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Huang Z, Xie L, Feng H, Lan M, Xu T, Chen D, Pu L, Lu Y. DAZL regulate germline, pluripotency, and proliferation related genes in chicken PGCs and cooperate with DDX4. Theriogenology 2024; 222:22-30. [PMID: 38615433 DOI: 10.1016/j.theriogenology.2024.03.010] [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: 09/27/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 04/16/2024]
Abstract
Primordial germ cells (PGCs) are the precursors of germ cells and play a crucial role in germline transmission. In chickens, PGCs can be cultured in vitro while maintaining their germline stem cell characteristics. The Deleted in Azoospermia-Like (DAZL) gene, which is highly expressed in PGCs, is essential for germ cell development. Here, through gene knockout experiments, we discovered that the loss of DAZL expression in chicken PGCs led to decreased proliferation and survival. By next employed techniques such as RIP-seq (RNA Binding Protein Immunoprecipitation) and Co-IP-MS/MS (Co-immunoprecipitation Mass Spectrometry), we identified genes directly regulated by DAZL or cooperating with DAZL at the transcriptomic and proteomic levels. DAZL was found to control genes related to germline development, pluripotency, and cell proliferation in PGCs. Additionally, we observed a significant overlap between RNAs and proteins that interact with both DAZL and DDX4, indicating their cooperation in the gene regulation network in chicken PGCs. Our research provides valuable insights into the function of the DAZL gene in germline cells.
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Affiliation(s)
- Zhenwen Huang
- From the Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Long Xie
- From the Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Hu Feng
- From the Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Meiyu Lan
- From the Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Tianpeng Xu
- From the Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Dongyang Chen
- From the Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Liping Pu
- From the Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Yangqing Lu
- From the Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China.
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Xin Y, Feng H, He C, Lu H, Zuo E, Yan N. Development of a universal antibiotic resistance screening system for efficient enrichment of C-to-G and A-to-G base editing. Int J Biol Macromol 2024; 268:131785. [PMID: 38679258 DOI: 10.1016/j.ijbiomac.2024.131785] [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: 02/01/2024] [Revised: 03/31/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
To expand the scope of genomic editing, a C-to-G transversion-based editor called CGBE has been developed for precise single-nucleotide genomic editing. However, limited editing efficiency and product purity have hindered the development and application of CGBE. In this study, we introduced the Puromycin-Resistance Screening System, referred to as CGBE/ABE-PRSS, to select genetically modified cells via the CGBE or ABE editors. The CGBE/ABE-PRSS system significantly improves the enrichment efficiency of CGBE- or ABE-modified cells, showing enhancements of up to 59.6 % compared with the controls. Our findings indicate that the CGBE/ABE-PRSS, when driven by the CMV promoter, results in a higher enrichment of edited cells compared to the CAG and EF1α promoters. Furthermore, we demonstrate that this system is compatible with different versions of both CGBE and ABE, enabling various cell species and simultaneous multiplexed genome editing without any detectable random off-targets. In conclusion, our developed CGBE/ABE-PRSS system facilitates the selection of edited cells and holds promise in both basic engineering and gene therapy applications.
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Affiliation(s)
- Ying Xin
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hu Feng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Chenfei He
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hongjiang Lu
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Erwei Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Nana Yan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China..
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Liu H, Wang X, Feng H, Zhou S, Pan J, Ouyang C, Hu X. Obstructive sleep apnea and mental disorders: a bidirectional mendelian randomization study. BMC Psychiatry 2024; 24:304. [PMID: 38654235 DOI: 10.1186/s12888-024-05754-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Previous studies have reported associations between obstructive sleep apnea (OSA) and several mental disorders. However, further research is required to determine whether these associations are causal. Therefore, we evaluated the bidirectional causality between the genetic liability for OSA and nine mental disorders by using Mendelian randomization (MR). METHOD We performed two-sample bidirectional MR of genetic variants for OSA and nine mental disorders. Summary statistics on OSA and the nine mental disorders were extracted from the FinnGen study and the Psychiatric Genomics Consortium. The primary analytical approach for estimating causal effects was the inverse-variance weighted (IVW), with the weighted median and MR Egger as complementary methods. The MR Egger intercept test, Cochran's Q test, Rucker's Q test, and the MR pleiotropy residual sum and outlier (MR-PRESSO) test were used for sensitivity analyses. RESULT MR analyses showed that genetic liability for major depressive disorder (MDD) was associated with an increased risk of OSA (odds ratio [OR] per unit increase in the risk of MDD, 1.29; 95% CI, 1.11-1.49; P < 0.001). In addition, genetic liability for OSA may be associated with an increased risk of attention-deficit/hyperactivity disorder (ADHD) (OR = 1.26; 95% CI, 1.02-1.56; p = 0.032). There was no evidence that OSA is associated with other mental disorders. CONCLUSION Our study indicated that genetic liability for MDD is associated with an increased risk of OSA without a bidirectional relationship. Additionally, there was suggestive evidence that genetic liability for OSA may have a causal effect on ADHD. These findings have implications for prevention and intervention strategies targeting OSA and ADHD. Further research is needed to investigate the biological mechanisms underlying our findings and the relationship between OSA and other mental disorders.
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Affiliation(s)
- Heming Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, No.199, Donggang West Road, Chengguan District, 730000, Lanzhou, Gansu Province, China
| | - Xuemei Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, No.199, Donggang West Road, Chengguan District, 730000, Lanzhou, Gansu Province, China
| | - Hu Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, No.199, Donggang West Road, Chengguan District, 730000, Lanzhou, Gansu Province, China
| | - Shengze Zhou
- Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, No.199, Donggang West Road, Chengguan District, 730000, Lanzhou, Gansu Province, China
| | - Jinhua Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, No.199, Donggang West Road, Chengguan District, 730000, Lanzhou, Gansu Province, China
| | - Changping Ouyang
- Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, No.199, Donggang West Road, Chengguan District, 730000, Lanzhou, Gansu Province, China
| | - Xiaobin Hu
- Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, No.199, Donggang West Road, Chengguan District, 730000, Lanzhou, Gansu Province, China.
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Cao W, Feng H, Yang Y, Wang L, Wang X, Ma Y, Zhao D, Hu X. Trends in antidiabetic drug use and expenditure in public hospitals in Northwest China, 2012-21: a case study of Gansu Province. BMC Health Serv Res 2024; 24:415. [PMID: 38570849 PMCID: PMC10988802 DOI: 10.1186/s12913-024-10917-0] [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: 08/16/2023] [Accepted: 03/27/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Since the twenty-first century, the prevalence of diabetes has risen globally year by year. In Gansu Province, an economically underdeveloped province in northwest China, the cost of drugs for diabetes patients accounted for one-third of their total drug costs. To fundamentally reduce national drug expenditures and the burden of medication on the population, the relevant departments of government have continued to reform and improve drug policies. This study aimed to analyse long-term trends in antidiabetic drug use and expenditure in Gansu Province from 2012 to 2021 and to explore the role of pharmaceutical policy. METHODS Data were obtained from the provincial centralised bidding and purchasing (CBP) platform. Drug use was quantified using the anatomical therapeutic chemistry/defined daily dose (ATC/DDD) method and standardised by DDD per 1000 inhabitants per day (DID), and drug expenditure was expressed in terms of the total amount and defined daily cost (DDC). Linear regression was used to analyse the trends and magnitude of drug use and expenditure. RESULTS The overall trend in the use and expenditure of antidiabetic drugs was on the rise, with the use increasing from 1.04 in 2012 to 16.02 DID in 2021 and the expenditure increasing from 48.36 in 2012 to 496.42 million yuan in 2021 (from 7.66 to 76.95 million USD). Some new and expensive drugs changed in the use pattern, and their use and expenditure shares (as the percentage of all antidiabetic drugs) increased from 0 to 11.17% and 11.37%, but insulins and analogues and biguanides remained the most used drug class. The DDC of oral drugs all showed a decreasing trend, but essential medicines (EMs) and medical insurance drugs DDC gradually decreased with increasing use. The price reduction of the bid-winning drugs was over 40%, and the top three drugs were glimepiride 2mg/30, acarbose 50mg/30 and acarbose 100mg/30. CONCLUSIONS The implementation of pharmaceutical policies has significantly increased drug use and expenditure while reducing drug prices, and the introduction of novel drugs and updated treatment guidelines has led to changes in use patterns.
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Affiliation(s)
- Wenxuan Cao
- School of Public Health, Lanzhou University, 222# Tianshui South Road, Lanzhou, 730000, China
| | - Hu Feng
- School of Public Health, Lanzhou University, 222# Tianshui South Road, Lanzhou, 730000, China
| | - Yaya Yang
- School of Public Health, Lanzhou University, 222# Tianshui South Road, Lanzhou, 730000, China
| | - Lei Wang
- School of Public Health, Lanzhou University, 222# Tianshui South Road, Lanzhou, 730000, China
| | - Xuemei Wang
- School of Public Health, Lanzhou University, 222# Tianshui South Road, Lanzhou, 730000, China
| | - Yongheng Ma
- Division of Pharmaceutical Procurement, Gansu Public Resources Trading Center, 68# Yanxing Road, Lanzhou, 730000, China
| | - Defang Zhao
- Division of Pharmaceutical Procurement, Gansu Public Resources Trading Center, 68# Yanxing Road, Lanzhou, 730000, China
| | - Xiaobin Hu
- School of Public Health, Lanzhou University, 222# Tianshui South Road, Lanzhou, 730000, China.
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5
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Jian H, Feng H, Zhu L, Li X, Ma Z. MicroRNA-150-5P regulates Th1/Th2 cytokines expression levels by targeting EGR2 in allergic rhinitis. Rhinology 2024; 62:250-256. [PMID: 38165680 DOI: 10.4193/rhin23.223] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
BACKGROUND MiR-150-5p is one of the miRNAs in the expression profile of miRNAs, and in many previous studies, it has been shown that miR-150-5p may play an important role in peripheral blood dendritic cells (DCs) of allergic rhinitis (AR) patients. We sought to investigate the role and mechanism of miR-150-5p in regulating DC function by modulating EGR2 and influencing T cell derivation to promote AR development. METHODS The expression of miR-150-5p and EGR2 in AR patients was examined by real-time quantitative polymerase chain reaction (qRT-PCR), the expression of IL-4 cytokines in the supernatant of AR patients was tested by enzyme-linked immunosorbent assay (ELISA), and the expression of eosinophils in the supernatant of AR patients was measured by HE staining. The expression of EGR2 was detected by immunohistochemistry and fluorescent m-immunohistochemistry. RESULTS MiR-150-5p expression was up-regulated and EGR2 expression was down-regulated in peripheral blood DCs from AR patients. miR-150-5p upregulated DCs, which promoted T-cell differentiation. miR-150-5p further regulated EGR2, which suppressed DCs and caused alteration of T-cell differentiation, in turn triggering the occurrence of AR. CONCLUSION MiR-150-5p and its target gene EGR2 are involved in the development of AR, and DCs foster T-cell differentiation in peripheral blood of AR patients.
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Affiliation(s)
- H Jian
- Department of Otorhinolaryngology, the Third Affiliated Hospital of ZunYi Medical University/First People’s Hospital of Zunyi 563002, China
| | - H Feng
- Department of Otorhinolaryngology, the Third Affiliated Hospital of ZunYi Medical University/First People’s Hospital of Zunyi 563002, China
| | - L Zhu
- Department of Otorhinolaryngology, the Third Affiliated Hospital of ZunYi Medical University/First People’s Hospital of Zunyi 563002, China
| | - X Li
- Department of Otorhinolaryngology, the Third Affiliated Hospital of ZunYi Medical University/First People’s Hospital of Zunyi 563002, China
| | - Z Ma
- Department of Otorhinolaryngology, the Third Affiliated Hospital of ZunYi Medical University/First People’s Hospital of Zunyi 563002, China
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Yang J, Lv M, Han L, Li Y, Liu Y, Guo H, Feng H, Wu Y, Zhong J. Evaluation of brain iron deposition in different cerebral arteries of acute ischaemic stroke patients using quantitative susceptibility mapping. Clin Radiol 2024; 79:e592-e598. [PMID: 38320942 DOI: 10.1016/j.crad.2024.01.007] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/05/2023] [Accepted: 01/03/2024] [Indexed: 02/08/2024]
Abstract
AIM To investigate differences in iron deposition between infarct and normal cerebral arterial regions in acute ischaemic stroke (AIS) patients using quantitative susceptibility mapping (QSM). MATERIALS AND METHODS Forty healthy controls and 40 AIS patients were recruited, and their QSM images were obtained. There were seven regions of interest (ROIs) in AIS patients, including the infarct regions of responsible arteries (R1), the non-infarct regions of responsible arteries (R2), the contralateral symmetrical sites of lesions (R3), and the non-responsible cerebral arterial regions (R4, R5, R6, R7). For the healthy controls, the cerebral arterial regions corresponding to the AIS patient group were selected as ROIs. The differences in corresponding ROI susceptibilities between AIS patients and healthy controls and the differences in susceptibilities between infarcted and non-infarct regions in AIS patients were compared. RESULTS The susceptibilities of infarct regions in AIS patients were significantly higher than those in healthy controls (p<0.0001). There was no significant difference in non-infarct regions between the two groups (p>0.05). The susceptibility of the infarct regions in AIS patients was significantly higher than those of the non-infarct region of responsible artery and non-responsible cerebral arterial regions (p<0.01). CONCLUSIONS Abnormal iron deposition detected by QSM in the infarct regions of AIS patients may not affect iron levels in the non-infarct regions of responsible arteries and normal cerebral arteries, which may open the door for potential new diagnostic and treatment strategies.
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Affiliation(s)
- J Yang
- Department of Radiology, Zigong First People's Hospital, Zigong, China
| | - M Lv
- Department of Radiology, Zigong First People's Hospital, Zigong, China
| | - L Han
- North Sichuan Medical College, Nanchong, China
| | - Y Li
- Department of Radiology, Zigong First People's Hospital, Zigong, China
| | - Y Liu
- Department of Radiology, Zigong First People's Hospital, Zigong, China
| | - H Guo
- Department of Radiology, Zigong First People's Hospital, Zigong, China
| | - H Feng
- Department of Radiology, Zigong First People's Hospital, Zigong, China
| | - Y Wu
- MR Scientific Marketing, SIEMENS Healthineers Ltd., Shanghai, China
| | - J Zhong
- Department of Radiology, Zigong First People's Hospital, Zigong, China.
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Xu Y, Zhu XY, Feng H, Yu XP, Wang Y, Rong X, Qi TY. The value of quantitative contrast-enhanced ultrasonography analysis in evaluating central retinal artery microcirculation in patients with diabetes mellitus: comparison with colour Doppler imaging. Clin Radiol 2024; 79:e560-e566. [PMID: 38336532 DOI: 10.1016/j.crad.2024.01.011] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/12/2023] [Accepted: 01/12/2024] [Indexed: 02/12/2024]
Abstract
AIM To compare the efficacy of quantitative contrast-enhanced ultrasonography (CEUS) analysis and colour Doppler ultrasound (CDU) in evaluating central retinal artery (CRA) microcirculation in patients with diabetes mellitus (DM). MATERIALS AND METHODS In this prospective study, a total of 55 patients (98 eyes) with DM were enrolled as the study group. They were compared to 46 age-matched healthy volunteers (92 eyes) who were selected as the control group. Each patient underwent CDU and subsequent CEUS examination. CDU and quantitative CEUS parameters were evaluated. The diagnostic efficiency of the diagnostic performance of CEUS and CDU was evaluated and compared, and the scale thresholds of predictive indicators for the diagnosis of proliferative diabetic retinopathy (PDR) were evaluated using receiver operating characteristics (ROC) curve analyses. RESULTS Group pairwise comparisons showed that the end diastolic velocity (EDV) and arrival time (AT) of CRA were significant predictors for PDR by CDU and by quantitative CEUS analysis, respectively (all p<0.05). The ROC curve analysis showed that the area under the curve value of AT was significantly higher than that of EDV (0.875 versus 0.634, p=0.0002). Accordingly, an AT cut-off value of 1.07 seconds resulted a sensitivity of 90.62 % and a specificity of 79.31 %. CONCLUSION Quantitative CEUS analysis can improve the accuracy of clinical staging of diabetic retinopathy for the patients with DM, and the AT showed the best diagnostic efficiency.
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Affiliation(s)
- Y Xu
- Department of Ultrasound, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225012, China
| | - X Y Zhu
- Department of Ophthalmology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225012, China
| | - H Feng
- Department of Ultrasound, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225012, China
| | - X P Yu
- Department of Ultrasound, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225012, China
| | - Y Wang
- Department of Ophthalmology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225012, China
| | - X Rong
- Department of Ultrasound, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225012, China
| | - T Y Qi
- Department of Ultrasound, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225012, China.
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Qu Z, Deng B, Sun W, Yang R, Feng H. A Convolutional Neural Network for Automated Detection of Cervical Ossification of the Posterior Longitudinal Ligament using Magnetic Resonance Imaging. Clin Spine Surg 2024; 37:E106-E112. [PMID: 37941120 DOI: 10.1097/bsd.0000000000001547] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
STUDY DESIGN Retrospective cohort study. OBJECTIVE We aimed to develop and validate a convolutional neural network (CNN) model to distinguish between cervical ossification of posterior longitudinal ligament (OPLL) and multilevel degenerative spinal stenosis using Magnetic Resonance Imaging (MRI) and to compare the diagnostic ability with spine surgeons. SUMMARY OF BACKGROUND DATA Some artificial intelligence models have been applied in spinal image analysis and many of promising results were obtained; however, there was still no study attempted to develop a deep learning model in detecting cervical OPLL using MRI images. MATERIALS AND METHODS In this retrospective study, 272 cervical OPLL and 412 degenerative patients underwent surgical treatment were enrolled and divided into the training (513 cases) and test dataset (171 cases). CNN models applying ResNet architecture with 34, 50, and 101 layers of residual blocks were constructed and trained with the sagittal MRI images from the training dataset. To evaluate the performance of CNN, the receiver operating characteristic curves of 3 ResNet models were plotted and the area under the curve were calculated on the test dataset. The accuracy, sensitivity, and specificity of the diagnosis by the CNN were calculated and compared with 3 senior spine surgeons. RESULTS The diagnostic accuracies of our ResNet34, ResNet50, and ResNet101 models were 92.98%, 95.32%, and 97.66%, respectively; the area under the curve of receiver operating characteristic curves of these models were 0.914, 0.942, and 0.971, respectively. The accuracies and specificities of ResNet50 and ResNet101 models were significantly higher than all spine surgeons; for the sensitivity, ResNet101 model achieved better values than that of the 2 surgeons. CONCLUSION The performance of our ResNet model in differentiating cervical OPLL from degenerative spinal stenosis using MRI is promising, better results were achieved with more layers of residual blocks applied.
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Affiliation(s)
- Zhe Qu
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Bin Deng
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Wei Sun
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Ranran Yang
- Xuzhou Medical University
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hu Feng
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
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Cao Z, Aharonian F, Axikegu, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Bian W, Bukevich AV, Cao Q, Cao WY, Cao Z, Chang J, Chang JF, Chen AM, Chen ES, Chen HX, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen S, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng N, Cheng YD, Cui MY, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang JH, Fang K, Feng CF, Feng H, Feng L, Feng SH, Feng XT, Feng Y, Feng YL, Gabici S, Gao B, Gao CD, Gao Q, Gao W, Gao WK, Ge MM, Geng LS, Giacinti G, Gong GH, Gou QB, Gu MH, Guo FL, Guo XL, Guo YQ, Guo YY, Han YA, Hasan M, He HH, He HN, He JY, He Y, Hor YK, Hou BW, Hou C, Hou X, Hu HB, Hu Q, Hu SC, Huang DH, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Ji XL, Jia HY, Jia K, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kang MM, Karpikov I, Kuleshov D, Kurinov K, Li BB, Li CM, Li C, Li C, Li D, Li F, Li HB, Li HC, Li J, Li J, Li K, Li SD, Li WL, Li WL, Li XR, Li X, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu DB, Liu H, Liu HD, Liu J, Liu JL, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Luo Q, Luo Y, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Mu HJ, Nan YC, Neronov A, Ou LJ, Pattarakijwanich P, Pei ZY, Qi JC, Qi MY, Qiao BQ, Qin JJ, Raza A, Ruffolo D, Sáiz A, Saeed M, Semikoz D, Shao L, Shchegolev O, Sheng XD, Shu FW, Song HC, Stenkin YV, Stepanov V, Su Y, Sun DX, Sun QN, Sun XN, Sun ZB, Takata J, Tam PHT, Tang QW, Tang R, Tang ZB, Tian WW, Wang C, Wang CB, Wang GW, Wang HG, Wang HH, Wang JC, Wang K, Wang K, Wang LP, Wang LY, Wang PH, Wang R, Wang W, Wang XG, Wang XY, Wang Y, Wang YD, Wang YJ, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu QW, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xiang GM, Xiao DX, Xiao G, Xin YL, Xing Y, Xiong DR, Xiong Z, Xu DL, Xu RF, Xu RX, Xu WL, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang CY, Yang F, Yang FF, Yang LL, Yang MJ, Yang RZ, Yang WX, Yao YH, Yao ZG, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Yue H, Zeng HD, Zeng TX, Zeng W, Zha M, Zhang BB, Zhang F, Zhang H, Zhang HM, Zhang HY, Zhang JL, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zhao XH, Zheng F, Zhong WJ, Zhou B, Zhou H, Zhou JN, Zhou M, Zhou P, Zhou R, Zhou XX, Zhou XX, Zhu BY, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zou YC, Zuo X. Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A. Phys Rev Lett 2024; 132:131002. [PMID: 38613275 DOI: 10.1103/physrevlett.132.131002] [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: 11/13/2023] [Revised: 01/23/2024] [Accepted: 02/12/2024] [Indexed: 04/14/2024]
Abstract
We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at 3.67±0.05±0.15 PeV. Below the knee, the spectral index is found to be -2.7413±0.0004±0.0050, while above the knee, it is -3.128±0.005±0.027, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -0.1200±0.0003±0.0341. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components.
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Affiliation(s)
- Zhen Cao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F Aharonian
- Dublin Institute for Advanced Studies, 31 Fitzwilliam Place, 2 Dublin, Ireland
- Max-Planck-Institut for Nuclear Physics, P.O. Box 103980, 69029 Heidelberg, Germany
| | - Axikegu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y X Bai
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y W Bao
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - D Bastieri
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - X J Bi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y J Bi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W Bian
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - A V Bukevich
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - Q Cao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - W Y Cao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Zhe Cao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - J Chang
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J F Chang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - A M Chen
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - E S Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H X Chen
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - Liang Chen
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - Lin Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Long Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M J Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M L Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Q H Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - S Chen
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - S H Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - S Z Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - T L Chen
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - Y Chen
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - N Cheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y D Cheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Cui
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S W Cui
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - X H Cui
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - Y D Cui
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B Z Dai
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - H L Dai
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Z G Dai
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Danzengluobu
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - X Q Dong
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - K K Duan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J H Fan
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y Z Fan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Fang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - J H Fang
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - K Fang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C F Feng
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - H Feng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
| | - L Feng
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S H Feng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X T Feng
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - Y Feng
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - Y L Feng
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - S Gabici
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - B Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C D Gao
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - Q Gao
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - W Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W K Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M M Ge
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - L S Geng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - G Giacinti
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - G H Gong
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Q B Gou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M H Gu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - F L Guo
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - X L Guo
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y Q Guo
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Y Guo
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y A Han
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - M Hasan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H H He
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H N He
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Y He
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y He
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y K Hor
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B W Hou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C Hou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X Hou
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - H B Hu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Q Hu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S C Hu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- China Center of Advanced Science and Technology, Beijing 100190, China
| | - D H Huang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - T Q Huang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W J Huang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - X T Huang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - X Y Huang
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y Huang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X L Ji
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - H Y Jia
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - K Jia
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - K Jiang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - X W Jiang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z J Jiang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - M Jin
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M M Kang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - I Karpikov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - D Kuleshov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - K Kurinov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - B B Li
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - C M Li
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Cheng Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Cong Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - H B Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H C Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Jian Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Jie Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - K Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - S D Li
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - W L Li
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - W L Li
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - X R Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Xin Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Y Z Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Zhe Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Zhuo Li
- School of Physics, Peking University, 100871 Beijing, China
| | - E W Liang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - Y F Liang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - S J Lin
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B Liu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - C Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Liu
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - D B Liu
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H Liu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H D Liu
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - J Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J L Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Liu
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - R Y Liu
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - S M Liu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - W Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Liu
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y N Liu
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Q Luo
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - Y Luo
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H K Lv
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Q Ma
- School of Physics, Peking University, 100871 Beijing, China
| | - L L Ma
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X H Ma
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J R Mao
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Z Min
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W Mitthumsiri
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - H J Mu
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - Y C Nan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - A Neronov
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - L J Ou
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - P Pattarakijwanich
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Z Y Pei
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - J C Qi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Qi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Q Qiao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J J Qin
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - A Raza
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Ruffolo
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - A Sáiz
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - M Saeed
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Semikoz
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - L Shao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - O Shchegolev
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - X D Sheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F W Shu
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - H C Song
- School of Physics, Peking University, 100871 Beijing, China
| | - Yu V Stenkin
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - V Stepanov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - Y Su
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - D X Sun
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Q N Sun
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - X N Sun
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - Z B Sun
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - J Takata
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - P H T Tam
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - Q W Tang
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - R Tang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Z B Tang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - W W Tian
- University of Chinese Academy of Sciences, 100049 Beijing, China
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - C Wang
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - C B Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - G W Wang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - H G Wang
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - H H Wang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - J C Wang
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Kai Wang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Kai Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - L P Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Y Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - P H Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - R Wang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - W Wang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - X G Wang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - X Y Wang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Y Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y D Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y J Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z H Wang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - Z X Wang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - Zhen Wang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Zheng Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - D M Wei
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J J Wei
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y J Wei
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - T Wen
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - C Y Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H R Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Q W Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - S Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X F Wu
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y S Wu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - S Q Xi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J Xia
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - G M Xiang
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - D X Xiao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - G Xiao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y L Xin
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y Xing
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - D R Xiong
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Z Xiong
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D L Xu
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - R F Xu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - R X Xu
- School of Physics, Peking University, 100871 Beijing, China
| | - W L Xu
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - L Xue
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - D H Yan
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - J Z Yan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - T Yan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C W Yang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - C Y Yang
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - F Yang
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - F F Yang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - L L Yang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - M J Yang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - R Z Yang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - W X Yang
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y H Yao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z G Yao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Q Yin
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - N Yin
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - X H You
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z Y You
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y H Yu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Q Yuan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - H Yue
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H D Zeng
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - T X Zeng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - W Zeng
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - M Zha
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B B Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - F Zhang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H Zhang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H M Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - H Y Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J L Zhang
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - Li Zhang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - P F Zhang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - P P Zhang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - R Zhang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S B Zhang
- University of Chinese Academy of Sciences, 100049 Beijing, China
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - S R Zhang
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - S S Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - X P Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y F Zhang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Yi Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Yong Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Zhao
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - J Zhao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Zhao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - L Z Zhao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - S P Zhao
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - X H Zhao
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - F Zheng
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - W J Zhong
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - B Zhou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H Zhou
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - J N Zhou
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - M Zhou
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - P Zhou
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - R Zhou
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - X X Zhou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X X Zhou
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - B Y Zhu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - C G Zhu
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - F R Zhu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H Zhu
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - K J Zhu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Y C Zou
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - X Zuo
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
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Yuan T, Wu L, Li S, Zheng J, Li N, Xiao X, Zhang H, Fei T, Xie L, Zuo Z, Li D, Huang P, Feng H, Cao Y, Yan N, Wei X, Shi L, Sun Y, Wei W, Sun Y, Zuo E. Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes. Cell Discov 2024; 10:20. [PMID: 38378648 PMCID: PMC10879117 DOI: 10.1038/s41421-023-00624-1] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 11/09/2023] [Indexed: 02/22/2024] Open
Abstract
Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.
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Affiliation(s)
- Tanglong Yuan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Leilei Wu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Shiyan Li
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jitan Zheng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, Guangxi, China
| | - Nana Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Xiao Xiao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Haihang Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Tianyi Fei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Long Xie
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Zhenrui Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Di Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, Guangxi, China
| | | | - Hu Feng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Yaqi Cao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Nana Yan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Xinming Wei
- Epigenic Therapeutics, Inc., Shanghai, China
| | - Lei Shi
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Yongsen Sun
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Wu Wei
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- Lingang Laboratory, Shanghai, China.
| | - Yidi Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
| | - Erwei Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China.
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11
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Xie L, Feng H, Li Z, Li D, Yang X, Yuan T, Yan N, He C, Zheng J, Zuo Z, Zheng Y, Cao Y, Lu Y, Xiong XY, Zuo E. Undetectable off-target effects induced by FokI catalytic domain in mouse embryos. Genome Biol 2024; 25:51. [PMID: 38378658 PMCID: PMC10877887 DOI: 10.1186/s13059-024-03188-9] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
The FokI catalytic domain can be fused to various DNA binding architectures to improve the precision of genome editing tools. However, evaluation of off-target effects is essential for developing these tools. We use Genome-wide Off-target analysis by Two-cell embryo Injection (GOTI) to detect low-frequency off-target editing events in mouse embryos injected with FokI-based architectures. Specifically, we test FokI-heterodimers fused with TALENs, FokI homodimers fused with RYdCas9, or FokI catalytic domains alone resulting in no significant off-target effects. These FokI genome editing systems exhibit undetectable off-target effects in mouse embryos, supporting the further development of these systems for clinical applications.
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Affiliation(s)
- Long Xie
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hu Feng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhifang Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Di Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Xiali Yang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Tanglong Yuan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Nana Yan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Chenfei He
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jitan Zheng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Zhenrui Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yaxuan Zheng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- College of Animal Sciences and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yaqi Cao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yangqing Lu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, China.
| | - Xing Yao Xiong
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - Erwei Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
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12
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Liu MY, Zhu L, Yang Y, Ma YL, Feng H. [Research progress in clinical diagnosis and treatment of osteosarcoma of the jaw]. Zhonghua Kou Qiang Yi Xue Za Zhi 2024; 59:197-203. [PMID: 38280741 DOI: 10.3760/cma.j.cn112144-20230719-00025] [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: 01/29/2024]
Abstract
Osteosarcoma of the jaw (JOS), is a relatively rare type of osteosarcoma, with a unique pathogenesis and pathological manifestations. The clinical manifestation of JOS is not characteristic, and it often needs to be diagnosed by combining radiological and pathological examination. At present, the conventional treatment of JOS is a comprehensive treatment based on surgery and supplemented by radiotherapy and chemotherapy. Recently, the emergence of new therapies such as immunotherapy, gene therapy, phototherapy and traditional Chinese medicine has provided more choices for treatment and brought new hope to patients with JOS. Therefore, this article summarized the current understanding of diagnosis and the latest treatment development of JOS.
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Affiliation(s)
- M Y Liu
- Department of Oral Mucosa, Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University & Hunan Clinical Research Center of Oral Major Diseases and Oral Health,Changsha, 410008, China
| | - L Zhu
- Department of Oral Mucosa, Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University & Hunan Clinical Research Center of Oral Major Diseases and Oral Health,Changsha, 410008, China
| | - Y Yang
- Department of Oral Mucosa, Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University & Hunan Clinical Research Center of Oral Major Diseases and Oral Health,Changsha, 410008, China
| | - Y L Ma
- Department of Oral Mucosa, Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University & Hunan Clinical Research Center of Oral Major Diseases and Oral Health,Changsha, 410008, China
| | - H Feng
- Department of Oral Mucosa, Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University & Hunan Clinical Research Center of Oral Major Diseases and Oral Health,Changsha, 410008, China
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13
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Ding L, Wu S, Hou Z, Li A, Xu Y, Feng H, Pan W, Ruan J. Improving error-correcting capability in DNA digital storage via soft-decision decoding. Natl Sci Rev 2024; 11:nwad229. [PMID: 38213525 PMCID: PMC10776348 DOI: 10.1093/nsr/nwad229] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 08/03/2023] [Accepted: 08/15/2023] [Indexed: 01/13/2024] Open
Abstract
Error-correcting codes (ECCs) employed in the state-of-the-art DNA digital storage (DDS) systems suffer from a trade-off between error-correcting capability and the proportion of redundancy. To address this issue, in this study, we introduce soft-decision decoding approach into DDS by proposing a DNA-specific error prediction model and a series of novel strategies. We demonstrate the effectiveness of our approach through a proof-of-concept DDS system based on Reed-Solomon (RS) code, named as Derrick. Derrick shows significant improvement in error-correcting capability without involving additional redundancy in both in vitro and in silico experiments, using various sequencing technologies such as Illumina, PacBio and Oxford Nanopore Technology (ONT). Notably, in vitro experiments using ONT sequencing at a depth of 7× reveal that Derrick, compared with the traditional hard-decision decoding strategy, doubles the error-correcting capability of RS code, decreases the proportion of matrices with decoding-failure by 229-fold, and amplifies the potential maximum storage volume by impressive 32 388-fold. Also, Derrick surpasses 'state-of-the-art' DDS systems by comprehensively considering the information density and the minimum sequencing depth required for complete information recovery. Crucially, the soft-decision decoding strategy and key steps of Derrick are generalizable to other ECCs' decoding algorithms.
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Affiliation(s)
- Lulu Ding
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen518120, China
| | - Shigang Wu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen518120, China
| | - Zhihao Hou
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen518120, China
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou510642, China
| | - Alun Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen518120, China
| | - Yaping Xu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen518120, China
| | - Hu Feng
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen518120, China
| | - Weihua Pan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen518120, China
| | - Jue Ruan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen518120, China
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14
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Hu P, Cao Q, Feng H, Liu Y, Chen Y, Xu J, Feng W, Sun H, Ding H, Wang C, Gao J, Xiao M. MicroRNA-451a is a candidate biomarker and therapeutic target for major depressive disorder. Gen Psychiatr 2024; 37:e101291. [PMID: 38304710 PMCID: PMC10831421 DOI: 10.1136/gpsych-2023-101291] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 12/13/2023] [Indexed: 02/03/2024] Open
Abstract
Background Increasing evidence supports the role of microRNAs (miRNAs) in major depressive disorder (MDD), but the pathophysiological mechanism remains elusive. Aims To explore the mechanism of microRNA-451a (miR-451a) in the pathology and behaviours of depression. Methods Abnormal miRNAs such as miR-451a reported previously in the serum of patients with MDD were screened and then confirmed in a mouse model of depression induced by chronic restraint stress (CRS). Eight-week-old male C57BL/6 mice had miR-451a overexpression in the medial prefrontal cortex (mPFC) via adeno-associated virus serotype 9 vectors encoding a pri-mmu-miR-451a-GFP fusion protein followed by behavioural and pathological analyses. Finally, molecular biological experiments were conducted to investigate the potential mechanism of miR-451a against depression. Results The serum levels of miRNA-451a were significantly lower in patients with MDD, with a negative correlation with the Hamilton Depression Scale scores. Additionally, a negative association between serum miR-451a and behavioural despair or anhedonia was observed in CRS mice. Notably, miR-451a expression was significantly downregulated in the mPFC of CRS-susceptible mice. Overexpressing miR-451a in the mPFC reversed the loss of dendritic spines and the depression-like phenotype of CRS mice. Mechanistically, miR-451a could inhibit CRS-induced corticotropin-releasing factor receptor 1 expression via targeting transcription factor 2, subsequently protecting dendritic spine plasticity. Conclusions Together, these results highlighted miR-451a as a candidate biomarker and therapeutic target for MDD.
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Affiliation(s)
- Panpan Hu
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qiuchen Cao
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hu Feng
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yun Liu
- Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yan Chen
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jingfan Xu
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Weixi Feng
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Huaiqing Sun
- Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Huachen Ding
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chun Wang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Junying Gao
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ming Xiao
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, Jiangsu, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China
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15
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Zhong L, Yang F, Sun S, Wang L, Yu H, Nie X, Liu A, Xu N, Zhang L, Zhang M, Qi Y, Ji H, Liu G, Zhao H, Jiang Y, Li J, Song C, Yu X, Yang L, Yu J, Feng H, Guo X, Yang F, Xue F. Predicting lung cancer survival prognosis based on the conditional survival bayesian network. BMC Med Res Methodol 2024; 24:16. [PMID: 38254038 PMCID: PMC10801949 DOI: 10.1186/s12874-023-02043-y] [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: 07/05/2022] [Accepted: 09/25/2023] [Indexed: 01/24/2024] Open
Abstract
Lung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.
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Affiliation(s)
- Lu Zhong
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Hainan Center for Disease Control and Prevention, Institute for Prevention and Control of Tropical Diseases and Chronic Noninfectious Diseases, Haikou, Hainan, China.
| | - Fan Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Institute for Medical Dataology, Shandong University, Jinan, China.
| | - Shanshan Sun
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Lijie Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Shandong University, Jinan, China
| | - Hong Yu
- Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xiushan Nie
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Ailing Liu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Ning Xu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Lanfang Zhang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Mingjuan Zhang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Yue Qi
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Huaijun Ji
- Department of Thoracic Surgery, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Guiyuan Liu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Huan Zhao
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
- The Second School of Clinical Medicine of Binzhou Medical University, Yantai, China
| | - Yinan Jiang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Jingyi Li
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Chengcun Song
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Xin Yu
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Liu Yang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Jinchao Yu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Hu Feng
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Xiaolei Guo
- The Department for Chronic and Non-communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Fujun Yang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China.
| | - Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Institute for Medical Dataology, Shandong University, Jinan, China.
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16
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Ma SR, Feng H, Zhao GF, Bai HJ, Zhao L, Zhao ZR. [Nomogram prediction model of cervical anastomotic leakage after esophageal cancer surgery]. Zhonghua Zhong Liu Za Zhi 2023; 45:1065-1076. [PMID: 38110315 DOI: 10.3760/cma.j.cn112152-20201127-01026] [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] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Objective: To retrospectively analyze the risk factors of anastomotic leakage in the neck after esophageal cancer and establish a nomogram prediction model that can accurately predict the occurrence of anastomotic leakage in the neck of the patient. Methods: The study retrospectively analyzed 702 patients who underwent radical esophageal cancer surgery between January 2010 and May 2015 at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. A multivariate logistic regression model was used to determine the risk factors for neck anastomotic leak, and a nomogram model was constructed, internal validation methods were used to evaluate and verify the predictive effectiveness of the nomogram. Results: There were 702 patients in the whole group, 492 in the training group and 210 in the validation group. The incidence of postoperative cervical anastomotic leak was 16.1% (79/492) in 492 patients with esophageal cancer in the training group. Multifactorial analysis revealed calcification of the descending aorta (OR=2.12, 95% CI: 1.14, 3.94, P=0.018), calcification of the celiac artery (OR=2.29, 95% CI: 1.13, 4.64, P=0.022), peripheral vascular disease (OR=5.50, 95% CI: 1.64, 18.40, P=0.006), postoperative ventilator-assisted breathing (OR=5.33, 95% CI: 1.83, 15.56, P=0.002), pleural effusion or septic chest (OR=3.08, 95% CI: 1.11, 8.55, P=0.031), incisional fat liquefaction and infection (OR=3.49, 95% CI: 1.68, 7.27, P=0.001) were independent risk factors for the development of cervical anastomotic leak after esophageal cancer surgery. The results of the nomogram prediction model showed that the consistency indices of the training and external validation groups were 0.73 and 0.74, respectively (P<0.001), suggesting that the prediction model has good predictive efficacy. Conclusion: The nomogram prediction model can intuitively predict the incidence of postoperative cervical anastomotic leakage in patients with high prediction accuracy, which can help provide a clinical basis for preventing cervical anastomotic leak and individualized treatment of patients.
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Affiliation(s)
- S R Ma
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - H Feng
- Administration Office of Science and Technology Projects, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - G F Zhao
- Department of Thoracic Surgery, Zhongshan Hospital of Fudan University, Shanghai 200433, China
| | - H J Bai
- Administration Office of Science and Technology Projects, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - L Zhao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Z R Zhao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
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17
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Wang L, Qi Y, Liu A, Guo X, Sun S, Zhang L, Ji H, Liu G, Zhao H, Jiang Y, Li J, Song C, Yu X, Yang L, Yu J, Feng H, Yang F, Xue F. Opportunistic Screening With Low-Dose Computed Tomography and Lung Cancer Mortality in China. JAMA Netw Open 2023; 6:e2347176. [PMID: 38085543 PMCID: PMC10716726 DOI: 10.1001/jamanetworkopen.2023.47176] [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] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/26/2023] [Indexed: 12/18/2023] Open
Abstract
Importance Despite the recommendations of lung cancer screening guidelines and the evidence supporting the effectiveness of population-based lung screening, a common barrier to effective lung cancer screening is that the participation rates of low-dose computed tomography (LDCT) screening among individuals with the highest risk are not large. There are limited data from clinical practice regarding whether opportunistic LDCT screening is associated with reduced lung-cancer mortality. Objective To evaluate whether opportunistic LDCT screening is associated with improved prognosis among adults with lung cancer in mainland China. Design, Setting, and Participants This cohort study included patients diagnosed with lung cancer at Weihai Municipal Hospital Healthcare Group, Weihai City, China, from 2016 to 2021. Data were analyzed from January 2022 to February 2023. Exposures Data collected included demographic indicators, tumor characteristics, comorbidities, blood indexes, and treatment information. Patients were classified into screened and nonscreened groups on the basis of whether or not their lung cancer diagnosis occurred through opportunistic screening. Main Outcomes and Measures Follow-up outcome indicators included lung cancer-specific mortality and all-cause mortality. Propensity score matching (PSM) was adopted to account for potential imbalanced factors between groups. The associations between LDCT screening and outcomes were analyzed using Cox regression models based on the matched data. Propensity score regression adjustment and inverse probability treatment weighting were used for sensitivity analysis. Results A total of 5234 patients (mean [SD] baseline age, 61.8 [9.8] years; 2518 [48.1%] female) with complete opportunistic screening information were included in the analytical sample, with 2251 patients (42.91%) receiving their lung cancer diagnosis through opportunistic screening. After 1:1 PSM, 2788 patients (1394 in each group) were finally included. The baseline characteristics of the matched patients were balanced between groups. Opportunistic screening with LDCT was associated with a 49% lower risk of lung cancer death (HR, 0.51; 95% CI, 0.42-0.62) and 46% lower risk of all-cause death (HR, 0.54; 95% CI, 0.45-0.64). Conclusions and Relevance In this cohort study of patients with lung cancer, opportunistic lung cancer screening with LDCT was associated with lower lung cancer mortality and all-cause mortality. These findings suggest that opportunistic screening is an important supplement to population screening to improve prognosis of adults with lung cancer.
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Affiliation(s)
- Lijie Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Healthcare Big Data Research Institute, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yue Qi
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Ailing Liu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Xiaolei Guo
- Department for Chronic and Non-Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Shanshan Sun
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Lanfang Zhang
- Department of Chemotherapy, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Huaijun Ji
- Department of Thoracic Surgery, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Guiyuan Liu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Huan Zhao
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Yinan Jiang
- Department of Radiotherapy, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Jingyi Li
- Department of Radiotherapy, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Chengcun Song
- Department of Chemotherapy, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Xin Yu
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Liu Yang
- Department of Chemotherapy, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Jinchao Yu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Hu Feng
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Fujun Yang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Healthcare Big Data Research Institute, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
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Liu W, Zhang R, Feng H, Luo J, Zhu H. Increased expression of Nav1.6 of reactive astrocytes in the globus pallidus is closely associated with motor deficits in a model of Parkinson's disease. Glia 2023; 71:2850-2865. [PMID: 37572007 DOI: 10.1002/glia.24455] [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: 12/28/2021] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease in elderly people, which is characterized by motor disabilities in PD patients. Nav1.6 is the most abundant subtype of voltage-gated sodium channels (VGSCs) in the brain of adult mammals and rodents. Here we investigated the role of Nav1.6 in the external globus pallidus (GP) involved in the pathogenesis of motor deficits in unilateral 6-OHDA(6-hydroxydopamine)lesioned rats. The results show that Nav1.6 is dramatically increased in reactive astrocytes of the ipsilateral GP in the middle stage, but not different from the control rats in the later stage of the pathological process in 6-OHDA lesioned rats. Furthermore, the down-regulation of Nav1.6 expression in the ipsilateral GP can significantly improve motor deficits in 6-OHDA lesioned rats in the middle stage of the pathological process. The electrophysiological experiments show that the down-regulation of Nav1.6 expression in the ipsilateral GP significantly decreases the abnormal high synchronization between the ipsilateral M1 (the primary motor cortex) and GP in 6-OHDA lesioned rats. Ca2+ imaging reveals that the down-regulation of Nav1.6 expression reduces the intracellular concentration of Ca2+ ([Ca2+ ]i) in primary cultured astrocytes. These findings suggest that the increased Nav1.6 expression of reactive astrocytes in the GP play an important role in the pathogenesis of motor dysfunction in the middle stage in 6-OHDA lesioned rats, which may participate in astrocyte-neuron communication by regulating [Ca2+ ]i of astrocytes, thereby contributing to the formation of abnormal electrical signals of the basal ganglia (BG) in 6-OHDA lesioned rats.
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Affiliation(s)
- Weitang Liu
- School of Life Science, Shanghai University, Shanghai, China
| | - Renxing Zhang
- School of Life Science, Shanghai University, Shanghai, China
| | - Hu Feng
- School of Life Science, Shanghai University, Shanghai, China
| | - Jiamin Luo
- School of Life Science, Shanghai University, Shanghai, China
| | - Hongyan Zhu
- School of Life Science, Shanghai University, Shanghai, China
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Feng H, Yu QS, Wang JX, Yuan YY, Rao WL, Liang X, Yu SS, Wei FS. [Establishment and validation of nomogram prediction model for complicated acute appendicitis]. Zhonghua Wai Ke Za Zhi 2023; 61:1074-1079. [PMID: 37932143 DOI: 10.3760/cma.j.cn112139-20230104-00005] [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: 11/08/2023]
Abstract
Objective: To establish and internally validate a nomogram model for predicting complicated acute appendicitis (CA). Methods: The clinical data from 663 acute appendicitis patients from the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from October 2015 to October 2022 were retrospectively analyzed. There were 411 males and 252 females, aged (M (IQR)) 41 (22) years (range: 18 to 84 years). There were 516 cases of CA and 147 cases of uncomplicated acute appendicitis. The minimum absolute contraction and selection operator regression model was used to screen the potential relative factors of CA, and the screened factors were included in the Logistic regression model for multivariate analysis. Software R was used to establish a preoperative CA nomogram prediction model, the receiver operating characteristic curve of the model was drawn, and the value of area under the curve (AUC) was compared to evaluate its identification ability, and the Bootstrap method was used for internal verification. Results: The elderly (age≥60 years) (OR=2.428, 95%CI: 1.295 to 4.549), abdominal pain time (every rise of 1 hour) (OR=1.089, 95%CI: 1.072 to 1.107), high fever (body temperature≥39 ℃) (OR=1.122, 95%CI: 1.078 to 1.168), total bilirubin (every rise of 1 μmol/L) (OR=2.629, 95%CI: 1.227 to 5.635) were independent relative factors of CA (all P<0.05). The AUC of this model was 0.935 (95%CI: 0.915 to 0.956). After internal verification using the Bootstrap method, the model still had a high discrimination ability (AUC=0.933), and the predicted CA curve was still in good agreement with the actual clinical CA curve. Conclusion: The clinical prediction model based on the elderly (age≥60 years), prolonged abdominal pain time, high fever (body temperature≥39 ℃), and increased total bilirubin can help clinicians effectively identify CA.
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Affiliation(s)
- H Feng
- Depertment of Emergency Surgery, the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Institute of Surgery, Anhui Academy of Traditional Chinese Medicine, Hefei 230031, China
| | - Q S Yu
- Depertment of General Surgery, the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Institute of Surgery, Anhui Academy of Traditional Chinese Medicine, Hefei 230031, China
| | - J X Wang
- Depertment of Emergency Surgery, the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Institute of Surgery, Anhui Academy of Traditional Chinese Medicine, Hefei 230031, China
| | - Y Y Yuan
- Depertment of Emergency Surgery, the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Institute of Surgery, Anhui Academy of Traditional Chinese Medicine, Hefei 230031, China
| | - W L Rao
- Depertment of Emergency Surgery, the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Institute of Surgery, Anhui Academy of Traditional Chinese Medicine, Hefei 230031, China
| | - X Liang
- Depertment of Emergency Surgery, the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Institute of Surgery, Anhui Academy of Traditional Chinese Medicine, Hefei 230031, China
| | - S S Yu
- Depertment of Emergency Surgery, the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Institute of Surgery, Anhui Academy of Traditional Chinese Medicine, Hefei 230031, China
| | - F S Wei
- Depertment of Emergency Surgery, the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Institute of Surgery, Anhui Academy of Traditional Chinese Medicine, Hefei 230031, China
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Feng H, Zhou ZY, Dai YK. Algorithm for Predicting Respiratory Motion of Liver Tissue Based on Short-Term Respiratory Monitoring. Stud Health Technol Inform 2023; 308:549-555. [PMID: 38007782 DOI: 10.3233/shti230883] [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] [Indexed: 11/28/2023]
Abstract
In this study, an algorithm for predicting respiratory motion of liver tissue based on the combination of subject-specific external surrogate signals and 2D ultrasound image sequences was investigated to achieve better respiratory monitoring in clinical procedures. To achieve non-invasiveness in clinical procedures, an EM position tracker and a Doppler ultrasound diagnostic system were used as data collectors. Firstly, the mapping relationship between the magnetic sensing surrogate signal and the internal motion of liver tissue was learned by the Ridge regression model to achieve the estimation of the internal motion of liver tissue by the magnetic sensing surrogate signal; then the motion prediction of the estimated internal motion of liver tissue was performed by the artificial neural network (ANN) as the prediction filter; finally, the prediction of the respiratory motion of liver tissue by the magnetic sensing surrogate signal was achieved. Through experimental tests on 16 subject volunteers, the experimental results show that the RMSE of the proposed algorithm for predicting the respiratory motion of liver tissue is 2mm, indicating the potential of this prediction algorithm to achieve the localization of the internal motion position of liver tissue by the human magnetic sensing surrogate signal.
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Affiliation(s)
- Hu Feng
- College of Energy and Electrical Engineering, Hohai University, Nanjing Jiangsu, China
| | - Zhi Yong Zhou
- Suzhou Institute of Bioengineering Technology, Chinese Academy of Sciences, Suzhou Jiangsu, China
| | - Ya Kang Dai
- Suzhou Institute of Bioengineering Technology, Chinese Academy of Sciences, Suzhou Jiangsu, China
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Feng H, Lei X, Yu G, Changchun Z. Spatio-temporal evolution and trend prediction of urban ecosystem service value based on CLUE-S and GM (1,1) compound model. Environ Monit Assess 2023; 195:1282. [PMID: 37812253 PMCID: PMC10562314 DOI: 10.1007/s10661-023-11853-y] [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: 10/16/2022] [Accepted: 09/06/2023] [Indexed: 10/10/2023]
Abstract
Ecosystem service value (ESV) is a significant indicator related to regional ecological well-being. Evaluating ESV premised on continuous time series land benefit data can provide an accurate reference for regional ecological civilization construction and sustainable development. Taking Shijiazhuang, the capital city of Hebei Province as an example, the study analyzed land use changes based on the land use data of the continuous time series from 2000 to 2020 and introduced a socio-economic adjustment factor and biomass factor adjustment factor to construct a dynamic assessment model of ecosystem service value. The spatiotemporal changes of the ecosystem service value in Shijiazhuang City were evaluated, and the dynamic prediction of the ecosystem service value was made using the CLUE-S model and the GM (1,1) model. (1) The changes in the overall ESV and spatial pattern in Shijiazhuang are strongly linked to the change in land use, and the contribution of cultivated land, woodland, and grassland to ecosystem service value exceeds 90%. (2) Between 2000 and 2020, the value of ecosystem services illustrated a dynamic change and gradually declined, with the total amount falling from 28.003 to 19.513 billion yuan. Among individual ecosystem services, the value of regulation services suffered the most serious loss. (3) CLUE-S and GM (1,1) perform well in the prediction of ESV. The prediction outcomes illustrate that the ecosystem service value of Shijiazhuang will continue to decline by 2025, and the ecosystem value will drop to 16.771 billion yuan. This research may offer a reference for the dynamic assessment of ESV of the continuous sequence and help to promote regional ecological protection and sustainable development.
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Affiliation(s)
- Hu Feng
- Department of Land and Resources, Hebei Agricultural University, Baoding, 071000, Heibei, China
| | - Xu Lei
- Department of Land and Resources, Hebei Agricultural University, Baoding, 071000, Heibei, China
| | - Guo Yu
- Department of Land and Resources, Hebei Agricultural University, Baoding, 071000, Heibei, China
| | - Zhang Changchun
- Department of Land and Resources, Hebei Agricultural University, Baoding, 071000, Heibei, China.
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Bei W, Qian J, Zilu Q, Kai C, Ruili J, Feng H, Liuqing C. Comparing four immunosuppressive agents for chronic spontaneous urticaria-A network meta-analysis. Int Immunopharmacol 2023; 123:110577. [PMID: 37567010 DOI: 10.1016/j.intimp.2023.110577] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 04/16/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 08/13/2023]
Abstract
BACKGROUND Immunosuppression is an integral part of treating chronic spontaneous urticaria (CSU), but there is no literature to evaluate the efficacy of multiple immunosuppressive agents. OBJECTIVE The comparison of the efficacy, safety, and incidence of adverse effects of four immunosuppressive medicines (tripterygium glycosides, methotrexate, cyclosporine A, and azathioprine) in combination with antihistamines in treating CSU provides a clinical reference and evidence-based medicine for treating CSU. METHODS PUBMED, The Cochrane Library, EMBASE, WANFANG, CNKI, CBM, and clinical trial registration platform were searched to collect relevant randomized controlled trials (RCT) and cohort studies of four immunosuppressive medicines combined with antihistamines for treating CSU. The primary outcomes were the efficacy of weekly urticaria activity score 7 (UAS7) and adverse effects. RESULTS This study pooled data from seven randomized clinical trials with 410 participants. The standardized mean differences for change in UAS7 were 0.10 (95% confidence interval (CI), 0.01 to 0.68) for cyclosporine A plus antihistamine; 0.03 (95% CI, 0.00 to 0.23) for azathioprine plus antihistamine; 0.52 (95% CI, 0.32 to 0.85) for tripterygium glycosides plus antihistamine; and 1.54 (95% CI, 0.64 to 3.67) for methotrexate plus antihistamine. There were no significant differences in side effects between these medicines in the limited number of trials and clinical samples. CONCLUSION Our results indicate that cyclosporine A combined with antihistamine resulted in greater improvements regarding the UAS7 in CSU patients and that tripterygium glycosides are also effective in treating CSU.
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Affiliation(s)
- Wang Bei
- Clinical College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei 430000, China; Department of Dermatology, Wuhan No. 1 Hospital, Hospital of Traditional Chinese and Western Medicine Affiliated to Hubei University of Chinese Medicine, Wuhan Hospital of Traditional Chinese and Western Medicine Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430000, China; Hubei Province & Key Laboratory of Skin Infection and Immunity, Wuhan, Hubei 430000, China
| | - Jiang Qian
- Department of Dermatology, Wuhan No. 1 Hospital, Hospital of Traditional Chinese and Western Medicine Affiliated to Hubei University of Chinese Medicine, Wuhan Hospital of Traditional Chinese and Western Medicine Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430000, China; Hubei Province & Key Laboratory of Skin Infection and Immunity, Wuhan, Hubei 430000, China
| | - Qu Zilu
- Department of Dermatology, Wuhan No. 1 Hospital, Hospital of Traditional Chinese and Western Medicine Affiliated to Hubei University of Chinese Medicine, Wuhan Hospital of Traditional Chinese and Western Medicine Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430000, China; Hubei Province & Key Laboratory of Skin Infection and Immunity, Wuhan, Hubei 430000, China
| | - Chen Kai
- Department of Dermatology, Wuhan No. 1 Hospital, Hospital of Traditional Chinese and Western Medicine Affiliated to Hubei University of Chinese Medicine, Wuhan Hospital of Traditional Chinese and Western Medicine Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430000, China; Hubei Province & Key Laboratory of Skin Infection and Immunity, Wuhan, Hubei 430000, China
| | - Jiang Ruili
- Department of Dermatology, Wuhan No. 1 Hospital, Hospital of Traditional Chinese and Western Medicine Affiliated to Hubei University of Chinese Medicine, Wuhan Hospital of Traditional Chinese and Western Medicine Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430000, China; Hubei Province & Key Laboratory of Skin Infection and Immunity, Wuhan, Hubei 430000, China
| | - Hu Feng
- Department of Dermatology, Wuhan No. 1 Hospital, Hospital of Traditional Chinese and Western Medicine Affiliated to Hubei University of Chinese Medicine, Wuhan Hospital of Traditional Chinese and Western Medicine Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430000, China; Hubei Province & Key Laboratory of Skin Infection and Immunity, Wuhan, Hubei 430000, China.
| | - Chen Liuqing
- Clinical College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei 430000, China; Department of Dermatology, Wuhan No. 1 Hospital, Hospital of Traditional Chinese and Western Medicine Affiliated to Hubei University of Chinese Medicine, Wuhan Hospital of Traditional Chinese and Western Medicine Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430000, China; Hubei Province & Key Laboratory of Skin Infection and Immunity, Wuhan, Hubei 430000, China.
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Peng J, Zhang L, Wang L, Feng H, Yao D, Meng R, Liu X, Li X, Liu N, Tan B, Huang Z, Li S, Meng X. PD-L1 Inhibitors Combined with Thoracic Radiotherapy in First-Line Treatment of Extensive Stage Small Cell Lung Cancer: A Propensity Score-Matched, Real-World Study. Int J Radiat Oncol Biol Phys 2023; 117:S127-S128. [PMID: 37784327 DOI: 10.1016/j.ijrobp.2023.06.472] [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 CREST study showed that the addition of thoracic radiotherapy (TRT) could improve the survival of extensive stage small cell lung cancer (ES-SCLC), but whether TRT can bring survival benefit in the era of immunotherapy is controversial. This study aims to explore the efficacy and safety of adding TRT to the combination of PD-L1 inhibitors and chemotherapy. MATERIALS/METHODS Thepatients who received PD-L1 inhibitors combined with platinum-based chemotherapy as the first-line treatment of ES-SCLC from January 2019 to December 2021 were retrospectively collected. According to whether they received TRT, they were divided into two groups, and the follow-up analysis was performed. Propensity score matching (PSM) in with a 1:1 ratio was performed to balance the baseline characteristics of the two cohorts. The endpoints were progression-free survival (PFS) and OS. RESULTS A total of 211 patients with ES-SCLC were enrolled, of whom 70 (33.2%) patients received standard therapy plus TRT as first-line treatment, and 141 (66.8%) patients in the control group received PD-L1 inhibitors plus chemotherapy. After PSM, a total of 65 pairs of patients were enrolled in the analysis. There were no significant differences in baseline characteristics between the two groups of patients who received TRT and those who did not. In all patients, the median PFS (mPFS) in the TRT group and the non-TRT groupwere 9.5 months and 7.2 months, respectively, with HR = 0.60 (95% CI 0.41-0.87, p = 0.007). The median OS (mOS) in the TRT group was also significantly longer than that in the non-TRT group (24.1 months vs. 18.5 months, HR = 0.53, 95% CI 0.32-0.85, p = 0.009). Multivariable analysis showed that baseline liver metastasis and bone metastasis were independent prognostic factors for OS. In terms of safety, immunotherapy combined with thoracic radiotherapy increased the incidence of treatment-related pneumonia (p<0.001), most of which were grade 1-2. CONCLUSION This real-world study shows that adding TRT to durvalumab or atezolizumab plus chemotherapy significantly improves survival in ES-SCLC. It leads to more treatment-related pneumonia, but most of them can be relieved after symptomatic treatment. This treatment model deserves to be explored in prospective clinical trials.
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Affiliation(s)
- J Peng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - L Zhang
- Department of Thoracic Department, Hunan Cancer Hospital, Changsha, China
| | - L Wang
- Department of Medical Oncology, Baotou Cancer Hospital, Baotou, China
| | - H Feng
- Department of Clinical Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - D Yao
- Department of Medical Oncology, Chaoyang Second Hospital, Chaoyang, China
| | - R Meng
- Department of Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - X Liu
- Department of Oncology Department, Jinzhou Medical University, Jinzhou, China, Jinzhou, China
| | - X Li
- Department of Respiratory and Critical Care, Chifeng Municipal Hospital, Chifeng, China
| | - N Liu
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin, China
| | - B Tan
- QILU HOSPITAL OF SHANDONG UNIVERSITY, Jinan, China
| | - Z Huang
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - S Li
- Department of Oncology, Zibo Municipal Hospital, Zibo, China
| | - X Meng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
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Feng H, Liu H, Wang Q, Song M, Yang T, Zheng L, Wu D, Shao X, Shi G. Breast cancer diagnosis and prognosis using a high b-value non-Gaussian continuous-time random-walk model. Clin Radiol 2023:S0009-9260(23)00227-1. [PMID: 37344324 DOI: 10.1016/j.crad.2023.05.016] [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: 11/18/2022] [Revised: 05/11/2023] [Accepted: 05/19/2023] [Indexed: 06/23/2023]
Abstract
AIM To compare the diagnostic performance of mono-exponential model-derived apparent diffusion coefficient (ADC), continuous-time random-walk (CTRW) model-derived Dm, α, β and their combinations in discriminating malignancy of breast lesions, and investigate the association between model-derived parameters and prognosis-related immunohistochemical indices. MATERIALS AND METHODS A total of 85 patients with breast lesions (51 malignant, 34 benign) were analysed in this retrospective study. Clinical characteristics include oestrogen receptor (ER), progesterone receptor (PR), human epidermal receptor 2 (HER2), and Ki-67. The ADC was fitted using a mono-exponential model (b-values = 0, 800 s/mm2), while Dm, α, and β were fitted using a CTRW model. Independent Student's t-test and the Mann-Whitney U-test were used for the comparison of parameters. Discrimination performance was accomplished by receiver operating characteristic (ROC) analysis, and Spearman's correlation analysis was used to explore the association between immunohistochemical indices and diffusion parameters, the statistical significance level was p<0.05. RESULTS Dm and ADC demonstrated similar performance in differentiating malignant and benign lesions (AUC = 0.928 versus 0.930), while the combination of Dm, α, and β could improve the AUC to 0.969. The combined parameter generated by ADC, Dm, α, and β was effective in identifying the ER+/ER- and PR+/PR- patients. Temporal heterogeneity parameter α correlated significantly with the expression of PR. CONCLUSION Diffusion parameters derived from the CTRW model could effectively discriminate the malignancy of breast lesions. Meanwhile, the hormone receptor expression could be distinguished by combined diffusion parameters, and have the potential to reflect the prognosis.
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Affiliation(s)
- H Feng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - H Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Q Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - M Song
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - T Yang
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - L Zheng
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - D Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China
| | - X Shao
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
| | - G Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
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Wang B, Feng H, Huang H, Guo A, Zheng Y, Wang Y. Bonding Properties between Fly Ash/Slag-Based Engineering Geopolymer Composites and Concrete. Materials (Basel) 2023; 16:4232. [PMID: 37374415 DOI: 10.3390/ma16124232] [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: 05/09/2023] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023]
Abstract
Concrete infrastructure repair remains a formidable challenge. The application of engineering geopolymer composites (EGCs) as a repair material in the field of rapid structural repair can ensure the safety of structural facilities and prolong their service life. However, the interfacial bonding performance of existing concrete with EGCs is still unclear. The purpose of this paper is to explore a kind of EGC with good mechanical properties, and to evaluate the bonding performance of EGCs with existing concrete using a tensile bonding test and single shear bonding test. At the same time, X-ray diffraction (XRD) and Scanning electron microscopy (SEM) were adopted to study the microstructure. The results showed that the bond strength increased with the increase in interface roughness. For polyvinyl alcohol (PVA)-fiber-reinforced EGCs, the bond strength increased with the increase in FA content (0-40%). However, with the change of FA content (20-60%), the bond strength of polyethylene (PE) fiber-reinforced EGCs have little change. The bond strength of PVA-fiber-reinforced EGCs increased with the increase in water-binder ratio (0.30-0.34), while that of PE-fiber-reinforced EGCs decreased. The bond-slip model of EGCs with existing concrete was established based on the test results. XRD studies showed that when the FA content was 20-40%, the content of C-S-H gels was high and the reaction was sufficient. SEM studies showed that when the FA content was 20%, the PE fiber-matrix bonding was weakened to a certain extent, so the ductility of EGC was improved. Besides, with the increase in the water-binder ratio (0.30-0.34), the reaction products of the PE-fiber-reinforced EGC matrix gradually decreased.
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Affiliation(s)
- Baogui Wang
- Yellow River Laboratory, Zhengzhou University, Zhengzhou 450001, China
- Zhengzhou Metro Group Co., Ltd., Zhengzhou 450000, China
| | - Hu Feng
- Yellow River Laboratory, Zhengzhou University, Zhengzhou 450001, China
| | - Hao Huang
- China Institute of Water Resources and Hydropower Research, Beijing 100038, China
| | - Aofei Guo
- Yellow River Laboratory, Zhengzhou University, Zhengzhou 450001, China
| | - Yiming Zheng
- School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Yang Wang
- School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
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Feng H, Hu P, Chen Y, Sun H, Cai J, He X, Cao Q, Yin M, Zhang Y, Li Q, Gao J, Marshall C, Sheng C, Shi J, Xiao M. Decreased miR-451a in cerebrospinal fluid, a marker for both cognitive impairment and depressive symptoms in Alzheimer's disease. Theranostics 2023; 13:3021-3040. [PMID: 37284450 PMCID: PMC10240826 DOI: 10.7150/thno.81826] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [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: 12/14/2022] [Accepted: 04/06/2023] [Indexed: 06/08/2023] Open
Abstract
Background: Alzheimer's disease (AD) patients are often accompanied by depressive symptoms, but its underlying mechanism remains unclear. The present study aimed to explore the potential role of microRNAs in the comorbidity of AD and depression. Methods: The miRNAs associated with AD and depression were screened from databases and literature and then confirmed in the cerebrospinal fluid (CSF) of AD patients and different ages of transgenic APP/PS1 mice. AAV9-miR-451a-GFP was injected into the medial prefrontal cortex (mPFC) of APP/PS1 mice at seven months, and four weeks later, a series of behavioral and pathological analyses were performed. Results: AD patients had low CSF levels of miR-451a, which was positively correlated with the cognitive assessment score, but negatively with their depression scale. In the mPFC of APP/PS1 transgenic mice, the miR-451a levels also decreased significantly in the neurons and microglia. Specific virus vector-induced overexpression of miR-451a in the mPFC of APP/PS1 mice ameliorated AD-related behavior deficits and pathologies, including long-term memory defects, depression-like phenotype, β-amyloid load, and neuroinflammation. Mechanistically, miR-451a decreased the expression of neuronal β-secretase 1 of neurons through inhibiting Toll-like receptor 4/Inhibitor of kappa B Kinase β/ Nuclear factor kappa-B signaling pathway and microglial activation by inhibiting activation of NOD-like receptor protein 3, respectively. Conclusion: This finding highlighted miR-451a as a potential target for diagnosing and treating AD, especially for those with coexisting symptoms of depression.
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Affiliation(s)
- Hu Feng
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Panpan Hu
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Department of Anesthetic Pharmacology, Faculty of Anesthesiology, Naval Medical University, Shanghai, 200082, China
| | - Yan Chen
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Huaiqing Sun
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Department of Neurology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jiachen Cai
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Xiaoxin He
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Qiuchen Cao
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
| | - Mengmei Yin
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Department of Neurology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yanli Zhang
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Qian Li
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Junying Gao
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
| | | | - Chengyu Sheng
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Jingping Shi
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
- Department of Neurology, the Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Ming Xiao
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing, 211166, China
- Brain Institute, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
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Peng J, Meng R, Liu X, Zhang L, Wang L, Feng R, Feng H, Huang Z, Yao D, Li X, Liu N, Tan B, Li S, Yu J, Meng X. 172P A Chinese multicenter, real-world study of PD-L1 inhibitors in extensive stage small cell lung cancer. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00426-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
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Yan N, Feng H, Sun Y, Xin Y, Zhang H, Lu H, Zheng J, He C, Zuo Z, Yuan T, Li N, Xie L, Wei W, Sun Y, Zuo E. Cytosine base editors induce off-target mutations and adverse phenotypic effects in transgenic mice. Nat Commun 2023; 14:1784. [PMID: 36997536 PMCID: PMC10063651 DOI: 10.1038/s41467-023-37508-7] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
Base editors have been reported to induce off-target mutations in cultured cells, mouse embryos and rice, but their long-term effects in vivo remain unknown. Here, we develop a Systematic evaluation Approach For gene Editing tools by Transgenic mIce (SAFETI), and evaluate the off-target effects of BE3, high fidelity version of CBE (YE1-BE3-FNLS) and ABE (ABE7.10F148A) in ~400 transgenic mice over 15 months. Whole-genome sequence analysis reveals BE3 expression generated de novo mutations in the offspring of transgenic mice. RNA-seq analysis reveals both BE3 and YE1-BE3-FNLS induce transcriptome-wide SNVs, and the numbers of RNA SNVs are positively correlated with CBE expression levels across various tissues. By contrast, ABE7.10F148A shows no detectable off-target DNA or RNA SNVs. Notably, we observe abnormal phenotypes including obesity and developmental delay in mice with permanent genomic BE3 overexpression during long-time monitoring, elucidating a potentially overlooked aspect of side effects of BE3 in vivo.
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Affiliation(s)
- Nana Yan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hu Feng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yongsen Sun
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Ying Xin
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, China
| | - Haihang Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hongjiang Lu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, China
| | - Jitan Zheng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Chenfei He
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhenrui Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Tanglong Yuan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Nana Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, China
| | - Long Xie
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wu Wei
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- Lingang Laboratory, Shanghai, China.
| | - Yidi Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
| | - Erwei Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
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Cao W, Feng H, Ma Y, Zhao D, Hu X. Long-term trend of antibiotic use at public health care institutions in northwest China, 2012-20 -- a case study of Gansu Province. BMC Public Health 2023; 23:27. [PMID: 36604660 PMCID: PMC9814306 DOI: 10.1186/s12889-022-14944-6] [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: 05/06/2022] [Accepted: 12/26/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Over the past 20 years, excessive antibiotic use has led to serious antimicrobial resistance (AMR) worldwide, and the phenomenon is particularly serious in China. To this end, the Chinese health sector took a series of measures to promote rational antibiotic use. In this study, to reveal the impact of policies on antibiotic use, we explored the long-term trend and patterns of antibiotic use at public health care institutions from 2012 to 2020 in northwest China, taking Gansu Province as an example. METHODS Antibiotic procurement data were obtained from the provincial centralized bidding procurement (CBP) platform between 2012 and 2020. Antibiotic use was quantified using the Anatomical Therapeutic Chemical (ATC)/defined daily doses (DDD) methodology and standardized using the DDD per 1000 inhabitants per day (DID). Twelve relevant quality indicators were calculated for comparison with the European Surveillance of Antimicrobial Consumption (ESAC) project monitoring results. RESULTS Total antibiotic use increased from 18.75 DID to 57.07 DID and then decreased to 19.11 DID, a turning point in 2014. The top three antibiotics used were J01C (beta-lactam antibacterials, penicillins), J01F (macrolides, lincosamides and streptogramins), and J01D (other beta-lactam antibacterials, cephalosporins), accounting for 45.15%, 31.40%, and 11.99% respectively. The oral antibiotics used were approximately 2.5 times the parenteral antibiotics, accounting for 71.81% and 28.19%, respectively. Different use preferences were shown in public hospitals and primary health care centres (PHCs), and the latter accounted for more than half of total use. The absolute use of all classes of antibiotics in Gansu is almost higher than any of the 31 European countries included in the ESAC, but the relative use of some focused antibiotics is lower than theirs. CONCLUSIONS The intervention policies of the health department reduced antibiotic use in Gansu Province, but the proportion of broad-spectrum and parenteral antibiotics was still high. It is necessary to further improve the quality of antibiotic prescriptions and pay more attention to the rationality of antibiotic use in PHCs.
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Affiliation(s)
- Wenxuan Cao
- grid.32566.340000 0000 8571 0482School of Public Health, Lanzhou University, Lanzhou, 730000 China
| | - Hu Feng
- grid.32566.340000 0000 8571 0482School of Public Health, Lanzhou University, Lanzhou, 730000 China
| | - Yongheng Ma
- Division of Pharmaceutical Procurement, Gansu Public Resources Trading Center, Lanzhou, 730000 China
| | - Defang Zhao
- Division of Pharmaceutical Procurement, Gansu Public Resources Trading Center, Lanzhou, 730000 China
| | - Xiaobin Hu
- grid.32566.340000 0000 8571 0482School of Public Health, Lanzhou University, Lanzhou, 730000 China
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Tiantian C, Jing Z, Yiwen M, Yiming Y, Haifeng Y, Feng H, Xiaoyan J, Jin Y. COVID-19 causing death in a rheumatoid arthritis patient who retested positive for SARS-CoV-2 RNA: A case report. Int J Rheum Dis 2023; 26:957-959. [PMID: 36599657 DOI: 10.1111/1756-185x.14555] [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: 10/17/2022] [Revised: 11/08/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023]
Abstract
BACKGROUND In the first half 2022, SARS-CoV-2 variant omicron rapidly spread in Shanghai. CASE PRESENTATION A 74-year-old woman, diagnosed as having rheumatoid arthritis for almost 30 years and treated with prednisone and cyclophosphamide, went through nasopharyngeal swab RNA test for SARS-CoV-2 for routine screening and was positive. She was then sent to the designated hospital. After negative RNA test, the patient returned to the former hospital for the treatment of basic disease. Unfortunately, the RNA test of this patient became positive again. And in this period, the clinical manifestations and computed tomography scans were more progressive. Finally, the patient passed away. CONCLUSION There is a long way to go for us to study expression characteristics of SARS-CoV-2.
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Affiliation(s)
- Chen Tiantian
- Department of Pulmonary Medicine and Critical Medicine, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhang Jing
- Department of Pulmonary Medicine and Critical Medicine, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ma Yiwen
- Department of Pulmonary Medicine and Critical Medicine, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yu Yiming
- Department of Pulmonary Medicine and Critical Medicine, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yang Haifeng
- Department of Pulmonary Medicine and Critical Medicine, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hu Feng
- Department of Pulmonary Medicine and Critical Medicine, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jin Xiaoyan
- Department of Pulmonary Medicine and Critical Medicine, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yan Jin
- Department of Pulmonary Medicine and Critical Medicine, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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Jun C, Jian W, Yanxi L, Feng H, Zhaofei C, Guoya W. TOTAL ARTHROSCOPIC RECONSTRUCTION OF THE ANTERIOR CRUCIATE LIGAMENT. REV BRAS MED ESPORTE 2023. [DOI: 10.1590/1517-8692202329012022_0492] [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: 02/05/2023] Open
Abstract
ABSTRACT Introduction: Total internal arthroscopic anterior cruciate ligament reconstruction is one of the new technologies in recent years. The main advantage is the need for only one tendon for the surgical procedure. Objective: Compare the clinical effects of total internal and traditional anterior cruciate ligament reconstruction techniques. Methods: From January 2019 to January 2022, the clinical data of 45 patients with anterior cruciate ligament reconstruction were retrospectively analyzed, including 32 males and 13 females aged 18-33 years, mean of 24.2 ± 3.3 years. Total internal reconstruction was performed in 22 cases (total internal group) and traditional reconstruction in 23 cases (traditional group). The two groups recorded and compared the time of injury, duration of surgical procedure, postoperative VAS score, and recovery of knee function. The International Knee Literature Committee (IKDC) and the Lysholm scoring system were used to evaluate clinical efficacy. Results: 45 patients were followed for 14 to 18 months, mean (15.4 ± 1.3) months. There were no significant differences between the two groups in time between operation and injury, duration of operation, IKDC, and Lysholm score of the affected knee at the last follow-up. However, there were significant differences in the VAS score on day one, day three, day seven, two weeks, and one month after the operation (P < 0.05), with no significant difference at three months, six months, and one year after the operation. Conclusion: The effect of total internal reconstruction of the anterior cruciate ligament is equivalent to that of traditional methods, with less postoperative pain, making it the ideal choice for this treatment. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
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Affiliation(s)
- Chen Jun
- University of Science and Technology, China
| | - Wu Jian
- University of Science and Technology, China
| | - Liu Yanxi
- University of Science and Technology, China
| | - Hu Feng
- University of Science and Technology, China
| | | | - Wu Guoya
- University of Science and Technology, China
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32
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Dong XQ, Zhang ZQ, Feng H, Cai L. [A case report of the first and second branchial arch syndrome with torticollis]. Zhonghua Yan Ke Za Zhi 2022; 58:923-924. [PMID: 36348531 DOI: 10.3760/cma.j.cn112142-20220421-00189] [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: 06/16/2023]
Abstract
A 54-month-old female patient presented to the department of ophthalmology with abnormal head posture and facial asymmetry for two years. The patient's facial development was asymmetrical, with the middle 1/3 of the left side shorter than the right side. The left ear is less malformed than the right. There was no obvious abnormality in corneal light reflex and eye movement. Head tilt test ( -). So, paralysis of the superior oblique muscle was excluded. In consultation with the department of maxillofacial surgery, the patient was confirmed as the first and second branchial arch syndrome and torticollis.
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Affiliation(s)
- X Q Dong
- Department of Ophthalmology, Shenzhen University General Hospital, Shenzhen 518000, China
| | - Z Q Zhang
- Department of Ophthalmology, Shenzhen University General Hospital, Shenzhen 518000, China
| | - H Feng
- Department of Ophthalmology, Shenzhen University General Hospital, Shenzhen 518000, China
| | - L Cai
- Department of Ophthalmology, Shenzhen University General Hospital, Shenzhen 518000, China
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33
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Wei Y, Zhang M, Hu J, Zhou Y, Xue M, Yin J, Liu Y, Feng H, Zhou L, Li Z, Wang D, Zhang Z, Zhou Y, Liu H, Yao N, Zuo E, Hu J, Du Y, Li W, Xu C, Yang H. Human 8-cell embryos enable efficient induction of disease-preventive mutations without off-target effect by cytosine base editor. Protein Cell 2022. [DOI: 10.1093/procel/pwac043] [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: 11/06/2022] Open
Abstract
Abstract
Approximately 140 million people worldwide are homozygous carriers of APOE4 (ε4), a strong genetic risk factor for late onset familial and sporadic Alzheimer’s disease (AD), 91% of whom will develop AD at earlier age than heterozygous carriers and non-carriers. Susceptibility to AD could be reduced by targeted editing of APOE4, but a technical basis for controlling the off-target effects of base editors is necessary to develop low-risk personalized gene therapies. Here, we first screened eight cytosine base editor variants at four injection stages (from 1- to 8-cell stage), and found that FNLS-YE1 variant in 8-cell embryos achieved the comparable base conversion rate (up to 100%) with the lowest bystander effects. In particular, 80% of AD-susceptible ε4 allele copies were converted to the AD-neutral ε3 allele in human ε4-carrying embryos. Stringent control measures combined with targeted deep sequencing, whole genome sequencing, and RNA sequencing showed no DNA or RNA off-target events in FNLS-YE1-treated human embryos or their derived stem cells. Furthermore, base editing with FNLS-YE1 showed no effects on embryo development to the blastocyst stage. Finally, we also demonstrated FNLS-YE1 could introduce known protective variants in human embryos to potentially reduce human susceptivity to systemic lupus erythematosus (SLE) and familial hypercholesterolemia (FH). Our study therefore suggests that base editing with FNLS-YE1 can efficiently and safely introduce known preventive variants in 8-cell human embryos, a potential approach for reducing human susceptibility to AD or other genetic diseases.
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Affiliation(s)
- Yinghui Wei
- Center for Reproductive Medicine, International Peace Maternity and Child Health Hospital, Innovative Research Team of High-level Local Universities in Shanghai , School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Meiling Zhang
- Center for Reproductive Medicine, International Peace Maternity and Child Health Hospital, Innovative Research Team of High-level Local Universities in Shanghai , School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai Jiao Tong University , Shanghai 200127, China
- Center for Reproductive Medicine, Anhui Provincial Maternal and Child Health Hospital , Hefei 230001, China
| | - Jing Hu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Yingsi Zhou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Mingxing Xue
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Jianhang Yin
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Genome Editing Research Center , Peking University, Beijing 100871, China
| | - Yuanhua Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Hu Feng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture , Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
| | - Ling Zhou
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture , Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
| | - Zhifang Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture , Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
| | - Dongshuang Wang
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai Jiao Tong University , Shanghai 200127, China
| | - Zhiguo Zhang
- Reproductive Medicine Center, Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University , Hefei 230022, China
| | - Yin Zhou
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai Jiao Tong University , Shanghai 200127, China
| | - Hongbin Liu
- Center for Reproductive Medicine, Shandong University , Jinan 250012, China
| | - Ning Yao
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai Jiao Tong University , Shanghai 200127, China
| | - Erwei Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture , Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
| | - Jiazhi Hu
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Genome Editing Research Center , Peking University, Beijing 100871, China
| | - Yanzhi Du
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai Jiao Tong University , Shanghai 200127, China
| | - Wen Li
- Center for Reproductive Medicine, International Peace Maternity and Child Health Hospital, Innovative Research Team of High-level Local Universities in Shanghai , School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Chunlong Xu
- Lingang Laboratory, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence Technology , Shanghai 200031, China
| | - Hui Yang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
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34
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Wang H, Li X, Xu L, Ren Y, Deng W, Feng H, Yang Z, Ma S, Ni Q, Kuang Y. The Feasibility of Quad-Modal PET/SPECT/Spectral-CT/CBCT On-Board Imaging in a Small-Animal Radiation Therapy Platform. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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35
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Pan B, Guo D, Jing L, Li K, Li X, Li G, Gao X, Li ZW, Zhao W, Feng H, Cao MH. Long noncoding RNA Pvt1 promotes the proliferation and migration of Schwann cells by sponging microRNA-214 and targeting c-Jun following peripheral nerve injury. Neural Regen Res 2022; 18:1147-1153. [PMID: 36255005 PMCID: PMC9827779 DOI: 10.4103/1673-5374.353497] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Research has shown that long-chain noncoding RNAs (lncRNAs) are involved in the regulation of a variety of biological processes, including peripheral nerve regeneration, in part by acting as competing endogenous RNAs. c-Jun plays a key role in the repair of peripheral nerve injury. However, the precise underlying mechanism of c-Jun remains unclear. In this study, we performed microarray and bioinformatics analysis of mouse crush-injured sciatic nerves and found that the lncRNA Pvt1 was overexpressed in Schwann cells after peripheral nerve injury. Mechanistic studies revealed that Pvt1 increased c-Jun expression through sponging miRNA-214. We overexpressed Pvt1 in Schwann cells cultured in vitro and found that the proliferation and migration of Schwann cells were enhanced, and overexpression of miRNA-214 counteracted the effects of Pvt1 overexpression on Schwann cell proliferation and migration. We conducted in vivo analyses and injected Schwann cells overexpressing Pvt1 into injured sciatic nerves of mice. Schwann cells overexpressing Pvt1 enhanced the regeneration of injured sciatic nerves following peripheral nerve injury and the locomotor function of mice was improved. Our findings reveal the role of lncRNAs in the repair of peripheral nerve injury and highlight lncRNA Pvt1 as a novel potential treatment target for peripheral nerve injury.
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Affiliation(s)
- Bin Pan
- Department of Orthopedics, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Di Guo
- Department of Orthopedics, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Li Jing
- Department of Orthopedics, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Ke Li
- Department of Imaging, Xuzhou Central Hospital, Xuzhou, Jiangsu Province, China
| | - Xin Li
- Department of Orthopedics, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Gen Li
- Department of Orthopedics, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Xiao Gao
- Department of Orthopedics, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Zhi-Wen Li
- College of Extended Education, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Wei Zhao
- Department of Orthopedics, Kuitun Hospital, Yili Kazak Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China
| | - Hu Feng
- Department of Orthopedics, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China,Correspondence to: Meng-Han Cao, ; Hu Feng, .
| | - Meng-Han Cao
- Center of Clinical Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China,Correspondence to: Meng-Han Cao, ; Hu Feng, .
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36
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Wang Y, Zhou T, Ruan S, Feng H, Bi W, Hu J, Chen T, Liu H, Yuan B, Zhang N, Wang W, Zhang L, Chu W, Wu C, Xie Y. Directional Manipulation of Electron Transfer by Energy Level Engineering for Efficient Cathodic Oxygen Reduction. Nano Lett 2022; 22:6622-6630. [PMID: 35931416 DOI: 10.1021/acs.nanolett.2c01933] [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] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electron transfer plays an important role in determining the energy conversion efficiency of energy devices. Nitrogen-coordinated single metal sites (M-N4) materials as electrocatalysts have exhibited great potential in devices. However, there are still great difficulties in how to directionally manipulate electron transfer in M-N4 catalysts for higher efficiency. Herein, we demonstrated the mechanism of electron transfer being affected by energy level structure based on classical iron phthalocyanine (FePc) molecule/carbon models and proposed an energy level engineering strategy to manipulate electron transfer, preparing high-performance ORR catalysts. Engineering molecular energy level via modulating FePc molecular structure with nitro induces a strong interfacial electronic coupling and efficient charge transfer from carbon to FePc-β-NO2 molecule. Consequently, the assembled zinc-air battery exhibits ultrahigh performance which is superior to most of M-N4 catalysts. Energy level engineering provides a universal approach for directionally manipulating electron transfer, bringing a new concept to design efficient and stable M-N4 electrocatalyst.
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Affiliation(s)
- Yang Wang
- School of Chemistry and Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Tianpei Zhou
- School of Chemistry and Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Shanshan Ruan
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Hu Feng
- School of Chemistry and Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Wentuan Bi
- Institute of Energy, Hefei Comprehensive National Science Center, Hefei, Anhui 230026, P. R. China
| | - Jun Hu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Ting Chen
- School of Chemistry and Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Hongfei Liu
- School of Chemistry and Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Bingkai Yuan
- School of Chemistry and Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Nan Zhang
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Wenjie Wang
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Lidong Zhang
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Wangsheng Chu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
| | - Changzheng Wu
- School of Chemistry and Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
- Institute of Energy, Hefei Comprehensive National Science Center, Hefei, Anhui 230026, P. R. China
| | - Yi Xie
- School of Chemistry and Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, P.R. China
- Institute of Energy, Hefei Comprehensive National Science Center, Hefei, Anhui 230026, P. R. China
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Hong L, Wang X, Fang Z, Sun X, Ge X, Chen C, Feng H, Hu H. Clinical Efficacy of Venastent - A Novel Iliac Vein Stent for Non-Thrombotic Iliac Vein Lesions: A Multi-Centre Randomised Controlled Trial. J Vasc Surg 2022. [DOI: 10.1016/j.jvs.2022.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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38
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Qu Z, Deng B, Gao X, Pan B, Sun W, Feng H. The association between Roussouly sagittal alignment type and risk for adjacent segment degeneration following short-segment lumbar interbody fusion: a retrospective cohort study. BMC Musculoskelet Disord 2022; 23:653. [PMID: 35804342 PMCID: PMC9264674 DOI: 10.1186/s12891-022-05617-x] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/04/2022] [Indexed: 11/29/2022] Open
Abstract
Background To date, the influence of Roussouly type on development of adjacent segment degeneration (ASD) after lumber fusion is still not fully explored, and the current study is aimed to evaluate the effect of Roussouly type on development of radiological ASD after single-level lumber fusion, and to compare the Roussouly types and spinopelvic parameters among those with different degenerative patterns of ASDs on sagittal plane. Methods A retrospective review of 288 patients underwent L4/5 or L5/S1 single-level posterior interbody fusions between January 2016 and December 2018 with a minimum 2-year follow up was performed. Radiological ASDs were identified and divided into 3 groups according to different degenerative patterns of the cephalad adjacent level on sagittal plane, including the types of retrolisthesis (Group A), anterolisthesis (Group B), and axial disc space narrowing (Group C). Roussouly types and radiological measurements were compared among three groups and potential risk factors for ASD were evaluated. Results Radiological ASD was found in 59 (20.5%) cases, in which patients with Roussouly type-2 was the most common. While, on subgroup analysis among three ASD groups, Roussouly type-1 occupied the highest proportion in Group A, differ in Group B and Group C, both with Type-2 as the most common. Moreover, Group A had significantly lower pelvic tilt (PT), larger sacral slope (SS), and larger segmental angle (SA) than Group B and Group C, which showed a more anteverted pelvic in Group A. Multivariate regression analysis noted Roussouly type, preoperative PT, and ∆PI-LL as the independent risk factors for radiological ASD. Conclusion Roussouly type was significantly associated with the development of radiological ASD; however, the Roussouly types and spinal pelvic parameters were varied among different sagittal degenerative patterns of ASD, which was important in restoring optimal lumbar sagittal alignments in initial surgery.
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Affiliation(s)
- Zhe Qu
- Department of Spine Surgery, the Affiliated Hospital of Xuzhou Medical University, Huaihai West Road 99, Xuzhou, 221006, China.,Xuzhou Medical University, Xuzhou, China
| | - Bin Deng
- Department of Spine Surgery, the Affiliated Hospital of Xuzhou Medical University, Huaihai West Road 99, Xuzhou, 221006, China.,Xuzhou Medical University, Xuzhou, China
| | - Xiao Gao
- Department of Spine Surgery, the Affiliated Hospital of Xuzhou Medical University, Huaihai West Road 99, Xuzhou, 221006, China.,Xuzhou Medical University, Xuzhou, China
| | - Bin Pan
- Department of Spine Surgery, the Affiliated Hospital of Xuzhou Medical University, Huaihai West Road 99, Xuzhou, 221006, China.,Xuzhou Medical University, Xuzhou, China
| | - Wei Sun
- Department of Spine Surgery, the Affiliated Hospital of Xuzhou Medical University, Huaihai West Road 99, Xuzhou, 221006, China.,Xuzhou Medical University, Xuzhou, China
| | - Hu Feng
- Department of Spine Surgery, the Affiliated Hospital of Xuzhou Medical University, Huaihai West Road 99, Xuzhou, 221006, China. .,Xuzhou Medical University, Xuzhou, China.
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39
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Zhao H, Yang H, Yu X, Feng H, Yang F. Pyrotinib for HER2-amplified non-small cell lung cancer patient after progression to Afatinib: a case report. Anticancer Drugs 2022; 33:509-512. [PMID: 35324516 DOI: 10.1097/cad.0000000000001298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Contrary to the success of antihuman epidermal growth factor receptor 2 (HER2) therapy in HER2-amplified breast cancer, the optimal targeted drug therapy for HER2-amplified lung cancer remains to be determined clinically. In this report, a nonsmoker, Chinese, old, male patient was diagnosed with cT2bN3M0 nonsmall cell lung cancer with genetic testing revealing HER2 amplification. Though the patient received successful microwave ablation, the results of reexamination after two cycles of afatinib monotherapy showed disease progression. Then the treatment regimen was switched to pan-HER inhibitor pyrotinib 400 mg daily, with which the patient remained with stable disease for 9 months. After computed tomography showed tumor enlargement in October 2021, anlotinib was added to the present treatment. This case suggests that pyrotinib may provide a novel effective treatment option for HER2-amplified lung cancer.
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Affiliation(s)
- Huan Zhao
- Department of Comprehensive Cancer Treatment, Weihai Municipal Hospital, Weihai
- Department of Oncology, The Second Medical College of Binzhou Medical University, Binzhou Medical University, Yantai, Shandong, China
| | - Hongbo Yang
- Department of Comprehensive Cancer Treatment, Weihai Municipal Hospital, Weihai
| | - Xin Yu
- Department of Comprehensive Cancer Treatment, Weihai Municipal Hospital, Weihai
| | - Hu Feng
- Department of Comprehensive Cancer Treatment, Weihai Municipal Hospital, Weihai
| | - Fujun Yang
- Department of Comprehensive Cancer Treatment, Weihai Municipal Hospital, Weihai
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40
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Zhu Y, Li JQ, Chang Q, Qiang HP, Lu JH, Feng H, Shen YC, Qian JL, Chu TQ. [Impact of neoadjuvant immunotherapy on pulmonary function and perioperative outcomes in patients with resectable non-small cell lung cancer]. Zhonghua Yi Xue Za Zhi 2022; 102:393-398. [PMID: 35144337 DOI: 10.3760/cma.j.cn112137-20211009-02226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To explore the effect of neoadjuvant immunotherapy on pulmonary function and the efficacy in patients with resectable non-small cell lung cancer. Methods: Data of 30 patients with non-small cell lung cancer (NSCLC) who received neoadjuvant immunotherapy before surgery in the Chest Hospital of Shanghai Jiaotong University from March 2018 to September 2021 were retrospectively collect. The efficacy and safety of neoadjuvant immunotherapy in the perioperative period and changes in pulmonary function of patients before and after neoadjuvant treatment were valuated. Results: The patients were all-male with age of (61±8)years old, The major pathological response (MPR) rate of patients receiving neoadjuvant immunotherapy was 43%(13 cases), the pathologic complete response (pCR) rate was 37% (11 cases), disease control rate (DCR) was 97% (29 cases), objective response rate (ORR) was 67% (20 cases). The forced expiratory volume in one second (FEV1) after treatment was (2.59±0.63) L, and the ratio of FEV1 to the predicted value (FEV1%pred) was 85.27%±15.86%, which were significantly higher than those before treatment [(2.48±0.59)L, 81.73%±15.94%, respectively] (P=0.013, 0.022, respectively). Forced vital capacity (FVC) after treatment was (3.59±0.77) L, which was also significantly higher than before [(3.47±0.76) L,P=0.036]; while there were no statistical difference in FEV1/FVC and FVC accounted for the proportion of predicted values (FVC%pred) between before and after treatment (P=0.084, 0.344, respectively). The ratio of carbon monoxide dispersion (DLCO) to the predicted value (DLCO%pred) decreased from 83.61%±13.10% to 78.69%±13.85% after treatment (P=0.023). There was no significant difference in the incidence of postoperative complications between the DLCO%pred decreased group and the non-decreased group (3/18 vs 0/6; P=0.546). Conclusions: Neoadjuvant immunotherapy can increase the rate of MPR and PCR, significantly increase FEV1 and FEV1%pred, but also lead to a decrease in DLCO%pred; neoadjuvant immunotherapy does not increase the incidence of postoperative complications.
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Affiliation(s)
- Y Zhu
- Department of Pulmonary Function, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - J Q Li
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Q Chang
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - H P Qiang
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - J H Lu
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - H Feng
- Department of Emergency Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Y C Shen
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - J L Qian
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - T Q Chu
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
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41
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Wu Y, Wei X, Feng H, Hu B, Liu J, Wang T. LINC00993 promoting METTL14-mediated m6A methylation in prostate cancer cell proliferation and progression. Eur Urol 2022. [DOI: 10.1016/s0302-2838(22)00507-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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42
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Feng H, Deng Z, Ruan Y, Liu J, Wang T. Circular RNA EPHA3 suppresses prostate cancer cells proliferation and metastasis through miR-513a-3p/ SOX6 axis. Eur Urol 2022. [DOI: 10.1016/s0302-2838(22)00505-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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43
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Wei Y, Xu C, Feng H, Xu K, Li Z, Hu J, Zhou L, Wei Y, Zuo Z, Zuo E, Li W, Yang H, Zhang M. Human cleaving embryos enable efficient mitochondrial base-editing with DdCBE. Cell Discov 2022; 8:7. [PMID: 35102133 PMCID: PMC8803867 DOI: 10.1038/s41421-021-00372-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/28/2021] [Indexed: 11/09/2022] Open
Affiliation(s)
- Yinghui Wei
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Chunlong Xu
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China
| | - Hu Feng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Kui Xu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhifang Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jing Hu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ling Zhou
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yu Wei
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zhenrui Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Erwei Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wen Li
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Hui Yang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. .,Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
| | - Meiling Zhang
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Zhang J, Lu WD, Li M, Li G, Feng H, Zhang HY, Ji QS, Cui XP. [Risk factors of perinatal complications in patients with pulmonary hypertension underwent cesarean section in 4 Chinese centers]. Zhonghua Xin Xue Guan Bing Za Zhi 2022; 50:43-48. [PMID: 35045613 DOI: 10.3760/cma.j.cn112148-20211202-01041] [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: 06/14/2023]
Abstract
Objective: To identify the risk factors related to perinatal complications in patients with pulmonary hypertension underwent cesarean section. Methods: We retrospectively analyzed the medical records of all pregnant women with pulmonary hypertension hospitalized in 4 different hospitals in Shandong province and underwent cesarean section between May 2010 and May 2020. Patients were divided into perinatal complication group and control group according to the presence or absence of perinatal complications. Perinatal complications included aggravated heart function, new onset arrythmias, sudden cardiac arrest, all-cause death within 42 days post cesarean section, postpartum bleeding and thrombotic events. Risk factors of perinatal complications were analyzed. Results: A total of 167 patients (47 cases in the perinatal complication group and 120 cases in the control group) were included in this study. The average age of this cohort was 28(24, 32) years, and 75(44.9%) patients suffered newly diagnosed pulmonary hypertension during pregnancy. The main cause of pulmonary hypertension was congenital heart disease (137(82.0%)). Age, pregnant weeks, percent of primipara, intra-cardiac shunt, and receiving targeted medication therapy, cardiac dimensions were similar between the two groups. A total of 62 complications were recorded in the complication group including 28 cases of aggravated heart function, 4 cases of new onset arrythmias, 2 cases of cardiac arrest, 11 cases of bleeding or thrombotic events and 17 patients were dead. Prevalence of idiopathic pulmonary hypertension and general anesthesia was significantly higher, functional capacity was significantly lower in perinatal complication group than in control group (all P<0.05). The estimated systolic pulmonary artery pressure, serum N-terminal pro-B type natriuretic peptide and total bilirubin (TBIL) levels were significantly higher in perinatal complication group than in control group (all P<0.05). Logistic analysis demonstrated WHO Function Class(FC) Ⅲ/Ⅳ (OR=2.416,95%CI 1.016-5.743, P=0.046) and TBIL level (OR=6.874,95%CI 1.643-28.757, P=0.008) were the independent risk factors of perinatal complications. Conclusion: TBIL and WHO FC are independent risk factors of perinatal complications in pregnant women with pulmonary hypertension underwent cesarean section.
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Affiliation(s)
- J Zhang
- Department of Cardiovascular Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - W D Lu
- Department of Geriatric Medicine & Shandong Key Laboratory Cardiovascular Proteomics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - M Li
- Intensive Care Unit of Cardiac Surgery, Shandong Provincial Qianfoshan Hospital, First Hospital Affiliated to Shandong First Medical University, Affiliated Hospital of Shandong University, Jinan 250014, China
| | - G Li
- Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - H Feng
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - H Y Zhang
- Department of Geriatric Medicine & Shandong Key Laboratory Cardiovascular Proteomics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Q S Ji
- Department of Cardiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - X P Cui
- Department of Geriatric Medicine & Shandong Key Laboratory Cardiovascular Proteomics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
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Wang J, Qi W, Shi H, Huang L, Ning F, Wang F, Wang K, Bai H, Wu H, Zhuang J, Hong H, Zhou H, Feng H, Zhou Y, Dong N, Liu L, Kong Y, Xie J, Zhao RC. MiR-4763-3p targeting RASD2as a Potential Biomarker and Therapeutic Target for Schizophrenia. Aging Dis 2022; 13:1278-1292. [PMID: 35855328 PMCID: PMC9286908 DOI: 10.14336/ad.2022.0103] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/03/2021] [Indexed: 11/06/2022] Open
Abstract
Existing diagnostic methods are limited to observing appearance and demeanor, even though genetic factors play important roles in the pathology of schizophrenia. Indeed, no molecular-level test exists to assist diagnosis, which has limited treatment strategies. To address this serious shortcoming, we used a bioinformatics approach to identify 61 genes that are differentially expressed in schizophrenia patients compared with healthy controls. In particular, competing endogenous RNA network revealed the important role of the gene RASD2, which is regulated by miR-4763-3p. Indeed, analysis of blood samples confirmed that RASD2 is downregulated in schizophrenia patients. Moreover, positron emission tomography data collected for 44 human samples identified the prefrontal and temporal lobes as potential key brain regions in schizophrenia patients. Mechanistic studies indicated that miR-4763-3p inhibits RASD2 by base-pairing with the 3’ untranslated region of RASD2 mRNA. Importantly, RASD2 has been shown to interact with β-arrestin2, which contributes to the regulation of the DRD2-dependent CREB response element-binding protein pathway in the dopamine system. Finally, results obtained with a mouse model of schizophrenia revealed that inhibition of miR-4763-3p function alleviated anxiety symptoms and improved memory. The dopamine transporters in the striatal regions were significantly reduced in schizophrenia model mice as compared with wild-type mice, suggesting that inhibition of miR-4763-3p can lessen the symptoms of schizophrenia. Our findings demonstrate that miR-4763-3p may target RASD2 mRNA and thus may serve as a potential biomarker and therapeutic target for schizophrenia, providing a theoretical foundation for further studies of the molecular basis of this disease.
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Affiliation(s)
- Jiao Wang
- School of Life Sciences, Shanghai University, Shanghai, China.
- Correspondence should be addressed to: Dr. Jiao Wang (), School of Life Sciences, Shanghai University, Shanghai, China; Dr. Yanyan Kong (), PET Center, Huashan Hospital, Fudan University, Shanghai, China; Dr. Jiang Xie (), School of Computer Engineering and Science, Shanghai University, Shanghai, China, and Dr. Robert Chunhua Zhao (), School of Life Sciences, Shanghai University, Shanghai, China
| | - Wenxin Qi
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Hongwei Shi
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Lin Huang
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Fujiang Ning
- Psychological Rehabilitation Hospital of Penglai District, Yantai, Shandong, China
| | - Fushuai Wang
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Kai Wang
- Central Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
| | - Haotian Bai
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Hao Wu
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Junyi Zhuang
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Huanle Hong
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Haicong Zhou
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Hu Feng
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Yinping Zhou
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Naijun Dong
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Li Liu
- Psychological Rehabilitation Hospital of Penglai District, Yantai, Shandong, China
| | - Yanyan Kong
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.
- Correspondence should be addressed to: Dr. Jiao Wang (), School of Life Sciences, Shanghai University, Shanghai, China; Dr. Yanyan Kong (), PET Center, Huashan Hospital, Fudan University, Shanghai, China; Dr. Jiang Xie (), School of Computer Engineering and Science, Shanghai University, Shanghai, China, and Dr. Robert Chunhua Zhao (), School of Life Sciences, Shanghai University, Shanghai, China
| | - Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
- Correspondence should be addressed to: Dr. Jiao Wang (), School of Life Sciences, Shanghai University, Shanghai, China; Dr. Yanyan Kong (), PET Center, Huashan Hospital, Fudan University, Shanghai, China; Dr. Jiang Xie (), School of Computer Engineering and Science, Shanghai University, Shanghai, China, and Dr. Robert Chunhua Zhao (), School of Life Sciences, Shanghai University, Shanghai, China
| | - Robert Chunhua Zhao
- School of Life Sciences, Shanghai University, Shanghai, China.
- Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
- Centre of Excellence in Tissue Engineering, Chinese Academy of Medical Sciences, Beijing, China.
- Beijing Key Laboratory of New Drug Development and Clinical Trial of Stem Cell Therapy (BZ0381), Beijing, China.
- Correspondence should be addressed to: Dr. Jiao Wang (), School of Life Sciences, Shanghai University, Shanghai, China; Dr. Yanyan Kong (), PET Center, Huashan Hospital, Fudan University, Shanghai, China; Dr. Jiang Xie (), School of Computer Engineering and Science, Shanghai University, Shanghai, China, and Dr. Robert Chunhua Zhao (), School of Life Sciences, Shanghai University, Shanghai, China
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Meng X, Peng J, Li S, Feng H, Meng R, Zhang L, Liu X, Yu J. 106P Real-world outcomes in extensive-stage small cell lung cancer with PD-L1 inhibitors in China. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.10.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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47
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Drozdowski R, Sinha S, Lin G, Feng H. Accuracy of popular online symptom checkers for dermatological diagnoses. Clin Exp Dermatol 2021; 47:456-457. [PMID: 34609769 DOI: 10.1111/ced.14960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 11/27/2022]
Affiliation(s)
- R Drozdowski
- University of Connecticut School of Medicine, Farmington, CT, USA
| | - S Sinha
- Frank H. Netter MD School of Medicine at Quinnipiac University, North Haven, CT, USA
| | - G Lin
- Department of Dermatology, University of Connecticut Health Center, Farmington, CT, USA
| | - H Feng
- Department of Dermatology, University of Connecticut Health Center, Farmington, CT, USA
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48
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Feng H, Chen Y, Xie Z, Jiang J, Zhong Y, Gao L, Zhou W, Guo W, Yan W, Lv Z, Lu D, Liang H, Xu F, Yang J, Yang X, Zhou Q, Zhang D, Zhang Z, Chuai S, Zhang H, Wu Y, Zhang X. P52.02 High SHP2 Expression Determines the Efficacy of PD-1/PD-L1 Inhibitors in Advanced KRAS Mutant Non-Small Cell Lung Cancer. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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49
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Chen Z, Liu XF, Feng H, Tang JH, Zhao CM, Guo SJ, Chen Q, Liu L. Application of Maxillary Sinus Effusion Detection in Diagnosis of Drowning. Fa Yi Xue Za Zhi 2021; 37:215-219. [PMID: 34142483 DOI: 10.12116/j.issn.1004-5619.2020.400325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Indexed: 11/30/2022]
Abstract
Abstract Objective To study the imaging characteristics of maxillary sinus effusion in drowned bodies, to explore its morphological characteristics and value in the diagnosis of the cause of death, and to provide objective evidence to support the study of virtual anatomy of drowning. Methods The 154 postmortem CT examination cases (31 cases of drowning, 123 cases of non-drowning) of Beijing Public Security Bureau Forensic Center in 2019 were collected. The bodies of all cases were scanned by multi-layer spiral CT before double-blind reading by clinical imaging experts. Maxillary sinus of corpses with maxillary sinus effusion in imaging findings was punctured. The detection rate of maxillary sinus effusion was calculated. The CT value and volume of maxillary sinus effusion were measured on 3D DICOM workstation. Results The detection rate of maxillary sinus effusion in the drowning was 100%, the shape was horizontal liquid level, the volume was 1.2-11.2 mL, the CT value was 6.08-19.02 Hu, with an average value of 12.85 Hu. The detection rate of maxillary sinus effusion in non-drowning was 19.51% (24/123), the shape was wavy or irregular, and there were bubbles inside, the volume was 0.4-13.4 mL, the CT value was 23.68-77.75 Hu, with an average value of 42.08 Hu. The differences in CT value between the two groups had statistical significance. Conclusion The postmortem CT examination method can be used to observe the shape and measure the CT value of the maxillary sinus effusion in the bodies in water, which can be an auxiliary examination method for identification of drowning.
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Affiliation(s)
- Z Chen
- School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - X F Liu
- Criminal Investigation Brigade of Beijing Public Security Bureau, Beijing 100023, China
| | - H Feng
- Criminal Investigation Brigade of Beijing Public Security Bureau, Beijing 100023, China
| | - J H Tang
- Criminal Investigation Brigade of Beijing Public Security Bureau, Beijing 100023, China
| | - C M Zhao
- Criminal Investigation Brigade of Beijing Public Security Bureau, Beijing 100023, China
| | - S J Guo
- Detachment of Criminal Investigation, Haidian Branch of Beijing Public Security Bureau, Beijing 100192, China
| | - Q Chen
- Criminal Investigation Brigade of Beijing Public Security Bureau, Beijing 100023, China
| | - L Liu
- Criminal Investigation Brigade of Beijing Public Security Bureau, Beijing 100023, China
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50
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Liu H, Wang X, Wang G, Cui P, Wu S, Ai C, Hu N, Li A, He B, Shao X, Wu Z, Feng H, Chang Y, Mu D, Hou J, Dai X, Yin T, Ruan J, Cao F. The nearly complete genome of Ginkgo biloba illuminates gymnosperm evolution. Nat Plants 2021; 7:748-756. [PMID: 34135482 DOI: 10.1038/s41477-021-00933-x] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 04/29/2021] [Indexed: 05/19/2023]
Abstract
Gymnosperms are a unique lineage of plants that currently lack a high-quality reference genome due to their large genome size and high repetitive sequence content. Here, we report a nearly complete genome assembly for Ginkgo biloba with a genome size of 9.87 Gb, an N50 contig size of 1.58 Mb and an N50 scaffold size of 775 Mb. We were able to accurately annotate 27,832 protein-coding genes in total, superseding the inaccurate annotation of 41,840 genes in a previous draft genome assembly. We found that expansion of the G. biloba genome, accompanied by the notable extension of introns, was mainly caused by the insertion of long terminal repeats rather than the recent occurrence of whole-genome duplication events, in contrast to the findings of a previous report. We also identified candidate genes in the central pair, intraflagellar transport and dynein protein families that are associated with the formation of the spermatophore flagellum, which has been lost in all seed plants except ginkgo and cycads. The newly obtained Ginkgo genome provides new insights into the evolution of the gymnosperm genome.
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Affiliation(s)
- Hailin Liu
- The Southern Modern Forestry Collaborative Innovation Center, the Key Lab of Tree Genetics and Biotechnology of Educational Department of China and the Key Lab of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Xiaobo Wang
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, China
| | - Guibin Wang
- The Southern Modern Forestry Collaborative Innovation Center, the Key Lab of Tree Genetics and Biotechnology of Educational Department of China and the Key Lab of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, China
| | - Peng Cui
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Shigang Wu
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Cheng Ai
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Nan Hu
- The Southern Modern Forestry Collaborative Innovation Center, the Key Lab of Tree Genetics and Biotechnology of Educational Department of China and the Key Lab of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, China
- Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA
| | - Alun Li
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Bing He
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Xiujuan Shao
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhichao Wu
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hu Feng
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yuxiao Chang
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Desheng Mu
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jing Hou
- The Southern Modern Forestry Collaborative Innovation Center, the Key Lab of Tree Genetics and Biotechnology of Educational Department of China and the Key Lab of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, China
| | - Xiaogang Dai
- The Southern Modern Forestry Collaborative Innovation Center, the Key Lab of Tree Genetics and Biotechnology of Educational Department of China and the Key Lab of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, China
| | - Tongming Yin
- The Southern Modern Forestry Collaborative Innovation Center, the Key Lab of Tree Genetics and Biotechnology of Educational Department of China and the Key Lab of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, China.
| | - Jue Ruan
- Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - Fuliang Cao
- The Southern Modern Forestry Collaborative Innovation Center, the Key Lab of Tree Genetics and Biotechnology of Educational Department of China and the Key Lab of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, China.
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