FA, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Himmelreich M, Holtmann T, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang LQ, Huang XT, Huang YP, Hussain T, Imoehl W, Irshad M, Jaeger S, Janchiv S, Ji Q, Ji QP, Ji XB, Ji XL, Jiang XS, Jiao JB, Jiao Z, Jin S, Jin Y, Johansson T, Kalantar-Nayestanaki N, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Keshk IK, Khoukaz A, Kiese P, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuemmel M, Kuessner MK, Kupsc A, Kurth MG, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li JQ, Li JW, Li K, Li LK, Li L, Li PL, Li PR, Li SY, Li WD, Li WG, Li XH, Li XL, Li ZY, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Liu BJ, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JY, Liu K, Liu KY, Liu L, Liu MH, Liu Q, Liu SB, Liu S, Liu T, Liu WM, Liu X, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JD, Lu JG, Lu XL, Lu Y, Lu YP, Luo CL, Luo MX, Luo T, Luo XL, Lusso S, Lyu XR, Ma FC, Ma HL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XX, Ma XY, Maas FE, Maggiora M, Maldaner S, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Muramatsu H, Nakhoul S, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Pitka A, Poling R, Prasad V, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Qu SQ, Ravindran K, Redmer CF, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Sarantsev A, Schelhaas Y, Schnier C, Schoenning K, Scodeggio M, Shan DC, Shan W, Shan XY, Shao M, Shen CP, Shen PX, Shen XY, Shi HC, Shi RS, Shi X, Shi XD, Song WM, Song YX, Sosio S, Spataro S, Su KX, Sun GX, Sun JF, Sun L, Sun SS, Sun T, Sun WY, Sun YJ, Sun YK, Sun YZ, Sun ZT, Tan YH, Tan YX, Tang CJ, Tang GY, Tang J, Teng JX, Thoren V, Uman I, Wang B, Wang BL, Wang CW, Wang DY, Wang HP, Wang K, Wang LL, Wang M, Wang M, Wang WH, Wang WP, Wang X, Wang XF, Wang XL, Wang Y, Wang YD, Wang YF, Wang YQ, Wang Z, Wang ZY, Wang Z, Wang Z, Wei DH, Weidenkaff P, Weidner F, Wen SP, White DJ, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu JF, Wu LH, Wu LJ, Wu X, Wu Z, Xia L, Xiao H, Xiao SY, Xiao ZJ, Xie XH, Xie YG, Xie YH, Xing TY, Xu GF, Xu JJ, Xu QJ, Xu W, Xu XP, Xu YC, Yan F, Yan L, Yan WB, Yan WC, Yan X, Yang HJ, Yang HX, Yang L, Yang SL, Yang YH, Yang Y, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yuan CZ, Yuan L, Yuan W, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng Y, Zhang BX, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HY, Zhang JJ, Zhang JQ, Zhang JW, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang L, Zhang SF, Zhang XD, Zhang XY, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao Q, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng YH, Zhong B, Zhong C, Zhou LP, Zhou Q, Zhou X, Zhou XK, Zhou XR, Zhu AN, Zhu J, Zhu K, Zhu KJ, Zhu SH, Zhu WJ, Zhu WJ, Zhu YC, Zhu ZA, Zou BS, Zou JH. Search for Λ[over ¯]-Λ Baryon-Number-Violating Oscillations in the Decay J/ψ→pK^{-}Λ[over ¯]+c.c. Phys Rev Lett 2023; 131:121801. [PMID: 37802947 DOI: 10.1103/physrevlett.131.121801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/14/2023] [Accepted: 08/29/2023] [Indexed: 10/08/2023]
Abstract
We report on the first search for Λ[over ¯]-Λ oscillations in the decay J/ψ→pK^{-}Λ[over ¯]+c.c. by analyzing 1.31×10^{9} J/ψ events accumulated with the BESIII detector at the BEPCII collider. The J/ψ events are produced using e^{+}e^{-} collisions at a center of mass energy sqrt[s]=3.097 GeV. No evidence for hyperon oscillations is observed. The upper limit for the oscillation rate of Λ[over ¯] to Λ hyperons is determined to be P(Λ)=[B(J/ψ→pK^{-}Λ+c.c.)/B(J/ψ→pK^{-}Λ[over ¯]+c.c.)]<4.4×10^{-6} corresponding to an oscillation parameter δm_{ΛΛ[over ¯]} of less than 3.8×10^{-18} GeV at the 90% confidence level.
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Affiliation(s)
- M Ablikim
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - M N Achasov
- Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
| | - P Adlarson
- Uppsala University, Box 516, SE-75120 Uppsala, Sweden
| | - S Ahmed
- Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
| | - M Albrecht
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - R Aliberti
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - A Amoroso
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - Q An
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Y Bai
- Southeast University, Nanjing 211100, People's Republic of China
| | - O Bakina
- Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
| | - R Baldini Ferroli
- INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044, Frascati, Italy
| | - I Balossino
- INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122, Ferrara, Italy
| | - Y Ban
- Peking University, Beijing 100871, People's Republic of China
| | - K Begzsuren
- Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
| | - N Berger
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - M Bertani
- INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044, Frascati, Italy
| | - D Bettoni
- INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122, Ferrara, Italy
| | - F Bianchi
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - J Bloms
- University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
| | - A Bortone
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - I Boyko
- Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
| | - R A Briere
- Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - H Cai
- Wuhan University, Wuhan 430072, People's Republic of China
| | - X Cai
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - A Calcaterra
- INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044, Frascati, Italy
| | - G F Cao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - N Cao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - S A Cetin
- Turkish Accelerator Center Particle Factory Group, Istinye University, 34010, Istanbul, Turkey
| | - J F Chang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - W L Chang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - G Chelkov
- Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
| | - G Chen
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - H S Chen
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - M L Chen
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - S J Chen
- Nanjing University, Nanjing 210093, People's Republic of China
| | - X R Chen
- Institute of Modern Physics, Lanzhou 730000, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Y B Chen
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - Z J Chen
- Hunan University, Changsha 410082, People's Republic of China
| | | | - G Cibinetto
- INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122, Ferrara, Italy
| | | | - H L Dai
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - J P Dai
- Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - X C Dai
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - A Dbeyssi
- Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
| | - R E de Boer
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - D Dedovich
- Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
| | - Z Y Deng
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - A Denig
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - I Denysenko
- Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
| | - M Destefanis
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - F De Mori
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - Y Ding
- Liaoning University, Shenyang 110036, People's Republic of China
| | - J Dong
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - L Y Dong
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - M Y Dong
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - X Dong
- Wuhan University, Wuhan 430072, People's Republic of China
| | - S X Du
- Zhengzhou University, Zhengzhou 450001, People's Republic of China
| | - J Fang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - S S Fang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Y Fang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - R Farinelli
- INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122, Ferrara, Italy
| | - L Fava
- University of Eastern Piedmont, I-15121, Alessandria, Italy
- INFN, I-10125, Turin, Italy
| | - F Feldbauer
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - G Felici
- INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044, Frascati, Italy
| | - C Q Feng
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - M Fritsch
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - C D Fu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Y N Gao
- Peking University, Beijing 100871, People's Republic of China
| | - Ya Gao
- University of South China, Hengyang 421001, People's Republic of China
| | - Yang Gao
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - I Garzia
- INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122, Ferrara, Italy
- University of Ferrara, I-44122, Ferrara, Italy
| | - E M Gersabeck
- University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
| | - A Gilman
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - K Goetzen
- GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
| | - L Gong
- Liaoning University, Shenyang 110036, People's Republic of China
| | - W X Gong
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - W Gradl
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - M Greco
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - L M Gu
- Nanjing University, Nanjing 210093, People's Republic of China
| | - M H Gu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - S Gu
- Beihang University, Beijing 100191, People's Republic of China
| | - Y T Gu
- Guangxi University, Nanning 530004, People's Republic of China
| | - C Y Guan
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - A Q Guo
- Indiana University, Bloomington, Indiana 47405, USA
| | - L B Guo
- Nanjing Normal University, Nanjing 210023, People's Republic of China
| | - R P Guo
- Shandong Normal University, Jinan 250014, People's Republic of China
| | - Y P Guo
- Fudan University, Shanghai 200433, People's Republic of China
| | - A Guskov
- Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
| | - T T Han
- Shandong University, Jinan 250100, People's Republic of China
| | - X Q Hao
- Henan Normal University, Xinxiang 453007, People's Republic of China
| | - F A Harris
- University of Hawaii, Honolulu, Hawaii 96822, USA
| | - K L He
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | | | - C H Heinz
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - Y K Heng
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - C Herold
- Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
| | - M Himmelreich
- GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
| | - T Holtmann
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - Y R Hou
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Z L Hou
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - H M Hu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - J F Hu
- South China Normal University, Guangzhou 510006, People's Republic of China
| | - T Hu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Y Hu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - G S Huang
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - L Q Huang
- University of South China, Hengyang 421001, People's Republic of China
| | - X T Huang
- Shandong University, Jinan 250100, People's Republic of China
| | - Y P Huang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - T Hussain
- University of the Punjab, Lahore-54590, Pakistan
| | - W Imoehl
- Indiana University, Bloomington, Indiana 47405, USA
| | - M Irshad
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - S Jaeger
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - S Janchiv
- Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
| | - Q Ji
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Q P Ji
- Henan Normal University, Xinxiang 453007, People's Republic of China
| | - X B Ji
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - X L Ji
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - X S Jiang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - J B Jiao
- Shandong University, Jinan 250100, People's Republic of China
| | - Z Jiao
- Huangshan College, Huangshan 245000, People's Republic of China
| | - S Jin
- Nanjing University, Nanjing 210093, People's Republic of China
| | - Y Jin
- University of Jinan, Jinan 250022, People's Republic of China
| | - T Johansson
- Uppsala University, Box 516, SE-75120 Uppsala, Sweden
| | | | - X S Kang
- Liaoning University, Shenyang 110036, People's Republic of China
| | - R Kappert
- University of Groningen, NL-9747 AA Groningen, The Netherlands
| | - M Kavatsyuk
- University of Groningen, NL-9747 AA Groningen, The Netherlands
| | - B C Ke
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- Shanxi Normal University, Linfen 041004, People's Republic of China
| | - I K Keshk
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - A Khoukaz
- University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
| | - P Kiese
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - R Kiuchi
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - R Kliemt
- GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
| | - O B Kolcu
- Turkish Accelerator Center Particle Factory Group, Istinye University, 34010, Istanbul, Turkey
| | - B Kopf
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - M Kuemmel
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | | | - A Kupsc
- Uppsala University, Box 516, SE-75120 Uppsala, Sweden
| | - M G Kurth
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - W Kühn
- Justus-Liebig-Universitaet Giessen, II. Physikalisches Institut, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany
| | - J J Lane
- University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
| | - P Larin
- Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
| | - A Lavania
- Indian Institute of Technology Madras, Chennai 600036, India
| | - L Lavezzi
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - Z H Lei
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - H Leithoff
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - M Lellmann
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - T Lenz
- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
| | - C Li
- Qufu Normal University, Qufu 273165, People's Republic of China
| | - C H Li
- Liaoning Normal University, Dalian 116029, People's Republic of China
| | - Cheng Li
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - D M Li
- Zhengzhou University, Zhengzhou 450001, People's Republic of China
| | - F Li
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - G Li
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - H Li
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - H B Li
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - H J Li
- Fudan University, Shanghai 200433, People's Republic of China
| | - J Q Li
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - J W Li
- Shandong University, Jinan 250100, People's Republic of China
| | - Ke Li
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - L K Li
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Lei Li
- Beijing Institute of Petrochemical Technology, Beijing 102617, People's Republic of China
| | - P L Li
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - P R Li
- Lanzhou University, Lanzhou 730000, People's Republic of China
| | - S Y Li
- Tsinghua University, Beijing 100084, People's Republic of China
| | - W D Li
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - W G Li
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - X H Li
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - X L Li
- Shandong University, Jinan 250100, People's Republic of China
| | - Z Y Li
- Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - H Liang
- Jilin University, Changchun 130012, People's Republic of China
| | - H Liang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - H Liang
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Y F Liang
- Sichuan University, Chengdu 610064, People's Republic of China
| | - Y T Liang
- Institute of Modern Physics, Lanzhou 730000, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - G R Liao
- Guangxi Normal University, Guilin 541004, People's Republic of China
| | - L Z Liao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - J Libby
- Indian Institute of Technology Madras, Chennai 600036, India
| | - A Limphirat
- Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
| | - B J Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - C X Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - D Liu
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - F H Liu
- Shanxi University, Taiyuan 030006, People's Republic of China
| | - Fang Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Feng Liu
- Central China Normal University, Wuhan 430079, People's Republic of China
| | - H B Liu
- Guangxi University, Nanning 530004, People's Republic of China
| | - H M Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Huanhuan Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Huihui Liu
- Henan University of Science and Technology, Luoyang 471003, People's Republic of China
| | - J B Liu
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - J Y Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - K Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - K Y Liu
- Liaoning University, Shenyang 110036, People's Republic of China
| | - L Liu
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - M H Liu
- Fudan University, Shanghai 200433, People's Republic of China
| | - Q Liu
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - S B Liu
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Shuai Liu
- Soochow University, Suzhou 215006, People's Republic of China
| | - T Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - W M Liu
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - X Liu
- Lanzhou University, Lanzhou 730000, People's Republic of China
| | - Y B Liu
- Nankai University, Tianjin 300071, People's Republic of China
| | - Z A Liu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Z Q Liu
- Shandong University, Jinan 250100, People's Republic of China
| | - X C Lou
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - F X Lu
- Henan Normal University, Xinxiang 453007, People's Republic of China
| | - H J Lu
- Huangshan College, Huangshan 245000, People's Republic of China
| | - J D Lu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - J G Lu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - X L Lu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Y Lu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Y P Lu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - C L Luo
- Nanjing Normal University, Nanjing 210023, People's Republic of China
| | - M X Luo
- Zhejiang University, Hangzhou 310027, People's Republic of China
| | - T Luo
- Fudan University, Shanghai 200433, People's Republic of China
| | - X L Luo
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | | | - X R Lyu
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - F C Ma
- Liaoning University, Shenyang 110036, People's Republic of China
| | - H L Ma
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - L L Ma
- Shandong University, Jinan 250100, People's Republic of China
| | - M M Ma
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Q M Ma
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - R Q Ma
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - R T Ma
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - X X Ma
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - X Y Ma
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - F E Maas
- Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
| | - M Maggiora
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - S Maldaner
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - S Malde
- University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
| | - Q A Malik
- University of the Punjab, Lahore-54590, Pakistan
| | - A Mangoni
- INFN Sezione di Perugia, I-06100, Perugia, Italy
| | - Y J Mao
- Peking University, Beijing 100871, People's Republic of China
| | - Z P Mao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - S Marcello
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
- INFN, I-10125, Turin, Italy
| | - Z X Meng
- University of Jinan, Jinan 250022, People's Republic of China
| | - J G Messchendorp
- GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
- University of Groningen, NL-9747 AA Groningen, The Netherlands
| | - G Mezzadri
- INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122, Ferrara, Italy
| | - T J Min
- Nanjing University, Nanjing 210093, People's Republic of China
| | - R E Mitchell
- Indiana University, Bloomington, Indiana 47405, USA
| | - X H Mo
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - N Yu Muchnoi
- Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
| | - H Muramatsu
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - S Nakhoul
- GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
| | - Y Nefedov
- Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
| | - F Nerling
- GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
| | - I B Nikolaev
- Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
| | - Z Ning
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - S Nisar
- COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, 54000 Lahore, Pakistan
| | - S L Olsen
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Q Ouyang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - S Pacetti
- INFN Sezione di Perugia, I-06100, Perugia, Italy
- University of Perugia, I-06100, Perugia, Italy
| | - X Pan
- Fudan University, Shanghai 200433, People's Republic of China
| | - Y Pan
- University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
| | - A Pathak
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - P Patteri
- INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044, Frascati, Italy
| | - M Pelizaeus
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - H P Peng
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - K Peters
- GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
| | - J L Ping
- Nanjing Normal University, Nanjing 210023, People's Republic of China
| | - R G Ping
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - A Pitka
- Bochum Ruhr-University, D-44780 Bochum, Germany
| | - R Poling
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - V Prasad
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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| | - H Qi
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
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- Tsinghua University, Beijing 100084, People's Republic of China
| | - M Qi
- Nanjing University, Nanjing 210093, People's Republic of China
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- Beihang University, Beijing 100191, People's Republic of China
| | - S Qian
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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- Guangxi Normal University, Guilin 541004, People's Republic of China
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- Fudan University, Shanghai 200433, People's Republic of China
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- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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- Nankai University, Tianjin 300071, People's Republic of China
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- Tsinghua University, Beijing 100084, People's Republic of China
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- Indian Institute of Technology Madras, Chennai 600036, India
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- Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
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| | - C Schnier
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| | - K Schoenning
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| | - X Y Shan
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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| | - M Shao
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| | - C P Shen
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| | - P X Shen
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| | - X Y Shen
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| | - H C Shi
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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| | - R S Shi
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| | - Y X Song
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| | - S Sosio
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| | - S Spataro
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| | - K X Su
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| | - G X Sun
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| | - J F Sun
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| | - L Sun
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| | - S S Sun
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| | - T Sun
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- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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| | - J X Teng
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| | - V Thoren
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| | - B Wang
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| | - C W Wang
- Nanjing University, Nanjing 210093, People's Republic of China
| | - D Y Wang
- Peking University, Beijing 100871, People's Republic of China
| | - H P Wang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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| | - K Wang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - L L Wang
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| | - M Wang
- Shandong University, Jinan 250100, People's Republic of China
| | - Meng Wang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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| | - W H Wang
- Wuhan University, Wuhan 430072, People's Republic of China
| | - W P Wang
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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| | - X Wang
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| | - X F Wang
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| | - Y Wang
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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| | - Y D Wang
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| | - Y F Wang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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| | - Y Q Wang
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| | - Ziyi Wang
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| | - D H Wei
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- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - L J Wu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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| | - X Wu
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| | - Z Wu
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| | - L Xia
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
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| | - S Y Xiao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Z J Xiao
- Nanjing Normal University, Nanjing 210023, People's Republic of China
| | - X H Xie
- Peking University, Beijing 100871, People's Republic of China
| | - Y G Xie
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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| | - Y H Xie
- Central China Normal University, Wuhan 430079, People's Republic of China
| | - T Y Xing
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| | - G F Xu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - J J Xu
- Nanjing University, Nanjing 210093, People's Republic of China
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| | - W Xu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - X P Xu
- Soochow University, Suzhou 215006, People's Republic of China
| | - Y C Xu
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - F Yan
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| | - L Yan
- Fudan University, Shanghai 200433, People's Republic of China
| | - W B Yan
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
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- Soochow University, Suzhou 215006, People's Republic of China
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- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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- Shanxi Normal University, Linfen 041004, People's Republic of China
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- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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- Nanjing University, Nanjing 210093, People's Republic of China
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- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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| | - M Ye
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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| | - M H Ye
- China Center of Advanced Science and Technology, Beijing 100190, People's Republic of China
| | - J H Yin
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Z Y You
- Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - B X Yu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - C X Yu
- Nankai University, Tianjin 300071, People's Republic of China
| | - G Yu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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- Hunan University, Changsha 410082, People's Republic of China
| | - T Yu
- University of South China, Hengyang 421001, People's Republic of China
| | - C Z Yuan
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - L Yuan
- Beihang University, Beijing 100191, People's Republic of China
| | - W Yuan
- University of Turin and INFN, University of Turin, I-10125, Turin, Italy
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| | - Y Yuan
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- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Z Y Yuan
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| | - C X Yue
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| | - A A Zafar
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| | - Y Zeng
- Hunan University, Changsha 410082, People's Republic of China
| | - B X Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - G Y Zhang
- Henan Normal University, Xinxiang 453007, People's Republic of China
| | - H Zhang
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - H H Zhang
- Jilin University, Changchun 130012, People's Republic of China
| | - H H Zhang
- Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - H Y Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - J J Zhang
- Shanxi Normal University, Linfen 041004, People's Republic of China
| | - J Q Zhang
- Nanjing Normal University, Nanjing 210023, People's Republic of China
| | - J W Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - J Y Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - J Z Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Jianyu Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Jiawei Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Lei Zhang
- Nanjing University, Nanjing 210093, People's Republic of China
| | - S F Zhang
- Nanjing University, Nanjing 210093, People's Republic of China
| | - X D Zhang
- North China Electric Power University, Beijing 102206, People's Republic of China
| | - X Y Zhang
- Shandong University, Jinan 250100, People's Republic of China
| | - Y Zhang
- University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
| | - Y T Zhang
- Zhengzhou University, Zhengzhou 450001, People's Republic of China
| | - Y H Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - Yan Zhang
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Yao Zhang
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - Z Y Zhang
- Wuhan University, Wuhan 430072, People's Republic of China
| | - G Zhao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - J Zhao
- Liaoning Normal University, Dalian 116029, People's Republic of China
| | - J Y Zhao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - J Z Zhao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - Lei Zhao
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Ling Zhao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - M G Zhao
- Nankai University, Tianjin 300071, People's Republic of China
| | - Q Zhao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - S J Zhao
- Zhengzhou University, Zhengzhou 450001, People's Republic of China
| | - Y B Zhao
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - Y X Zhao
- Institute of Modern Physics, Lanzhou 730000, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Z G Zhao
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - A Zhemchugov
- Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
| | - B Zheng
- University of South China, Hengyang 421001, People's Republic of China
| | - J P Zheng
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
| | - Y H Zheng
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - B Zhong
- Nanjing Normal University, Nanjing 210023, People's Republic of China
| | - C Zhong
- University of South China, Hengyang 421001, People's Republic of China
| | - L P Zhou
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Q Zhou
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - X Zhou
- Wuhan University, Wuhan 430072, People's Republic of China
| | - X K Zhou
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - X R Zhou
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - A N Zhu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - J Zhu
- Nankai University, Tianjin 300071, People's Republic of China
| | - K Zhu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - K J Zhu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - S H Zhu
- University of Science and Technology Liaoning, Anshan 114051, People's Republic of China
| | - W J Zhu
- Fudan University, Shanghai 200433, People's Republic of China
| | - W J Zhu
- Nankai University, Tianjin 300071, People's Republic of China
| | - Y C Zhu
- State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Z A Zhu
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - B S Zou
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
| | - J H Zou
- Institute of High Energy Physics, Beijing 100049, People's Republic of China
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Gao Y, Zhao YJ, Li Y, Song JN, Zhang XZ, Sun Y, Yu M, Zhou Q. [The predictive value of melanin-concentrating hormone combined with other related biomarkers in cerebrospinal fluid in preoperative cognitive dysfunction of elderly patients]. Zhonghua Yi Xue Za Zhi 2023; 103:2772-2777. [PMID: 37723051 DOI: 10.3760/cma.j.cn112137-20230119-00112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/20/2023]
Abstract
Objective: To explore the predictive value of cerebrospinal fluid melanin-concentrating hormone (MCH) combined with other related biomarkers in preoperative cognitive dysfunction of elderly patients. Methods: A total of 80 patients who underwent elective hip or knee replacement under intravertebral anesthesia in Chifeng Municipal Hospital, Inner Mongolia, from March to November 2022 were prospectively included, with 32 males and 48 females, and aged 65-85 (70.7±5.2) years old. According to the evaluation results of the Montreal Cognitive Assessment (MoCA), patients were divided into the preoperative cognitive dysfunction (n=23) and control (n=57) groups. The levels of MCH, amyloid-β 40 (Aβ40), amyloid-β 42 (Aβ42), and phosphorylated tau protein (p-tau) in cerebrospinal fluid were determined by enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of each biomarker separately or in combination for preoperative cognitive dysfunction. Spearman's rank correlation analysis was utilized to test the correlation between the level of each biomarker and MoCA scores. Results: The levels of MCH, Aβ40, Aβ42, p-tau, and Aβ42/p-tau in the preoperative cognitive dysfunction group were (35.53±5.94) μg/L, (39.21±9.18) ng/L, (221.83±43.17) ng/L, (42.64±9.74) ng/L, and 5.53±1.92, and the levels of these biomarkers in the control group were (28.74±4.90) μg/L, (36.37±7.87) ng/L, (280.23±45.67) ng/L, (35.00±9.27) ng/L, and 8.62±2.78, respectively. Compared with the control group, the levels of cerebrospinal fluid MCH and p-tau in the preoperative cognitive dysfunction group were significantly increased (all P<0.01), and the levels of Aβ42 and Aβ42/p-tau were significantly decreased (all P<0.001). MCH and Aβ42/p-tau provided higher predictive values. The area under the curve (AUC) of MCH and Aβ42/p-tau were 0.807 (95%CI: 0.703-0.911) and 0.842 (95%CI: 0.741-0.943), the sensitivity were 78.3% and 87.0%, and the specificity were 75.4% and 94.7%. MCH combined with Aβ42/p-tau have the higher AUC of 0.915 (95%CI: 0.837-0.992), the sensitivity (87.0%) and specificity (86.0%) were both high, which had a higher predictive value. The levels of cerebrospinal fluid MCH and p-tau were negatively correlated with MoCA score (r=-0.467, -0.321, all P<0.01), and the levels of Aβ42 and Aβ42/p-tau were positively correlated with MoCA score (r=0.480, 0.520, all P<0.001). Conclusion: The increase in cerebrospinal fluid MCH levels is associated with preoperative cognitive dysfunction in elderly patients. MCH combined with Aβ42/p-tau has the greatest predictive value.
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Affiliation(s)
- Y Gao
- Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng 024000, China
| | - Y J Zhao
- Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng 024000, China
| | - Y Li
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin Research Institute of Anesthesiology, Tianjin 300052, China
| | - J N Song
- Department of Anesthesiology, Chifeng Municipal Hospital of Inner Mongolia, Chifeng 024000, China
| | - X Z Zhang
- Department of Anesthesiology, Chifeng Municipal Hospital of Inner Mongolia, Chifeng 024000, China
| | - Y Sun
- Department of Anesthesiology, Chifeng Municipal Hospital of Inner Mongolia, Chifeng 024000, China
| | - M Yu
- Department of Anesthesiology, Chifeng Municipal Hospital of Inner Mongolia, Chifeng 024000, China
| | - Q Zhou
- Department of Anesthesiology, Chifeng Municipal Hospital of Inner Mongolia, Chifeng 024000, China
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Jiang AF, Zhou SS, Zhou Q, Zhao J, Li XP, Zhou RR, Li B. [Clinical characteristics and their influences on the survival of leptomeningeal metastasis derived from lung adenocarcinoma harboring epithelial growth factor receptor mutation]. Zhonghua Yi Xue Za Zhi 2023; 103:2713-2719. [PMID: 37675543 DOI: 10.3760/cma.j.cn112137-20221221-02686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Objective: To analyze the clinical characteristics of leptomeningeal metastasis (LM) patients from epithelial growth factor receptor (EGFR)-mutated lung adenocarcinoma, and their impacts on the survival of the patients. Methods: From July 2018 to July 2022, the clinicopathological data of 81 patients diagnosed as EGFR-mutated lung adenocarcinoma LM by cytopathology who admitted to the Department of Oncology of Xiangya Hospital of Central South University were retrospectively analyzed, including 33 males and 48 females. The age ranged from 31 to 76 years, with a median age of 54 years. All the 81 patients were followed up, with a median follow-up of 21.0 months (95%CI: 12.5 to 29.5 months). The Kaplan Meier method was used to draw survival curve. Cox proportional hazards regression model was used to analyze the impact of the factors on the survival of patients. Results: Among the 81 patients, the interval between the initial diagnosis of lung cancer and the pathological diagnosis of LM in cerebrospinal fluid (CSF) was 0-108 months, with a median interval of 14 months. Fifty-two patients (64.2%) used the third-generation epithelial growth factor receptor tyrosine kinase inhibitor (EGFR-TKIs), while 17 patients (21.0%) used EGFR-TKIs in combination with other drugs, and 12 patients (14.8%) were treated with best supportive care (BSC). Sixty patients (74.1%) had a Kanofsky performance status (KPS) score of less than 60 points, and 71 patients (87.7%) had brain parenchymal metastasis and/or spinal metastasis. Twenty-two patients (27.2%) used pemetrexed through intrathecal CSF, and 17 patients (21.0%) used pemetrexed through the Ommaya sac to the CSF of the ventricle. The incidence of adverse event related to the administration of pemetrexed through CSF was 64.1% (25/39), mainly manifested as myelosuppression, including 22 patients of leukocyte reduction, 25 patients of hemoglobin reduction, and 14 patients of platelet reduction. The median post-leptomeningeal metastasis overall survival (pLM-OS) in 81 patients was 11.0 (95%CI: 7.7-14.3) months. KPS score≥60 points (HR=0.407, 95%CI: 0.170-0.973, P=0.043), CSF cytology negative after treatment (vs persistent positive, HR=0.351, 95%CI: 0.155-0.792, P=0.012), intraventricular administration of pemetrexed (vs non intraventricular administration of pemetrexed, HR=0.319, 95%CI: 0.137-0.745, P=0.008) and the treatment with third-generation EGFR-TKIs after LM (vs EGFR-TKIs in combination with other drugs, HR=0.486, 95%CI: 0.237-0.998, P=0.049) were a factor affecting pLM-OS of patients. Conclusions: Brain parenchyma, or/and spine are the most sites where the LM patients concurrently metastasize. KPS score≥60 points and CSF cytology negative after treatment, intraventricular administration of pemetrexed and the treatment with third-generation EGFR-TKIs are indictors affecting pLM-OS of the patients.
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Affiliation(s)
- A F Jiang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - S S Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Q Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - J Zhao
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha 410008, China
| | - X P Li
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
| | - R R Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - B Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
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Li Y, Tao J, Zhang Y, Shi K, Chang J, Pan M, Song L, Jeppesen E, Zhou Q. Urbanization shifts long-term phenology and severity of phytoplankton blooms in an urban lake through different pathways. Glob Chang Biol 2023; 29:4983-4999. [PMID: 37353861 DOI: 10.1111/gcb.16828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/14/2023] [Accepted: 05/31/2023] [Indexed: 06/25/2023]
Abstract
Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi-source remote sensing data and integrated the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long-term (2000-2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011-2021 to 2000-2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km2 ) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change.
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Affiliation(s)
- Yuanrui Li
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming, China
| | - Juan Tao
- Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Institute of International Rivers and Eco-Security, Yunnan University, Kunming, China
| | - Yunlin Zhang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
| | - Kun Shi
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
| | - Junjun Chang
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming, China
| | - Min Pan
- Dianchi Lake Ecosystem Observation and Research Station of Yunnan Province, Kunming Dianchi and Plateau Lakes Institute, Kunming, China
| | - Lirong Song
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Erik Jeppesen
- Department of Ecoscience, Aarhus University, Aarhus, Denmark
- Sino-Danish Centre for Education and Research, Beijing, China
- Limnology Laboratory, Department of Biological Sciences and Centre for Ecosystem Research and Implementation, Middle East Technical University, Ankara, Turkey
- Institute of Marine Sciences, Middle East Technical University, Mersin, Turkey
| | - Qichao Zhou
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming, China
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Zhao L, Zhu R, Zhou Q, Jeppesen E, Yang K. Trophic status and lake depth play important roles in determining the nutrient-chlorophyll a relationship: Evidence from thousands of lakes globally. Water Res 2023; 242:120182. [PMID: 37311404 DOI: 10.1016/j.watres.2023.120182] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/20/2023] [Accepted: 06/06/2023] [Indexed: 06/15/2023]
Abstract
A fundamental problem in lake eutrophication management is that the nutrient-chlorophyll a (Chl a) relationship shows high variability due to diverse influences of for example lake depth, lake trophic status, and latitude. To accommodate the variability induced by spatial heterogeneity, a reliable and general insight into the nutrient-Chl a relationship may be achieved by applying probabilistic methods to analyze data compiled across a broad spatial scale. Here, the roles of two critical factors determining the nutrient-Chl a relationship, lake depth and trophic status, were explored by applying Bayesian networks (BNs) and a Bayesian hierarchical linear regression model (BHM) to a compiled global dataset from 2849 lakes and 25083 observations. We categorized the lakes into three groups (shallow, transitional, and deep) according to mean and maximum depth relative to mixing depth. We found that despite a stronger effect of total phosphorus (TP) and total nitrogen (TN) on Chl a when combined, TP played a dominant role in determining Chl a, regardless of lake depth. However, when the lake was hypereutrophic and/or TP was >40 μg/L, TN had a greater impact on Chl a, especially in shallow lakes. The response curve of Chl a to TP and TN varied with lake depth, with deep lakes having the lowest yield Chl a per unit of nutrient, followed by transitional lakes, while shallow lakes had the highest ratio. Moreover, we found a decrease of TN/TP with increasing Chl a concentrations and lake depth (represented as mixing depth/mean depth). Our established BHM may help estimating lake type and/or lake-specific acceptable TN and TP concentrations that comply with target Chl a concentrations with higher certainty than can be obtained when bulking all lake types.
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Affiliation(s)
- Lei Zhao
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China; GIS Technology Engineering Research Centre for West-China Resources and Environment, Ministry Education, Yunnan Normal University, Kunming 650500, China.
| | - Rao Zhu
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China
| | - Qichao Zhou
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China
| | - Erik Jeppesen
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China; Department of Ecoscience, Aarhus University, Aarhus 8000C, Denmark; Sino-Danish Centre for Education and Research, Beijing 100049, China; Department of Biological Sciences and Centre for Ecosystem Research and Implementation, Limnology Laboratory, Middle East Technical University, Ankara 06800, Turkey; Institute of Marine Sciences, Middle East Technical University, Mersin, Turkey
| | - Kun Yang
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China; GIS Technology Engineering Research Centre for West-China Resources and Environment, Ministry Education, Yunnan Normal University, Kunming 650500, China.
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36
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Wu SY, Lan H, Liu YL, Sun YJ, Ren MJ, Wang P, Chen ZJ, Zhou Q, Ke X, Li GB, Guo QQ, Chen YL, Lu SH. [Definition of severe pulmonary tuberculosis: a scoping review]. Zhonghua Jie He He Hu Xi Za Zhi 2023; 46:760-773. [PMID: 37536986 DOI: 10.3760/cma.j.cn112147-20230517-00247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Objective: To clarify the definition of severe pulmonary tuberculosis and its inclusion criteria by summarizing and analyzing the studies of severe pulmonary tuberculosis (TB). Methods: A systematic search of Medline (via PubMed), Cochrane Library, Web of Science, Web of Science, Epistemonikos, Embase, CNKI, WanFang database, and CBM database was conducted to collect studies published between 2017 and 2022 on patients with severe pulmonary TB. Searches were performed using a combination of subject terms and free words. The search terms included: tuberculosis, severe, serious, intensive care, critical care, respiratory failure, mechanical ventilation, hospitalization, respiratory distress syndrome, multiple organ failure, pulmonary heart disease, and pneumothorax. The definitions and inclusion criteria for severe pulmonary TB in the included studies were extracted. Results: A total of 19 981 studies were identified and 100 studies were finally included, involving 8 309 patients with severe pulmonary TB. A total of 8 (8.00%) studies explicitly mentioned the definition of severe pulmonary TB, and 53 (53.00%) studies clearly defined the inclusion criteria for patients with severe pulmonary TB. A total of 5 definitions and 30 inclusion criteria were extracted. A total of 132 dichotomous variables and 113 continuous variables were included in the outcome indicators related to patients with severe pulmonary TB of concern in the studies. Conclusions: The definition and diagnostic criteria for severe TB are unclear, and there is an urgent need to develop a clear definition and diagnostic criteria to guide clinical practice.
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Affiliation(s)
- S Y Wu
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - H Lan
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - Y L Liu
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - Y J Sun
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - M J Ren
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - P Wang
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou 730000, China
| | - Z J Chen
- The First School of Clinical Medical, Lanzhou University, Lanzhou 730000, China
| | - Q Zhou
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou 730000, China
| | - X Ke
- Department of Lung Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518112, China
| | - G B Li
- Department of Lung Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518112, China
| | - Q Q Guo
- School of Public Health, Lanzhou University, Lanzhou 730000, China
| | - Y L Chen
- Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences(2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China
| | - S H Lu
- Department of Lung Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518112, China
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37
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Sun LJ, Fu Q, Di MJ, Zhou Q, Chen XD. [Mammary myofibroblastoma with extensive atypical/bizarre cells: report of a case]. Zhonghua Bing Li Xue Za Zhi 2023; 52:862-864. [PMID: 37527998 DOI: 10.3760/cma.j.cn112151-20221221-01053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Affiliation(s)
- L J Sun
- Department of Pathology, Xiaoshan Affiliated Hospital of Wenzhou Medical University (the First People's Hospital of Xiaoshan District), Hangzhou 311200, China
| | - Q Fu
- Department of Pathology, Xiaoshan Affiliated Hospital of Wenzhou Medical University (the First People's Hospital of Xiaoshan District), Hangzhou 311200, China
| | - M J Di
- Department of Pathology, Xiaoshan Affiliated Hospital of Wenzhou Medical University (the First People's Hospital of Xiaoshan District), Hangzhou 311200, China
| | - Q Zhou
- Department of Pathology, Xiaoshan Affiliated Hospital of Wenzhou Medical University (the First People's Hospital of Xiaoshan District), Hangzhou 311200, China
| | - X D Chen
- Department of Pathology, Xiaoshan Affiliated Hospital of Wenzhou Medical University (the First People's Hospital of Xiaoshan District), Hangzhou 311200, China
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38
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Xu JX, Guo CY, Yuan P, Wang BZ, Zhou Q, Ying JM. [Mediastinal germ cell tumor with somatic-type malignancy: report of a case]. Zhonghua Bing Li Xue Za Zhi 2023; 52:733-735. [PMID: 37408409 DOI: 10.3760/cma.j.cn112151-20230212-00120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Affiliation(s)
- J X Xu
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - C Y Guo
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - P Yuan
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - B Z Wang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Q Zhou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J M Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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39
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Wu QS, Mao SQ, Xu Y, Gong RJ, Zhou Q, Liu M, Liu JY, Zhu DH, Guo X. [Safety of delayed vaccination with the national immunization program vaccines in children aged 0-6 years from 2019 to 2021 in Xuhui District, Shanghai City in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:983-991. [PMID: 37482734 DOI: 10.3760/cma.j.cn112150-20220804-00787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Objective: To understand the incidence of delayed vaccination with the national immunization program vaccines among children aged 0-6 years in Xuhui District, Shanghai, and to evaluate the safety of delayed vaccination. Methods: A stratified random sampling was used to obtain six vaccination clinics in Xuhui District, Shanghai. The vaccination records of children 0-6 years from these six vaccination clinics were collected from the Shanghai Immunization Program Information Management System. Adverse events following immunization (AEFI) data were collected from the China Information System for Disease Control and Prevention. Descriptive epidemiology was used to analyze the data. Children were divided into the timely vaccination group and delayed vaccination group according whether they were delayed in vaccination (received one month or more after the recommended age among children aged ≤1 year; received three months or more after the recommended age among children aged >1 year). The safety of four vaccination methods-individual vaccination, simultaneous vaccination, routine vaccination and combined vaccination-were further compared. Differences between groups were compared using chi-square test. Results: From 2019 to 2021, six vaccination clinics in Xuhui District administered 124 031 doses of the national immunization program vaccines among children aged 0-6 years, and delayed vaccinations accounted for 25.99% (32 234/124 031) of these doses. In 2020, the delayed vaccination rate during the first-level COVID-19 public health emergency response period in Shanghai was significantly higher than that in the same period in 2019 (34.70% vs. 24.19%, χ2=136.23, P<0.05). The delayed vaccination rate during the COVID-19 vaccination campaign in 2021 was significantly higher than that in the same period in 2019 (25.27% vs. 22.55%, χ2=82.80, P<0.05). From 2019 to 2021, a total of 475 cases of AEFI were reported in six vaccination clinics, with a reported incidence of 382.97 per 100 000 doses, including 421 cases of common adverse reaction (88.63%, 339.43 per 100 000 doses), 51 cases of rare adverse reaction (10.74%, 41.12 per 100 000 doses) and 3 cases of coincidences (0.63%, 2.42 per 100 000 doses). The reported incidence of AEFI among delayed vaccinations was significantly lower than that among timely vaccinations (291.62 per 100 000 doses vs. 415.05 per 100 000 doses). The incidence of AEFI for the four delayed vaccination methods (individual vaccination, simultaneous vaccination, routine vaccination and combined vaccination) was lower than that for timely vaccination. There were significant differences between the groups except for the routine vaccination group (χ2=9.82, P<0.05; χ2=5.46, P<0.05; χ2=2.97, P>0.05; χ2=11.89, P<0.05). Conclusions: In Xuhui District of Shanghai, 25.99% of doses of the national immunization program vaccines administered to children 0-6 years were delayed. Delayed vaccination does not increase the risk of AEFI compared with timely vaccination.
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Affiliation(s)
- Q S Wu
- Department of Immunization, Xuhui District Center for Disease Control and Prevention, Shanghai 200237, China
| | - S Q Mao
- Department of Immunization, Xuhui District Center for Disease Control and Prevention, Shanghai 200237, China
| | - Y Xu
- Department of Immunization, Xuhui District Center for Disease Control and Prevention, Shanghai 200237, China
| | - R J Gong
- Department of Immunization, Xuhui District Center for Disease Control and Prevention, Shanghai 200237, China
| | - Q Zhou
- Department of Immunization, Xuhui District Center for Disease Control and Prevention, Shanghai 200237, China
| | - M Liu
- Department of Immunization, Xuhui District Center for Disease Control and Prevention, Shanghai 200237, China
| | - J Y Liu
- Department of Immunization, Xuhui District Center for Disease Control and Prevention, Shanghai 200237, China
| | - D H Zhu
- Clinic of Vaccination, Xujiahui Community Health Service Centre in Xuhui District, Shanghai 200030, China
| | - X Guo
- Department of Immunization Program, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
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40
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Zhu Y, Zhou Q, Yu Y, Cao Y, Chen C, Zhai XW, Wang J, Wang HS. [A case of neonatal multi-system Langerhans cell histiocytosis treated by dabrafenib]. Zhonghua Er Ke Za Zhi 2023; 61:655-658. [PMID: 37385813 DOI: 10.3760/cma.j.cn112140-20230301-00149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Affiliation(s)
- Y Zhu
- Department of Neonatology,Children's Hospital of Fudan University,National Children's Medical Center, Shanghai 201102,China
| | - Q Zhou
- Department of Neonatology,Children's Hospital of Fudan University,National Children's Medical Center, Shanghai 201102,China
| | - Y Yu
- Department of Hematology,Children's Hospital of Fudan University,National Children's Medical Center, Shanghai 201102, China
| | - Y Cao
- Department of Neonatology,Children's Hospital of Fudan University,National Children's Medical Center, Shanghai 201102,China
| | - C Chen
- Department of Neonatology,Children's Hospital of Fudan University,National Children's Medical Center, Shanghai 201102,China
| | - X W Zhai
- Department of Hematology,Children's Hospital of Fudan University,National Children's Medical Center, Shanghai 201102, China
| | - J Wang
- Department of Neonatology,Children's Hospital of Fudan University,National Children's Medical Center, Shanghai 201102,China
| | - H S Wang
- Department of Hematology,Children's Hospital of Fudan University,National Children's Medical Center, Shanghai 201102, China
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Zhang WH, Zhang ZY, Liu Y, Tan ZY, Zhou Q, Lin YZ. High-throughput miRNA sequencing and identification of a novel ICE1-targeting miRNA in response to low temperature stress in Eucalyptus camaldulensis. Plant Biol (Stuttg) 2023; 25:541-550. [PMID: 36971569 DOI: 10.1111/plb.13520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/14/2023] [Indexed: 05/17/2023]
Abstract
MicroRNAs (miRNAs) play a crucial role in the growth, development, morphogenesis, signal transduction, and stress response in plants. The ICE (Inducer of CBF expression)-CBF (C-repeat binding factor)-COR (Cold-regulated gene) regulatory cascade is an important signalling pathway in plant response to low temperature stress, and it remains unknown whether this pathway is regulated by miRNAs. In this study, high-throughput sequencing was employed for predicting and identifying the miRNAs that were likely to target the ICE-CBF-COR pathway in Eucalyptus camaldulensis. A novel ICE1-targeting miRNA, eca-novel-miR-259-5p (nov-miR259), was further analysed. A total of 392 conserved miRNAs and 97 novel miRNAs were predicted, including 80 differentially expressed miRNAs. Of these, 30 miRNAs were predicted to be associated with the ICE-CBF-COR pathway. The full-length of mature nov-miR259 was 22 bp and its precursor gene was 60 bp in length, with a typical hairpin structure. The RNA ligase-mediated 5' amplification of cDNA ends (5'-RLM-RACE) and Agrobacterium-mediated tobacco transient expression assays demonstrated that nov-miR259 could cleave EcaICE1 in vivo. Moreover, qRT-PCR and Pearson's correlation analysis further revealed that the expression levels of nov-miR259 were almost significantly negatively correlated with those of its target gene, EcaICE1, and the other genes in the ICE-CBF-COR pathway. We first identified the nov-miR259 as a novel ICE1-targeting miRNA, and the nov-miR259-ICE1 module may be involved in regulating the cold stress response in E. camaldulensis.
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Affiliation(s)
- W-H Zhang
- College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, China
- Guangdong Academy of Forestry, Guangzhou, China
| | - Z-Y Zhang
- College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, China
- Guangzhou Huayin Medical Laboratory Center Limited, Guangzhou, China
| | - Y Liu
- College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, China
- Guangdong Key Laboratory for Innovative Development and Utilization of Forest Plant Germplasm, Guangzhou, China
| | - Z-Y Tan
- College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, China
- Guangdong Key Laboratory for Innovative Development and Utilization of Forest Plant Germplasm, Guangzhou, China
| | - Q Zhou
- College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, China
- Guangdong Key Laboratory for Innovative Development and Utilization of Forest Plant Germplasm, Guangzhou, China
| | - Y-Z Lin
- College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, China
- Guangdong Key Laboratory for Innovative Development and Utilization of Forest Plant Germplasm, Guangzhou, China
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42
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Wang J, Chen F, Ma Y, Wang L, Fei Z, Shuai J, Tang X, Zhou Q, Qin J. XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers. IEEE Trans Med Imaging 2023; 42:1735-1745. [PMID: 37018671 DOI: 10.1109/tmi.2023.3236037] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, XBound-Former, to simultaneously address the variation and boundary problems of skin lesion segmentation. XBound-Former is a purely attention-based network and catches boundary knowledge via three specially designed learners. First, we propose an implicit boundary learner (im-Bound) to constrain the network attention on the points with noticeable boundary variation, enhancing the local context modeling while maintaining the global context. Second, we propose an explicit boundary learner (ex-Bound) to extract the boundary knowledge at multiple scales and convert it into embeddings explicitly. Third, based on the learned multi-scale boundary embeddings, we propose a cross-scale boundary learner (X-Bound) to simultaneously address the problem of ambiguous and multi-scale boundaries by using learned boundary embedding from one scale to guide the boundary-aware attention on the other scales. We evaluate the model on two skin lesion datasets and one polyp lesion dataset, where our model consistently outperforms other convolution- and transformer-based models, especially on the boundary-wise metrics. All resources could be found in https://github.com/jcwang123/xboundformer.
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Sha X, Wang H, Sha H, Xie L, Zhou Q, Zhang W, Yin Y. Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network. Front Oncol 2023; 13:1172424. [PMID: 37324028 PMCID: PMC10266488 DOI: 10.3389/fonc.2023.1172424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/05/2023] [Indexed: 06/17/2023] Open
Abstract
Purpose/Objectives The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy. Materials/Methods Computed tomography (CT) scans from 265 rectal cancer patients treated at our institution were collected to train and validate automatic contouring models. The regions of CTV and OARs were delineated by experienced radiologists as the ground truth. We improved the conventional U-Net and proposed Flex U-Net, which used a register model to correct the noise caused by manual annotation, thus refining the performance of the automatic segmentation model. Then, we compared its performance with that of U-Net and V-Net. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD) were calculated for quantitative evaluation purposes. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P< 0.05). Results Our proposed framework achieved DSC values of 0.817 ± 0.071, 0.930 ± 0.076, 0.927 ± 0.03, and 0.925 ± 0.03 for CTV, the bladder, Femur head-L and Femur head-R, respectively. Conversely, the baseline results were 0.803 ± 0.082, 0.917 ± 0.105, 0.923 ± 0.03 and 0.917 ± 0.03, respectively. Conclusion In conclusion, our proposed Flex U-Net can enable satisfactory CTV and OAR segmentation for rectal cancer and yield superior performance compared to conventional methods. This method provides an automatic, fast and consistent solution for CTV and OAR segmentation and exhibits potential to be widely applied for radiation therapy planning for a variety of cancers.
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Affiliation(s)
- Xue Sha
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hui Wang
- Department of Radiation Oncology, Qingdao Central Hospital, Qingdao, Shandong, China
| | - Hui Sha
- Hunan Cancer Hospital, Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Lu Xie
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - Qichao Zhou
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - Wei Zhang
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Liu HS, Wu Z, Yang RY, Chen GZ, Li Y, Zhou Q, Yuan HP, Yang Z, Sun L. [Association between serum lysophosphatidylcholine level and elderly health index in older people from longevity areas of Guangxi Province]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:649-653. [PMID: 37165812 DOI: 10.3760/cma.j.cn112150-20221124-01144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Objective: To investigate the relationship between serum lysophosphatidylcholine (LPC) level and the health index of the elderly. Methods: A total of 251 subjects were selected from the 2016 baseline survey of the Yongfu Longevity Cohort in Guangxi Province among whom 66, 63 and 122 were in the young and middle-aged group (≤59 years old), the young group (60-89 years old) and the longevity group (≥90 years old), respectively. Demographic data were collected and related indicators of height, weight, blood pressure and lipid metabolism were measured. The cognitive and physical functions of the elderly were assessed by the results of the simple mental state scale and the daily living activity scale to construct the health index of the elderly. The serum levels of LPC16∶0, LPC18∶0, LPC18∶1 and LPC18∶2 were determined by liquid chromatography-tandem mass spectrometry, and the differences among different ages and health status groups were compared. The logistic regression model was used to analyze the relationship between the serum LPC level and the health index of the elderly. Results: With the increase in age, the proportion of female subjects increased, and the rate of smoking and drinking decreased. BMI, TC, TG, LDL-C, diastolic blood pressure, and the four LPCs levels decreased with the increase of age, and systolic blood pressure levels increased with the increase of age (all P values<0.05). There was no significant difference in HDL-C levels among age groups (P>0.05). With the decline of health status in the elderly, serum levels of LPC16∶0, LPC18∶0, LPC18∶1 and LPC18∶2 showed a downward trend (all P values<0.001). After adjusting for age and gender, only LPC18∶0 was associated with the health status in old age [OR (95%CI): 0.48 (0.25-0.92)]. For every 1 standard deviation (16.87 nmol/L) increase in serum LPC18∶0 concentration, the risk of poor health status in old age decreased by 52%. Conclusion: Serum LPC18∶0 was associated with the health status in old age independent of age and sex.
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Affiliation(s)
- H S Liu
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - Z Wu
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - R Y Yang
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - G Z Chen
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - Y Li
- Department of Geriatrics, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Q Zhou
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - H P Yuan
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - Z Yang
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - L Sun
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
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Liu HS, Wu Z, Yang RY, Chen GZ, Li Y, Du SC, Zhou Q, Yuan HP, Yang Z, Sun L. [Research progress on main disease-related factors of healthy life expectancy]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:654-658. [PMID: 37165813 DOI: 10.3760/cma.j.cn112150-20221124-01146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
International research on healthy life expectancy (HALE) focuses on inequality of socioeconomic status and individual natural attributes. With the acceleration of population ageing and the increase in average life expectancy, the extension of unhealthy life expectancy and the increase of social and economic burden caused by diseases have gradually attracted the attention of countries around the world. Therefore, the evaluation of disease factors affecting HALE is a meaningful direction in the future. This study introduces the development process and commonly used measurement methods of HALE. According to the definition of health from the Global Burden of Disease Study and World Health Organization, physical and mental diseases such as cardiovascular and cerebrovascular diseases, chronic respiratory diseases, diabetes, malignant tumors and depression were selected to summarize the impact of these diseases and pre-disease states on HALE. It is expected to provide a theoretical basis for the formulation of relevant public health policies and the improvement of quality of life in China.
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Affiliation(s)
- H S Liu
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - Z Wu
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - R Y Yang
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - G Z Chen
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - Y Li
- Department of Geriatrics, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - S C Du
- Department of Geriatrics, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Q Zhou
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - H P Yuan
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - Z Yang
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China
| | - L Sun
- The Key Laboratory of Geriatrics/Beijing Institute of Geriatrics/Institute of Geriatric Medicine/Chinese Academy of Medical Sciences/Beijing Hospital/National Center of Gerontology of National Health Commission,Beijing 100730, China Department of Geriatrics, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
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Yang B, Liu Y, Chen Z, Wang Z, Zhou Q, Qiu J. Tissues margin-based analytical anisotropic algorithm boosting method via deep learning attention mechanism with cervical cancer. Int J Comput Assist Radiol Surg 2023; 18:953-959. [PMID: 36460828 DOI: 10.1007/s11548-022-02801-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 11/19/2022] [Indexed: 12/04/2022]
Abstract
PURPOSE Speed and accuracy are two critical factors in dose calculation for radiotherapy. Analytical Anisotropic Algorithm (AAA) is a rapid dose calculation algorithm but has dose errors in tissue margin area. Acuros XB (AXB) has high accuracy but takes long time to calculate. To improve the dose accuracy on the tissue margin area for AAA, we proposed a novel deep learning-based dose accuracy improvement method using Margin-Net combined with Margin-Loss. METHODS A novel model 'Margin-Net' was designed with a Margin Attention Mechanism to generate special margin-related features. Margin-Loss was introduced to consider the dose errors and dose gradients in tissues margin area. Ninety-five VMAT cervical cancer cases with paired AAA and AXB dose were enrolled in our study: 76 cases for training and 19 cases for testing. Tissues Margin Masks were generated from RT contours with 6 mm extension. Tissues Margin Mask, AAA dose and CTs were input data; AXB dose was used as reference dose for model training and evaluation. Comparison experiments were performed to evaluated effectiveness of Margin-Net and Margin-Loss. RESULTS Compared to AXB dose, the 3D gamma passing rate (1%/1 mm, 10% threshold) for 19 test cases 95.75 ± 1.05% using Margin-Net with Margin-Loss, which was significantly higher than the original AAA dose (73.64 ± 3.46%). The passing rate reduced to 94.07 ± 1.16% without Margin-Loss and 87.3 ± 1.18% if Margin-Net key structure 'MAM' was also removed. CONCLUSION The proposed novel tissues margin-based dose conversion method can significantly improve the dose accuracy of Analytical Anisotropic Algorithm to be comparable to AXB algorithm. It can potentially improve the efficiency of treatment planning process with low demanding of computation resources.
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Affiliation(s)
- Bo Yang
- Peking Union Medical College Hospital, Beijing, 100005, China
| | - Yaoying Liu
- School of Physics, Beihang University, Beijing, 102206, China
- Manteia Technologies Co., Ltd, Xiamen, 361008, China
| | - Zhaocai Chen
- Manteia Technologies Co., Ltd, Xiamen, 361008, China
| | - Zhiqun Wang
- Peking Union Medical College Hospital, Beijing, 100005, China
| | - Qichao Zhou
- Manteia Technologies Co., Ltd, Xiamen, 361008, China.
| | - Jie Qiu
- Peking Union Medical College Hospital, Beijing, 100005, China.
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Du ZQ, Jiang Y, Lu RR, Zhou Q, Shen Y, Zhu HH. Pharmaceutical care of vascular dementia patients with drug-induced liver injury caused by the Compound Congrong Yizhi Capsules: a case report. Eur Rev Med Pharmacol Sci 2023; 27:4693-4697. [PMID: 37259753 DOI: 10.26355/eurrev_202305_32481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
BACKGROUND Drug-induced liver injury (DILI) is a newly discovered adverse drug reaction of Compound Congrong Yizhi Capsules (CCYC) in the treatment of vascular dementia (VD), and targeted pharmaceutical care is urgently needed to be explored. CASE REPORT DILI was found in a patient who was admitted to the hospital with a diagnosis of VD after treatment with Compound Congrong Yizhi Capsules. According to the guidelines, the patient was initially treated with magnesium isoglycyrrhizinate injection. After 4 days, the clinical pharmacist monitored liver function: alanine aminotransferase (ALT): 153 IU/L, aspartate aminotransferase (AST): 160 IU/L, total bilirubin (TBil): 4.5 µmol/L, and alkaline phosphatase (ALP): 551 IU/L, which indicated that DILI was further aggravated. In addition, the increased blood pressure (156/65 mmHg) indicated the requirement to adjust the medication. Then, the hepatoprotective drugs were adjusted with reduced glutathione combined with ursodeoxycholic acid. After 12 days of treatment, the liver function was significantly improved, the clinical treatment was effective, and the blood pressure was controlled stably with no obvious adverse drug reactions. CONCLUSIONS With pharmaceutical care guided by clinical pharmacists, the DILI caused by Compound Congrong Yizhi capsules could be reversed to improve the clinical outcome and avoid the occurrence of serious complications.
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Affiliation(s)
- Z-Q Du
- The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China.
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Xu YY, Su ZZ, Zheng LM, Zhang MN, Tan JY, Yang YL, Zhang MX, Xu M, Chen N, Chen XQ, Zhou Q. [Read-through circular RNA rt-circ-HS promotes hypoxia inducible factor 1α expression and renal carcinoma cell proliferation, migration and invasiveness]. Beijing Da Xue Xue Bao Yi Xue Ban 2023; 55:217-227. [PMID: 37042131 PMCID: PMC10091263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
OBJECTIVE To identify and characterize read-through RNAs and read-through circular RNAs (rt-circ-HS) derived from transcriptional read-through hypoxia inducible factor 1α (HIF1α) and small nuclear RNA activating complex polypeptide 1 (SNAPC1) the two adjacent genes located on chromosome 14q23, in renal carcinoma cells and renal carcinoma tissues, and to study the effects of rt-circ-HS on biological behavior of renal carcinoma cells and on regulation of HIF1α. METHODS Reverse transcription-polymerase chain reaction (RT-PCR) and Sanger sequencing were used to examine expression of read-through RNAs HIF1α-SNAPC1 and rt-circ-HS in different tumor cells. Tissue microarrays of 437 different types of renal cell carcinoma (RCC) were constructed, and chromogenic in situ hybridization (ISH) was used to investigate expression of rt-circ-HS in different RCC types. Small interference RNA (siRNA) and artificial overexpression plasmids were designed to examine the effects of rt-circ-HS on 786-O and A498 renal carcinoma cell proliferation, migration and invasiveness by cell counting kit 8 (CCK8), EdU incorporation and Transwell cell migration and invasion assays. RT-PCR and Western blot were used to exa-mine expression of HIF1α and SNAPC1 RNA and proteins after interference of rt-circ-HS with siRNA, respectively. The binding of rt-circ-HS with microRNA 539 (miR-539), and miR-539 with HIF1α 3' untranslated region (3' UTR), and the effects of these interactions were investigated by dual luciferase reporter gene assays. RESULTS We discovered a novel 1 144 nt rt-circ-HS, which was derived from read-through RNA HIF1α-SNAPC1 and consisted of HIF1α exon 2-6 and SNAPC1 exon 2-4. Expression of rt-circ-HS was significantly upregulated in 786-O renal carcinoma cells. ISH showed that the overall positive expression rate of rt-circ-HS in RCC tissue samples was 67.5% (295/437), and the expression was different in different types of RCCs. Mechanistically, rt-circ-HS promoted renal carcinoma cell proliferation, migration and invasiveness by functioning as a competitive endogenous inhibitor of miR-539, which we found to be a potent post-transcriptional suppressor of HIF1α, thus promoting expression of HIF1α. CONCLUSION The novel rt-circ-HS is highly expressed in different types of RCCs and acts as a competitive endogenous inhibitor of miR-539 to promote expression of its parental gene HIF1α and thus the proliferation, migration and invasion of renal cancer cells.
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Affiliation(s)
- Y Y Xu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Z Z Su
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - L M Zheng
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - M N Zhang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - J Y Tan
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Y L Yang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - M X Zhang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - M Xu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - N Chen
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - X Q Chen
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Q Zhou
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
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Chen N, Zhou Q. [The 5th WHO classification of prostate tumors: an update and interpretation]. Zhonghua Bing Li Xue Za Zhi 2023; 52:321-328. [PMID: 36973190 DOI: 10.3760/cma.j.cn112151-20221208-01030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Affiliation(s)
- N Chen
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Q Zhou
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
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Fu Y, Cai J, Chen Y, Zhou Q, Xu YM, Shi J, Fan XS. [Concordance between three integrated scores based on prostate biopsy and grade-grouping of radical prostatectomy specimen]. Zhonghua Bing Li Xue Za Zhi 2023; 52:353-357. [PMID: 36973195 DOI: 10.3760/cma.j.cn112151-20221125-00992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Objective: To analyze three different integrated scoring schemes of prostate biopsy and to compare their concordance with the scoring of radical prostatectomy specimens. Methods: A retrospective analysis of 556 patients with radical prostatectomy performed in Nanjing Drum Tower Hospital, Nanjing, China from 2017 to 2020. In these cases, whole organ sections were performed, the pathological data based on biopsy and radical prostatectomy specimens were summarized, and 3 integrated scores of prostate biopsy were calculated, namely the global score, the highest score and score of the largest volume. Results: Among the 556 patients, 104 cases (18.7%) were classified as WHO/ISUP grade group 1, 227 cases (40.8%) as grade group 2 (3+4=7); 143 cases (25.7%) as grade group 3 (4+3=7); 44 cases (7.9%) as grade group 4 (4+4=8) and 38 cases (6.8%) as grade group 5. Among the three comprehensive scoring methods for prostate cancer biopsy, the consistency of global score was the highest (62.4%). In the correlation analysis, the correlation between the scores of radical specimens and the global scores was highest (R=0.730, P<0.01), while the correlations of the scores based on radical specimens with highest scores and scores of the largest volume based on biopsy were insignificant (R=0.719, P<0.01; R=0.631, P<0.01, respectively). Univariate and multivariate analyses showed tPSA group and the three integrated scores of prostate biopsy were statistically correlated with extraglandular invasion, lymph node metastasis, perineural invasion and biochemical recurrence. Elevated global score was an independent prognostic risk factor for extraglandular invasion and biochemical recurrence in patients; increased serum tPSA was an independent prognostic risk factor for extraglandular invasion; increased hjighest score was an independent risk factor for perineural invasion. Conclusions: In this study, among the three different integrated scores, the overall score is most likely corresponded to the radical specimen grade group, but there is difference in various subgroup analyses. Integrated score of prostate biopsy can reflect grade group of radical prostatectomy specimens, thereby providing more clinical information for assisting in optimal patient management and consultation.
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Affiliation(s)
- Y Fu
- Department of Pathology, the Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - J Cai
- Department of Pathology, Nanjing Jiangning Hospital, the Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 210000, China
| | - Y Chen
- Department of Pathology, the Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Q Zhou
- Department of Pathology, the Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Y M Xu
- Department of Pathology, the Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - J Shi
- Department of Pathology, the Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - X S Fan
- Department of Pathology, the Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
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