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Lin Z, Ge H, Guo Q, Ren J, Gu W, Lu J, Zhong Y, Qiang J, Gong J, Li H. MRI-based radiomics model to preoperatively predict mesenchymal transition subtype in high-grade serous ovarian cancer. Clin Radiol 2024; 79:e715-e724. [PMID: 38342715 DOI: 10.1016/j.crad.2024.01.018] [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: 10/09/2023] [Revised: 01/04/2024] [Accepted: 01/12/2024] [Indexed: 02/13/2024]
Abstract
AIM To develop a magnetic resonance imaging (MRI)-based radiomics model for the preoperative identification of mesenchymal transition (MT) subtype in high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS One hundred and eighty-nine patients with histopathologically confirmed HGSOC were enrolled retrospectively. Among the included patients, 55 patients were determined as the MT subtype and the remaining 134 were non-MT subtype. After extracting a total of 204 features from T2-weighted imaging (T2WI) and contrast-enhanced (CE)-T1WI images, the Mann-Whitney U-test, Spearman correlation test, and Boruta algorithm were adopted to select the optimal feature set. Three classifiers, including logistic regression (LR), support vector machine (SVM), and random forest (RF), were trained to develop radiomics models. The performance of established models was evaluated from three aspects: discrimination, calibration, and clinical utility. RESULTS Seven radiomics features relevant to MT subtypes were selected to build the radiomics models. The model based on the RF algorithm showed the best performance in predicting MT subtype, with areas under the curves (AUCs) of 0.866 (95 % confidence interval [CI]: 0.797-0.936) and 0.852 (95 % CI: 0.736-0.967) in the training and testing cohorts, respectively. The calibration curves, supported with Brier scores, indicated very good consistency between observation and prediction. Decision curve analysis (DCA) showed that the RF-based model could provide more net benefit, which suggested favorable utility in clinical application. CONCLUSION The RF-based radiomics model provided accurate identification of MT from the non-MT subtype and may help facilitate personalised management of HGSOC.
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Affiliation(s)
- Z Lin
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China
| | - H Ge
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Q Guo
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - J Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing 100176, China
| | - W Gu
- Department of Pathology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai 200090, China
| | - J Lu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Y Zhong
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China
| | - J Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China.
| | - J Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - H Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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Ren J, Proust A, Launay F, Villanneau R. Halide-free CO 2 cycloaddition onto styrene oxide catalysed by first row transition-metal derivatives of polyoxotungstates. Chem Commun (Camb) 2024; 60:4549-4552. [PMID: 38577743 DOI: 10.1039/d4cc00522h] [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: 04/06/2024]
Abstract
Quaternary ammonium salts of metal derivatives of polyoxometalates [XW11O39M(H2O)]n- (X = P, Si; M = Cr, Mn, Co, Ni, Zn) were successfully tested instead of quaternary ammonium halides as catalysts in the cycloaddition of CO2 to styrene oxide. Remarkably, they gave very satisfactory yields of styrene carbonate at moderate temperature (80 °C).
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Affiliation(s)
- Jingjing Ren
- Institut Parisien de Chimie Moléculaire, CNRS UMR 8232, Sorbonne Université, Campus Pierre et Marie Curie, 4 Place Jussieu, Paris F-75005, France.
| | - Anna Proust
- Institut Parisien de Chimie Moléculaire, CNRS UMR 8232, Sorbonne Université, Campus Pierre et Marie Curie, 4 Place Jussieu, Paris F-75005, France.
| | - Franck Launay
- Laboratoire de Réactivité de Surface, UMR CNRS 7197, Sorbonne Université, Campus Pierre et Marie Curie, 4 Place Jussieu, Paris F-75005, France
| | - Richard Villanneau
- Institut Parisien de Chimie Moléculaire, CNRS UMR 8232, Sorbonne Université, Campus Pierre et Marie Curie, 4 Place Jussieu, Paris F-75005, France.
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Wu J, Yang W, Li L, Wu J, He J, Ru Y, Ren J, Wang Y, Zheng H, Shang Y, Li D. Plasminogen activator urokinase interacts with the fusion protein and antagonizes the growth of Peste des petits ruminants virus. J Virol 2024; 98:e0014624. [PMID: 38440983 PMCID: PMC11019896 DOI: 10.1128/jvi.00146-24] [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: 01/21/2024] [Accepted: 02/05/2024] [Indexed: 03/06/2024] Open
Abstract
Peste des petits ruminants is an acute and highly contagious disease caused by the Peste des petits ruminants virus (PPRV). Host proteins play a crucial role in viral replication. However, the effect of fusion (F) protein-interacting partners on PPRV infection is poorly understood. In this study, we found that the expression of goat plasminogen activator urokinase (PLAU) gradually decreased in a time- and dose-dependent manner in PPRV-infected goat alveolar macrophages (GAMs). Goat PLAU was subsequently identified using co-immunoprecipitation and confocal microscopy as an F protein binding partner. The overexpression of goat PLAU inhibited PPRV growth and replication, whereas silencing goat PLAU promoted viral growth and replication. Additionally, we confirmed that goat PLAU interacted with a virus-induced signaling adapter (VISA) to antagonize F-mediated VISA degradation, increasing the production of type I interferon. We also found that goat PLAU reduced the inhibition of PPRV replication in VISA-knockdown GAMs. Our results show that the host protein PLAU inhibits the growth and replication of PPRV by VISA-triggering RIG-I-like receptors and provides insight into the host protein that antagonizes PPRV immunosuppression.IMPORTANCEThe role of host proteins that interact with Peste des petits ruminants virus (PPRV) fusion (F) protein in PPRV replication is poorly understood. This study confirmed that goat plasminogen activator urokinase (PLAU) interacts with the PPRV F protein. We further discovered that goat PLAU inhibited PPRV replication by enhancing virus-induced signaling adapter (VISA) expression and reducing the ability of the F protein to degrade VISA. These findings offer insights into host resistance to viral invasion and suggest new strategies and directions for developing PPR vaccines.
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Affiliation(s)
- Junhuang Wu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
| | - Wenping Yang
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
| | - Lingxia Li
- College of Agriculture and Animal Husbandry, Qinghai University, Xining, China
| | - Jingyan Wu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
| | - Jijun He
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
| | - Yi Ru
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
| | - Jingjing Ren
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
| | - Yong Wang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Haixue Zheng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
| | - Youjun Shang
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
| | - Dan Li
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou, China
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Yang Q, Yi SH, Fu BS, Zhang T, Zeng KN, Feng X, Yao J, Tang H, Li H, Zhang J, Zhang YC, Yi HM, Lyu HJ, Liu JR, Luo GJ, Ge M, Yao WF, Ren FF, Zhuo JF, Luo H, Zhu LP, Ren J, Lyu Y, Wang KX, Liu W, Chen GH, Yang Y. [Clinical application of split liver transplantation: a single center report of 203 cases]. Zhonghua Wai Ke Za Zhi 2024; 62:324-330. [PMID: 38432674 DOI: 10.3760/cma.j.cn112139-20231225-00297] [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: 03/05/2024]
Abstract
Objective: To investigate the safety and therapeutic effect of split liver transplantation (SLT) in clinical application. Methods: This is a retrospective case-series study. The clinical data of 203 consecutive SLT, 79 living donor liver transplantation (LDLT) and 1 298 whole liver transplantation (WLT) performed at the Third Affiliated Hospital of Sun Yat-sen University from July 2014 to July 2023 were retrospectively analyzed. Two hundred and three SLT liver grafts were obtained from 109 donors. One hundred and twenty-seven grafts were generated by in vitro splitting and 76 grafts were generated by in vivo splitting. There were 90 adult recipients and 113 pediatric recipients. According to time, SLT patients were divided into two groups: the early SLT group (40 cases, from July 2014 to December 2017) and the mature SLT technology group (163 cases, from January 2018 to July 2023). The survival of each group was analyzed and the main factors affecting the survival rate of SLT were analyzed. The Kaplan-Meier method and Log-rank test were used for survival analysis. Results: The cumulative survival rates at 1-, 3-, and 5-year were 74.58%, 71.47%, and 71.47% in the early SLT group, and 88.03%, 87.23%, and 87.23% in the mature SLT group, respectively. Survival rates in the mature SLT group were significantly higher than those in the early SLT group (χ2=5.560,P=0.018). The cumulative survival rates at 1-, 3- and 5-year were 93.41%, 93.41%, 89.95% in the LDLT group and 87.38%, 81.98%, 77.04% in the WLT group, respectively. There was no significant difference among the mature SLT group, the LDLT group and the WLT group (χ2=4.016, P=0.134). Abdominal hemorrhage, infection, primary liver graft nonfunction,and portal vein thrombosis were the main causes of early postoperative death. Conclusion: SLT can achieve results comparable to those of WLT and LDLT in mature technology liver transplant centers, but it needs to go through a certain time learning curve.
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Affiliation(s)
- Q Yang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - S H Yi
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - B S Fu
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - T Zhang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - K N Zeng
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - X Feng
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - J Yao
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - H Tang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - H Li
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - J Zhang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - Y C Zhang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - H M Yi
- Organ transplant Intensive Care Unit, the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - H J Lyu
- Organ transplant Intensive Care Unit, the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - J R Liu
- Organ transplant Intensive Care Unit, the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - G J Luo
- Anesthesia & Surgery Center, the Third Affiliated Hospital of Sun Yat-sen University ,Guangzhou 510630
| | - M Ge
- Anesthesia & Surgery Center, the Third Affiliated Hospital of Sun Yat-sen University ,Guangzhou 510630
| | - W F Yao
- Anesthesia & Surgery Center, the Third Affiliated Hospital of Sun Yat-sen University ,Guangzhou 510630
| | - F F Ren
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - J F Zhuo
- Organ transplant Intensive Care Unit, the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - H Luo
- Anesthesia & Surgery Center, the Third Affiliated Hospital of Sun Yat-sen University ,Guangzhou 510630
| | - L P Zhu
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - J Ren
- Ultrasound Department of the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - Y Lyu
- Ultrasound Department of the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - K X Wang
- Organ Donation Department of the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - W Liu
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - G H Chen
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - Y Yang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
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Peng G, Liu T, Qi X, Wang Y, Ren J, Peng J, Du X, Hu S, Wu S, Zhao Y, Li D, Zheng H. A genome-wide CRISPR screening uncovers that TOB1 acts as a key host factor for FMDV infection via both IFN and EGFR mediated pathways. PLoS Pathog 2024; 20:e1012104. [PMID: 38512977 PMCID: PMC10986976 DOI: 10.1371/journal.ppat.1012104] [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: 09/07/2023] [Revised: 04/02/2024] [Accepted: 03/07/2024] [Indexed: 03/23/2024] Open
Abstract
The interaction between foot-and-mouth disease virus (FMDV) and the host is extremely important for virus infection, but there are few researches on it, which is not conducive to vaccine development and FMD control. In this study, we designed a porcine genome-scale CRISPR/Cas9 knockout library containing 93,859 single guide RNAs targeting 16,886 protein-coding genes, 25 long ncRNAs, and 463 microRNAs. Using this library, several previously unreported genes required for FMDV infection are highly enriched post-FMDV selection in IBRS-2 cells. Follow-up studies confirmed the dependency of FMDV on these genes, and we identified a functional role for one of the FMDV-related host genes: TOB1 (Transducer of ERBB2.1). TOB1-knockout significantly inhibits FMDV infection by positively regulating the expression of RIG-I and MDA5. We further found that TOB1-knockout led to more accumulation of mRNA transcripts of transcription factor CEBPA, and thus its protein, which further enhanced transcription of RIG-I and MDA5 genes. In addition, TOB1-knockout was shown to inhibit FMDV adsorption and internalization mediated by EGFR/ERBB2 pathway. Finally, the FMDV lethal challenge on TOB1-knockout mice confirmed that the deletion of TOB1 inhibited FMDV infection in vivo. These results identify TOB1 as a key host factor involved in FMDV infection in pigs.
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Affiliation(s)
- Gaochuang Peng
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, National Engineering Laboratory for Animal Breeding, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Tianran Liu
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, National Engineering Laboratory for Animal Breeding, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xiaolan Qi
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Yuzhe Wang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, National Engineering Laboratory for Animal Breeding, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
| | - Jingjing Ren
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jiangling Peng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xuguang Du
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, National Engineering Laboratory for Animal Breeding, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
| | - Siyu Hu
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, National Engineering Laboratory for Animal Breeding, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
| | - Sen Wu
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, National Engineering Laboratory for Animal Breeding, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
| | - Yaofeng Zhao
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, National Engineering Laboratory for Animal Breeding, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
| | - Dan Li
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Haixue Zheng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
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Wen Y, Duan X, Ren J, Zhang J, Guan G, Ru Y, Li D, Zheng H. African Swine Fever Virus I267L Is a Hemorrhage-Related Gene Based on Transcriptome Analysis. Microorganisms 2024; 12:400. [PMID: 38399804 PMCID: PMC10892147 DOI: 10.3390/microorganisms12020400] [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: 11/29/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 02/25/2024] Open
Abstract
African swine fever (ASF) is an acute and severe disease transmitted among domestic pigs and wild boars. This disease is notorious for its high mortality rate and has caused great losses to the world's pig industry in the past few years. After infection, pigs can develop symptoms such as high fever, inflammation, and acute hemorrhage, finally leading to death. African swine fever virus (ASFV) is the causal agent of ASF; it is a large DNA virus with 150-200 genes. Elucidating the functions of each gene could provide insightful information for developing prevention and control methods. Herein, to investigate the function of I267L, porcine alveolar macrophages (PAMs) infected with an I267L-deleted ASFV strain (named ∆I267L) and wild-type ASFV for 18 h and 36 h were taken for transcriptome sequencing (RNA-seq). The most distinct different gene that appeared at both 18 hpi (hours post-infection) and 36 hpi was F3; it is the key link between inflammation and coagulation cascades. KEGG analysis (Kyoto encyclopedia of genes and genomes analysis) revealed the complement and coagulation cascades were also significantly affected at 18 hpi. Genes associated with the immune response were also highly enriched with the deletion of I267L. RNA-seq results were validated through RT-qPCR. Further experiments confirmed that ASFV infection could suppress the induction of F3 through TNF-α, while I267L deletion partially impaired this suppression. These results suggest that I267L is a pathogenicity-associated gene that modulates the hemorrhages of ASF by suppressing F3 expression. This study provides new insights into the molecular mechanisms of ASFV pathogenicity and potential targets for ASFV prevention and control.
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Affiliation(s)
- Yuan Wen
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China; (Y.W.); (X.D.); (J.R.); (J.Z.); (G.G.); (Y.R.)
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730000, China
| | - Xianghan Duan
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China; (Y.W.); (X.D.); (J.R.); (J.Z.); (G.G.); (Y.R.)
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730000, China
| | - Jingjing Ren
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China; (Y.W.); (X.D.); (J.R.); (J.Z.); (G.G.); (Y.R.)
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730000, China
| | - Jing Zhang
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China; (Y.W.); (X.D.); (J.R.); (J.Z.); (G.G.); (Y.R.)
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730000, China
| | - Guiquan Guan
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China; (Y.W.); (X.D.); (J.R.); (J.Z.); (G.G.); (Y.R.)
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730000, China
| | - Yi Ru
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China; (Y.W.); (X.D.); (J.R.); (J.Z.); (G.G.); (Y.R.)
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730000, China
| | - Dan Li
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China; (Y.W.); (X.D.); (J.R.); (J.Z.); (G.G.); (Y.R.)
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730000, China
| | - Haixue Zheng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China; (Y.W.); (X.D.); (J.R.); (J.Z.); (G.G.); (Y.R.)
- Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730000, China
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Liu Q, Tian Y, Zhou T, Lyu K, Wang Z, Zheng Y, Liu Y, Ren J, Li J. An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice. IEEE J Biomed Health Inform 2024; 28:707-718. [PMID: 37669206 DOI: 10.1109/jbhi.2023.3312154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients' electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0.7873, recall@10 of 0.9020 and hits@10 of 0.9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.
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8
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Hu M, Peng H, Zhang X, Wang L, Ren J. Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data. BMC Med Inform Decis Mak 2024; 24:24. [PMID: 38267946 PMCID: PMC10809625 DOI: 10.1186/s12911-024-02426-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 01/15/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND AND AIMS Sexually transmitted infections (STIs) are a significant global public health challenge due to their high incidence rate and potential for severe consequences when early intervention is neglected. Research shows an upward trend in absolute cases and DALY numbers of STIs, with syphilis, chlamydia, trichomoniasis, and genital herpes exhibiting an increasing trend in age-standardized rate (ASR) from 2010 to 2019. Machine learning (ML) presents significant advantages in disease prediction, with several studies exploring its potential for STI prediction. The objective of this study is to build males-based and females-based STI risk prediction models based on the CatBoost algorithm using data from the National Health and Nutrition Examination Survey (NHANES) for training and validation, with sub-group analysis performed on each STI. The female sub-group also includes human papilloma virus (HPV) infection. METHODS The study utilized data from the National Health and Nutrition Examination Survey (NHANES) program to build males-based and females-based STI risk prediction models using the CatBoost algorithm. Data was collected from 12,053 participants aged 18 to 59 years old, with general demographic characteristics and sexual behavior questionnaire responses included as features. The Adaptive Synthetic Sampling Approach (ADASYN) algorithm was used to address data imbalance, and 15 machine learning algorithms were evaluated before ultimately selecting the CatBoost algorithm. The SHAP method was employed to enhance interpretability by identifying feature importance in the model's STIs risk prediction. RESULTS The CatBoost classifier achieved AUC values of 0.9995, 0.9948, 0.9923, and 0.9996 and 0.9769 for predicting chlamydia, genital herpes, genital warts, gonorrhea, and overall STIs infections among males. The CatBoost classifier achieved AUC values of 0.9971, 0.972, 0.9765, 1, 0.9485 and 0.8819 for predicting chlamydia, genital herpes, genital warts, gonorrhea, HPV and overall STIs infections among females. The characteristics of having sex with new partner/year, times having sex without condom/year, and the number of female vaginal sex partners/lifetime have been identified as the top three significant predictors for the overall risk of male STIs. Similarly, ever having anal sex with a man, age and the number of male vaginal sex partners/lifetime have been identified as the top three significant predictors for the overall risk of female STIs. CONCLUSIONS This study demonstrated the effectiveness of the CatBoost classifier in predicting STI risks among both male and female populations. The SHAP algorithm revealed key predictors for each infection, highlighting consistent demographic characteristics and sexual behaviors across different STIs. These insights can guide targeted prevention strategies and interventions to alleviate the impact of STIs on public health.
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Affiliation(s)
- Mengjie Hu
- Department of General Practice, First Affiliated Hospital, Zhejiang University School of Medicine, 310003, Hangzhou, China
| | - Han Peng
- Clinical Research Institute, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, China
| | - Xuan Zhang
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, 310003, Hangzhou, China
| | - Lefeng Wang
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, 310003, Hangzhou, China
| | - Jingjing Ren
- Department of General Practice, First Affiliated Hospital, Zhejiang University School of Medicine, 310003, Hangzhou, China.
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9
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Hakonen M, Dahmani L, Lankinen K, Ren J, Barbaro J, Blazejewska A, Cui W, Kotlarz P, Li M, Polimeni JR, Turpin T, Uluç I, Wang D, Liu H, Ahveninen J. Individual connectivity-based parcellations reflect functional properties of human auditory cortex. bioRxiv 2024:2024.01.20.576475. [PMID: 38293021 PMCID: PMC10827228 DOI: 10.1101/2024.01.20.576475] [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] [Indexed: 02/01/2024]
Abstract
Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Furthermore, connectional homogeneity within networks was highest for the individual-specific parcellations. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.
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Affiliation(s)
- M Hakonen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - L Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - K Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - J Ren
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - J Barbaro
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - A Blazejewska
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - W Cui
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - P Kotlarz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - M Li
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - J R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - T Turpin
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - I Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - D Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - H Liu
- Division of Brain Sciences, Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
| | - J Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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10
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Liao X, Li W, Zhou H, Rajendran BK, Li A, Ren J, Luan Y, Calderwood DA, Turk B, Tang W, Liu Y, Wu D. The CUL5 E3 ligase complex negatively regulates central signaling pathways in CD8 + T cells. Nat Commun 2024; 15:603. [PMID: 38242867 PMCID: PMC10798966 DOI: 10.1038/s41467-024-44885-0] [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: 10/21/2022] [Accepted: 01/09/2024] [Indexed: 01/21/2024] Open
Abstract
CD8+ T cells play an important role in anti-tumor immunity. Better understanding of their regulation could advance cancer immunotherapies. Here we identify, via stepwise CRISPR-based screening, that CUL5 is a negative regulator of the core signaling pathways of CD8+ T cells. Knocking out CUL5 in mouse CD8+ T cells significantly improves their tumor growth inhibiting ability, with significant proteomic alterations that broadly enhance TCR and cytokine signaling and their effector functions. Chemical inhibition of neddylation required by CUL5 activation, also enhances CD8 effector activities with CUL5 validated as a major target. Mechanistically, CUL5, which is upregulated by TCR stimulation, interacts with the SOCS-box-containing protein PCMTD2 and inhibits TCR and IL2 signaling. Additionally, CTLA4 is markedly upregulated by CUL5 knockout, and its inactivation further enhances the anti-tumor effect of CUL5 KO. These results together reveal a negative regulatory mechanism for CD8+ T cells and have strong translational implications in cancer immunotherapy.
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Affiliation(s)
- Xiaofeng Liao
- Vascular Biology and Therapeutic Program, Yale University School of Medicine, New Haven, CT, 06520, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA.
| | - Wenxue Li
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Hongyue Zhou
- Vascular Biology and Therapeutic Program, Yale University School of Medicine, New Haven, CT, 06520, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Barani Kumar Rajendran
- Vascular Biology and Therapeutic Program, Yale University School of Medicine, New Haven, CT, 06520, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Ao Li
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Jingjing Ren
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Yi Luan
- Vascular Biology and Therapeutic Program, Yale University School of Medicine, New Haven, CT, 06520, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - David A Calderwood
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Benjamin Turk
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Wenwen Tang
- Vascular Biology and Therapeutic Program, Yale University School of Medicine, New Haven, CT, 06520, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA.
| | - Yansheng Liu
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA.
- Yale Cancer Research Institute, Yale University School of Medicine, West Haven, CT, 06516, USA.
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT, 06520, USA.
| | - Dianqing Wu
- Vascular Biology and Therapeutic Program, Yale University School of Medicine, New Haven, CT, 06520, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06520, USA.
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT, 06520, USA.
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Ren J, Liao X, Lewis JM, Chang J, Qu R, Carlson KR, Foss F, Girardi M. Generation and optimization of off-the-shelf immunotherapeutics targeting TCR-Vβ2+ T cell malignancy. Nat Commun 2024; 15:519. [PMID: 38225288 PMCID: PMC10789731 DOI: 10.1038/s41467-024-44786-2] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 01/05/2024] [Indexed: 01/17/2024] Open
Abstract
Current treatments for T cell malignancies encounter issues of disease relapse and off-target toxicity. Using T cell receptor (TCR)Vβ2 as a model, here we demonstrate the rapid generation of an off-the-shelf allogeneic chimeric antigen receptor (CAR)-T platform targeting the clone-specific TCR Vβ chain for malignant T cell killing while limiting normal cell destruction. Healthy donor T cells undergo CRISPR-induced TRAC, B2M and CIITA knockout to eliminate T cell-dependent graft-versus-host and host-versus-graft reactivity. Second generation 4-1BB/CD3zeta CAR containing high affinity humanized anti-Vβ scFv is expressed efficiently on donor T cells via both lentivirus and adeno-associated virus transduction with limited detectable pre-existing immunoreactivity. Our optimized CAR-T cells demonstrate specific and persistent killing of Vβ2+ Jurkat cells and Vβ2+ patient derived malignant T cells, in vitro and in vivo, without affecting normal T cells. In parallel, we generate humanized anti-Vβ2 antibody with enhanced antibody-dependent cellular cytotoxicity (ADCC) by Fc-engineering for NK cell ADCC therapy.
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Affiliation(s)
- Jingjing Ren
- Department of Dermatology, Yale School of Medicine, New Haven, CT, USA.
| | - Xiaofeng Liao
- Department of Dermatology, Yale School of Medicine, New Haven, CT, USA.
| | - Julia M Lewis
- Department of Dermatology, Yale School of Medicine, New Haven, CT, USA
| | - Jungsoo Chang
- Department of Dermatology, Yale School of Medicine, New Haven, CT, USA
| | - Rihao Qu
- The Computational Biology and Bioinformatics Program, Yale School of Medicine, New Haven, CT, USA
| | - Kacie R Carlson
- Department of Dermatology, Yale School of Medicine, New Haven, CT, USA
| | - Francine Foss
- Department of Internal Medicine, Section of Medical Oncology, Yale School of Medicine, New Haven, CT, USA
| | - Michael Girardi
- Department of Dermatology, Yale School of Medicine, New Haven, CT, USA.
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12
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Han R, Chen Z, Nie Y, Liu B, Tian G, Zhang X, Shi F, Sun H, Zhang Z, Ding Y, Ruan X, Ren J, Zhang S. Measurement and analysis of leakage neutron spectra from Lead slab samples with D-T neutrons. Appl Radiat Isot 2024; 203:111113. [PMID: 37977101 DOI: 10.1016/j.apradiso.2023.111113] [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: 08/10/2023] [Revised: 11/07/2023] [Accepted: 11/12/2023] [Indexed: 11/19/2023]
Abstract
The leakage neutron spectra from three different sizes of Lead samples were measured by a TOF technique at 60° and 120°. The essential characteristic properties of the experimental measurement spectra can be reproduced well by MCNP code simulations with the ENDF/B-VIII.0, CENDL-3.2, JENDL-5.0, JEFF-3.3 and TENDL-2021 evaluated nuclear data libraries. The calculated results of JENDL-5.0 and JEFF-3.3 libraries agree better with the experimental data in the whole energy range. The results from ENDF/B-VIII.0 and CENDL-3.2 are overestimated in the 4-9 MeV range at 60° and in the 4-12.5 MeV range at 120°. The differences of the leakage neutron spectra by MCNP simulations using five evaluated nuclear data libraries mainly originate from the differences of the spectrum distributions of neutron reaction channels in these libraries. And the secondary neutron energy distribution and angular distribution from the five libraries have been present to explain it.
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Affiliation(s)
- R Han
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Z Chen
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Y Nie
- China Nuclear Data Center, China Institute of Atomic Energy, Beijing, 102413, China
| | - B Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - G Tian
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - F Shi
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - H Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Z Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Y Ding
- China Nuclear Data Center, China Institute of Atomic Energy, Beijing, 102413, China
| | - X Ruan
- China Nuclear Data Center, China Institute of Atomic Energy, Beijing, 102413, China
| | - J Ren
- China Nuclear Data Center, China Institute of Atomic Energy, Beijing, 102413, China
| | - S Zhang
- College of Physics and Electronics Information, Inner Mongolia University for the Nationalities, Tongliao, 028000, China
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13
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Liu Q, Tian Y, Zhou T, Lyu K, Xin R, Shang Y, Liu Y, Ren J, Li J. A few-shot disease diagnosis decision making model based on meta-learning for general practice. Artif Intell Med 2024; 147:102718. [PMID: 38184346 DOI: 10.1016/j.artmed.2023.102718] [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: 05/05/2023] [Revised: 10/12/2023] [Accepted: 11/12/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Diagnostic errors have become the biggest threat to the safety of patients in primary health care. General practitioners, as the "gatekeepers" of primary health care, have a responsibility to accurately diagnose patients. However, many general practitioners have insufficient knowledge and clinical experience in some diseases. Clinical decision making tools need to be developed to effectively improve the diagnostic process in primary health care. The long-tailed class distributions of medical datasets are challenging for many popular decision making models based on deep learning, which have difficulty predicting few-shot diseases. Meta-learning is a new strategy for solving few-shot problems. METHODS AND MATERIALS In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n-way, k-shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n×k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset. RESULT Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90.02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29.13 % and 21.63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results. CONCLUSION The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.
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Affiliation(s)
- Qianghua Liu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou 311100, China
| | - Kewei Lyu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China
| | - Ran Xin
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou 311100, China
| | - Yong Shang
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou 311100, China
| | - Ying Liu
- General Practice Department, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Jingjing Ren
- General Practice Department, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China; Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou 311100, China.
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14
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Nguyen VA, Brooks-Richards TL, Ren J, Woodruff MA, Allenby MC. Quantitative and large-format histochemistry to characterize peripheral artery compositional gradients. Microsc Res Tech 2023; 86:1642-1654. [PMID: 37602569 DOI: 10.1002/jemt.24400] [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: 07/20/2023] [Accepted: 08/06/2023] [Indexed: 08/22/2023]
Abstract
The femoropopliteal artery (FPA) is a long, flexible vessel that travels down the anteromedial compartment of the thigh as the femoral artery and then behind the kneecap as the popliteal artery. This artery undergoes various degrees of flexion, extension, and torsion during normal walking movements. The FPA is also the most susceptible peripheral artery to atherosclerosis and is where peripheral artery disease manifests in 80% of cases. The connection between peripheral artery location, its mechanical flexion, and its physiological or pathological biochemistry has been investigated for decades; however, histochemical methods remain poorly leveraged in their ability to spatially correlate normal or abnormal extracellular matrix and cells with regions of mechanical flexion. This study generates new histological image processing pipelines to quantitate tissue composition across high-resolution FPA regions-of-interest or low-resolution whole-section cross-sections in relation to their anatomical locations and flexions during normal movement. Comparing healthy ovine femoral, popliteal, and cranial-tibial artery sections as a pilot, substantial arterial contortion was observed in the distal popliteal and cranial tibial regions of the FPA which correlated with increased vascular smooth muscle cells and decreased elastin content. These methods aim to aid in the quantitative characterization of the spatial distribution of extracellular matrix and cells in large heterogeneous tissue sections such as the FPA. RESEARCH HIGHLIGHTS: Large-format histology preserves artery architecture. Elastin and smooth muscle content is correlated with distance from heart and contortion during flexion. Cell and protein analyses are sensitive to sectioning plane and image magnification.
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Affiliation(s)
- V A Nguyen
- School of Mechanical, Medical and Process Engineering, Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, Queensland, Australia
| | - T L Brooks-Richards
- School of Mechanical, Medical and Process Engineering, Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, Queensland, Australia
| | - J Ren
- School of Mechanical, Medical and Process Engineering, Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, Queensland, Australia
| | - M A Woodruff
- School of Mechanical, Medical and Process Engineering, Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, Queensland, Australia
| | - M C Allenby
- School of Mechanical, Medical and Process Engineering, Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, Queensland, Australia
- School of Chemical Engineering, University of Queensland (UQ), Brisbane, Queensland, Australia
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15
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Zhang X, Xu L, Li M, Chen X, Tang J, Zhang P, Wang Y, Chen B, Ren J, Liu J. Intelligent Ti3C2–Pt heterojunction with oxygen self-supply for augmented chemo-sonodynamic/immune tumor therapy. Materials Today Nano 2023; 24:100386. [DOI: 10.1016/j.mtnano.2023.100386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2023]
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16
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Li P, Wang X, Zeng Q, Ren J, Qin RN, Zhang JY. [Interaction analysis of the influence of different factors and benzene exposure on workers' alanine aminotransferase]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2023; 41:831-835. [PMID: 38073210 DOI: 10.3760/cma.j.cn121094-20220901-00436] [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: 12/18/2023]
Abstract
Objective: To investigate the main factors that influence ALT abnormalities in workers exposed to benzene. Methods: In June 2022, data of 613 enterprises with benzene hazards and 585 enterprises with non-benzene hazards in Tianjin in 2021 were collected, and occupational health examination data of 13018 workers with benzene exposure and 13018 workers with non-benzene exposure were collected, and the region, enterprise type, industry classification and enterprise scale of the employer were analyzed. And occupational health examination data of workers with benzene exposure and non-benzene exposure. The effects of personal general situation, occupational history, enterprise information and benzene exposure on alanine aminotransferase were evaluated by additive interaction. Results: Compared with the group of non-benzene-exposed workers, the personal general conditions, occupational history, company information were higher in the benzene-exposed workers, and the differences were statistically significant (P<0.05). The quantitative analysis of additive interaction found that gender (RERI=2.632, 95%CI: 1.966-3.297; AP=0.383, 95%CI: 0.311-0.456; S=1.813, 95%CI: 1.530-2.149), age (RERI=1.142, 95%CI: 0.928-1.356; AP=0.462, 95% CI: 0.371-0.552; S=4.461, 95%CI: 1.800-11.053), length of service (RERI=-1.199, 95%CI: -1.653--0.745; AP=-0.456, 95%CI: -0.640--0.271; S=0.576, 95%CI: 0.479-0.693), region (RERI=0.421, 95% CI: 0.148-0.694; AP=0.161, 95%CI: 0.053-0.268; S=1.350, 95%CI: 1.057-1.726), industry classification (RERI=0.627, 95%CI: 0.345-0.910; AP=0.232, 95%CI: 0.132-0.332; S=1.584, 95%CI: 1.233-2.035) and benzene exposure had a statistically significant additive interaction with abnormal serum ALT. Conclusion: Emphasis should be placed on male workers under the age of 40 in the petrochemical industry, oil storage and transportation, and power production, so as to protect the health of workers more specifically and reduce the risk of disability due to disease.
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Affiliation(s)
- P Li
- Institute for Occupational Health, Tianjin Center for Disease Control and Prevention, Tianjin 300011, China
| | - X Wang
- Institute for Occupational Health, Tianjin Center for Disease Control and Prevention, Tianjin 300011, China
| | - Q Zeng
- Institute for Occupational Health, Tianjin Center for Disease Control and Prevention, Tianjin 300011, China
| | - J Ren
- Institute for Occupational Health, Tianjin Center for Disease Control and Prevention, Tianjin 300011, China
| | - R N Qin
- Institute for Occupational Health, Tianjin Center for Disease Control and Prevention, Tianjin 300011, China
| | - J Y Zhang
- School of Public Health, Tianjin Medical University, Tianjin 300070, China
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Ren J, Zhao J, Wang Y, Xu M, Liu XY, Jin ZY, He YL, Li Y, Xue HD. Value of deep-learning image reconstruction at submillisievert CT for evaluation of the female pelvis. Clin Radiol 2023; 78:e881-e888. [PMID: 37620170 DOI: 10.1016/j.crad.2023.07.016] [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: 04/18/2023] [Revised: 06/26/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
AIM To assess the value of deep-learning reconstruction (DLR) at submillisievert computed tomography (CT) for the evaluation of the female pelvis, with standard dose (SD) hybrid iterative reconstruction (IR) images as reference. MATERIALS AND METHODS The present study enrolled 50 female patients consecutively who underwent contrast-enhanced abdominopelvic CT for clinically indicated reasons. Submillisievert pelvic images were acquired using a noise index of 15 for low-dose (LD) scans, which were reconstructed with DLR (body and body sharp), hybrid-IR, and model-based IR (MBIR). Additionally, SD scans were reconstructed with a noise index of 7.5 using hybrid-IR. Radiation dose, quantitative image quality, overall image quality, image appearance using a five-point Likert scale (1-5: worst to best), and lesion evaluation in both SD and LD images were analysed and compared. RESULTS The submillisievert pelvic CT examinations showed a 61.09 ± 4.13% reduction in the CT dose index volume compared to SD examinations. Among the LD images, DLR (body sharp) had the highest quantitative quality, followed by DLR (body), MBIR, and hybrid-IR. LD DLR (body) had overall image quality comparable to the reference (p=0.084) and favourable image appearance (p=0.209). In total, 40 pelvic lesions were detected in both SD and LD images. LD DLR (body and body sharp) exhibited similar diagnostic confidence (p=0.317 and 0.096) compared with SD hybrid-IR. CONCLUSION DLR algorithms, providing comparable image quality and diagnostic confidence, are feasible in submillisievert abdominopelvic CT. The DLR (body) algorithm with favourable image appearance is recommended in clinical settings.
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Affiliation(s)
- J Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - J Zhao
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - Y Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - M Xu
- Cannon Medical System, Beijing, PR China
| | - X-Y Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - Z-Y Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - Y-L He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Y Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, PR China.
| | - H-D Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
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Ren J, Jin T, Li R, Zhong YY, Xuan YX, Wang YL, Yao W, Yu SL, Yuan JT. Priority list of potential endocrine-disrupting chemicals in food chemical contaminants: a docking study and in vitro/epidemiological evidence integration. SAR QSAR Environ Res 2023; 34:847-866. [PMID: 37920972 DOI: 10.1080/1062936x.2023.2269855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 10/05/2023] [Indexed: 11/04/2023]
Abstract
Diet is an important exposure route of endocrine-disrupting chemicals (EDCs), but many unfiltered potential EDCs remain in food. The in silico prediction of EDCs is a popular method for preliminary screening. Potential EDCs in food were screened using Endocrine Disruptome, an open-source platform for inverse docking, to predict the binding probabilities of 587 food chemical contaminants with 18 human nuclear hormone receptor (NHR) conformations. In total, 25 contaminants were bound to multiple NHRs such as oestrogen receptor α/β and androgen receptor. These 25 compounds mainly include pesticides and per- and polyfluoroalkyl substances (PFASs). The prediction results were validated with the in vitro data. The structural features and the crucial amino acid residues of the four NHRs were also validated based on previous literature. The findings indicate that the screening has good prediction efficiency. In addition, the epidemic evidence about endocrine interference of PFASs in food on children was further validated through this screening. This study provides preliminary screening results for EDCs in food and a priority list for in vitro and in vivo research.
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Affiliation(s)
- J Ren
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - T Jin
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - R Li
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Y Y Zhong
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Y X Xuan
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Y L Wang
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - W Yao
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - S L Yu
- Key Laboratory of Natural Medicine and Immune-Engineering of Henan Province, Henan University, Kaifeng, Henan, P. R. China
| | - J T Yuan
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
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Hu X, Han C, Zhang M, Mu Z, Fu Z, Ren J, Qiao K, Jia J, Yu J, Yuan S, Wei Y. Predicting Radiation Esophagitis using 18F-FAPI-04 PET/CT in Patients with LA-ESCC Treated with Concurrent Chemoradiotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e303-e304. [PMID: 37785107 DOI: 10.1016/j.ijrobp.2023.06.2323] [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) This prospective study examined whether 18F-FAPI-04 PET/CT can predict the development and severity of radiation esophagitis (RE) in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated with concurrent chemoradiotherapy. MATERIALS/METHODS From June 2021 to March 2022, images were prospectively collected from LA-ESCC patients who underwent 18F-FAPI-04 PET/CT examinations before and during radiotherapy. The development of RE was evaluated weekly according to Radiation Therapy Oncology Group criterion. The target-to-background ratio in blood (TBRblood) was analyzed at each time point and correlated with the onset and severity of RE. Factors that predicted RE were identified by multivariate logistic analyses. RESULTS Thirty patients (median age, 66.5 years [interquartile range: 56¨C71 years]; 22 men) were evaluated. Significantly higher TBRblood (during radiotherapy, mean: 3.06 vs 7.11, P = 0.003) and change in TBRblood compared with pre-RT (ΔTBRblood, mean: 0.67 vs 4.81, P = 0.002) were observed in patients with RE than patients without RE. Those with grade 3 RE had a significantly higher TBRblood (during radiotherapy, mean: 4.55 vs 9.66, P = 0.003) and ΔTBRblood (mean: 2.16 vs 7.50, P = 0.003) compared with those with RE CONCLUSION The ΔTBRblood on 18F-FAPI-04 PET/CT may be effective at identifying patients at risk for the development of RE, especially grade 3 RE.
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Affiliation(s)
- X Hu
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - C Han
- Department of Surgery II, Breast Cancer Center, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - M Zhang
- 1.Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China. 2.Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Z Mu
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Z Fu
- Shandong Cancer Hospital and Institute, Jinan, China
| | - J Ren
- Department of PET/CT Center, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - K Qiao
- Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - J Jia
- Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China 2. Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Yu
- Shandong Cancer Hospital, Shandong University, Jinan, Shandong, China
| | - S Yuan
- Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Y Wei
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Ying M, Shao X, Qin H, Yin P, Lin Y, Wu J, Ren J, Zheng Y. Disease Burden and Epidemiological Trends of Chronic Kidney Disease at the Global, Regional, National Levels from 1990 to 2019. Nephron Clin Pract 2023; 148:113-123. [PMID: 37717572 PMCID: PMC10860888 DOI: 10.1159/000534071] [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: 09/30/2022] [Accepted: 08/17/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is a serious public health issue worldwide, but the disease burden of CKD caused by different etiologies and changing trends has not been fully examined. METHODS We collected data from Global Burden of Disease Study 2019 (GBD 2019), including incident cases, age-standardized incidence rate (ASIR), disability-adjusted life years (DALYs), and age-standardized DALY rate between 1990 and 2019 by region, etiology, age, and sex, and calculated the estimated annual percentage change (EAPC) of the rate to evaluate the epidemiological trends. RESULTS Globally, incident cases of CKD increased from 7.80 million in 1990 to 18.99 million in 2019, and DALYs increased from 21.50 million to 41.54 million. ASIR increased with an EAPC of 0.69 (95% uncertainty interval [UI] 0.49-0.89) and reached 233.65 per 100,000 in 2019, while the age-standardized DALY rate increased with an EAPC of 0.30 (95% UI 0.17-0.43) and reached 514.86 per 100,000. North Africa and the Middle East, central Latin America, and North America had the highest ASIR in 2019. Central Latin America had the highest age-standardized DALY rate, meanwhile. Almost all countries experienced an increase in ASIR, and over 50% of countries had an increasing trend in age-standardized DALY rate from 1990 to 2019. CKD due to diabetes mellitus type 2 and hypertension accounted for the largest disease burden with 85% incident cases and 66% DALYs in 2019 of known causes, with the highest growth in age-standardized DALY rate and a similar geographic pattern to that of total CKD. Besides, the highest incidence rate of total and four specific CKDs were identified in people aged 70 plus years, who also had the highest DALY rate with a stable trend after 2010. Females had a higher ASIR, while males had a higher age-standardized DALY rate, the gap of which was most distinctive in CKD due to hypertension. CONCLUSION The disease burden of CKD remains substantial and continues to grow globally. From 1990 to 2019, global incident cases of CKD have more than doubled and DALYs have almost doubled, and surpassed 40 million years. CKD due to diabetes mellitus type 2 and hypertension contributed nearly 2/3 of DALYs in 2019 of known causes, and had witnessed the highest growth in age-standardized DALY rate. Etiology-specific prevention strategies should be placed as a high priority on the goal of precise control of CKD.
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Affiliation(s)
- Meike Ying
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xue Shao
- Kidney Disease Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hongli Qin
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Pei Yin
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yushi Lin
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jie Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jingjing Ren
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yang Zheng
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Ren J, Wang XQ, Nakao T, Libby P, Shi GP. Differential Roles of Interleukin-6 in Severe Acute Respiratory Syndrome-Coronavirus-2 Infection and Cardiometabolic Diseases. Cardiol Discov 2023; 3:166-182. [PMID: 38152628 PMCID: PMC10750760 DOI: 10.1097/cd9.0000000000000096] [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] [Indexed: 12/29/2023]
Abstract
Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection can lead to a cytokine storm, unleashed in part by pyroptosis of virus-infected macrophages and monocytes. Interleukin-6 (IL-6) has emerged as a key participant in this ominous complication of COVID-19. IL-6 antagonists have improved outcomes in patients with COVID-19 in some, but not all, studies. IL-6 signaling involves at least 3 distinct pathways, including classic-signaling, trans-signaling, and trans-presentation depending on the localization of IL-6 receptor and its binding partner glycoprotein gp130. IL-6 has become a therapeutic target in COVID-19, cardiovascular diseases, and other inflammatory conditions. However, the efficacy of inhibition of IL-6 signaling in metabolic diseases, such as obesity and diabetes, may depend in part on cell type-dependent actions of IL-6 in controlling lipid metabolism, glucose uptake, and insulin sensitivity owing to complexities that remain to be elucidated. The present review sought to summarize and discuss the current understanding of how and whether targeting IL-6 signaling ameliorates outcomes following SARS-CoV-2 infection and associated clinical complications, focusing predominantly on metabolic and cardiovascular diseases.
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Affiliation(s)
- Jingjing Ren
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Xiao-Qi Wang
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Tetsushi Nakao
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Peter Libby
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Guo-Ping Shi
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
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Li D, Peng J, Wu J, Yi J, Wu P, Qi X, Ren J, Peng G, Duan X, Ru Y, Liu H, Tian H, Zheng H. African swine fever virus MGF-360-10L is a novel and crucial virulence factor that mediates ubiquitination and degradation of JAK1 by recruiting the E3 ubiquitin ligase HERC5. mBio 2023; 14:e0060623. [PMID: 37417777 PMCID: PMC10470787 DOI: 10.1128/mbio.00606-23] [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: 03/09/2023] [Accepted: 05/16/2023] [Indexed: 07/08/2023] Open
Abstract
African swine fever virus (ASFV) causes acute hemorrhagic infectious disease in pigs. The ASFV genome encodes various proteins that enable the virus to escape innate immunity; however, the underlying mechanisms are poorly understood. The present study found that ASFV MGF-360-10L significantly inhibits interferon (IFN)-β-triggered STAT1/2 promoter activation and the production of downstream IFN-stimulated genes (ISGs). ASFV MGF-360-10L deletion (ASFV-Δ10L) replication was impaired compared with the parental ASFV CN/GS/2018 strain, and more ISGs were induced by the ASFV-Δ10L in porcine alveolar macrophages in vitro. We found that MGF-360-10L mainly targets JAK1 and mediates its degradation in a dose-dependent manner. Meanwhile, MGF-360-10L also mediates the K48-linked ubiquitination of JAK1 at lysine residues 245 and 269 by recruiting the E3 ubiquitin ligase HERC5 (HECT and RLD domain-containing E3 ubiquitin protein ligase 5). The virulence of ASFV-Δ10L was significantly lower than that of the parental strain in vivo, which indicates that MGF-360-10L is a novel virulence factor of ASFV. Our findings elaborate the novel mechanism of MGF-360-10L on the STAT1/2 signaling pathway, expanding our understanding of the inhibition of host innate immunity by ASFV-encoded proteins and providing novel insights that could contribute to the development of African swine fever vaccines. IMPORTANCE African swine fever outbreaks remain a concern in some areas. There is no effective drug or commercial vaccine to prevent African swine fever virus (ASFV) infection. In the present study, we found that overexpression of MGF-360-10L strongly inhibited the interferon (IFN)-β-induced STAT1/2 signaling pathway and the production of IFN-stimulated genes (ISGs). Furthermore, we demonstrated that MGF-360-10L mediates the degradation and K48-linked ubiquitination of JAK1 by recruiting the E3 ubiquitin ligase HERC5. The virulence of ASFV with MGF-360-10L deletion was significantly less than parental ASFV CN/GS/2018. Our study identified a new virulence factor and revealed a novel mechanism by which MGF-360-10L inhibits the immune response, thus providing new insights into the vaccination strategies against ASFV.
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Affiliation(s)
- Dan Li
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jiangling Peng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Junhuang Wu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jiamin Yi
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Panxue Wu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xiaolan Qi
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jingjing Ren
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Gaochuang Peng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xianghan Duan
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Yi Ru
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Huanan Liu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Hong Tian
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Haixue Zheng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
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Xu S, Ren J, Lewis JM, Carlson KR, Girardi M. Proteasome Inhibitors Interact Synergistically with BCL2, Histone Deacetylase, BET, and Jak Inhibitors against Cutaneous T-Cell Lymphoma Cells. J Invest Dermatol 2023; 143:1322-1325.e3. [PMID: 36642402 DOI: 10.1016/j.jid.2022.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/25/2022] [Accepted: 12/16/2022] [Indexed: 01/15/2023]
Affiliation(s)
- Suzanne Xu
- Department of Dermatology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jingjing Ren
- Department of Dermatology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Julia M Lewis
- Department of Dermatology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kacie R Carlson
- Department of Dermatology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Michael Girardi
- Department of Dermatology, Yale School of Medicine, New Haven, Connecticut, USA.
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Wang A, Xu H, Zhang C, Ren J, Liu J, Zhou P. Radiomic analysis of MRI for prediction of response to induction chemotherapy in nasopharyngeal carcinoma patients. Clin Radiol 2023:S0009-9260(23)00223-4. [PMID: 37331848 DOI: 10.1016/j.crad.2023.05.012] [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: 03/03/2023] [Revised: 05/03/2023] [Accepted: 05/23/2023] [Indexed: 06/20/2023]
Abstract
AIM To establish and validate radiomic models for response prediction to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) using the radiomic features from pretreatment MRI. MATERIALS AND METHODS This retrospective analysis included 184 consecutive NPC patients, 132 in the primary cohort and 52 in the validation cohort. Radiomic features were derived from contrast-enhanced T1-weighted imaging (CE-T1) and T2-weighted imaging (T2-WI) for each subject. The radiomic features were then selected and combined with clinical characteristics to build radiomic models. The potential of the radiomic models was evaluated based on its discrimination and calibration. To measure the performance of these radiomic models in predicting the treatment response to IC in NPC, the area under the receiver operating characteristic curve (AUC), and sensitivity, specificity, and accuracy were used. RESULTS Four radiomic models were constructed in the present study including the radiomic signature of CE-T1, T2-WI, CE-T1 + T2-WI, and the radiomic nomogram of CE-T1. The radiomic signature of CE-T1 + T2-WI performed well in distinguishing response and non-response to IC in patients with NPC, which yielded an AUC of 0.940 (95% CI, 0.885-0.974), sensitivity of 83.1%, specificity of 91.8%, and accuracy of 87.1% in the primary cohort, and AUC of 0.952 (95% CI, 0.855-0.992), sensitivity of 74.2%, specificity of 95.2%, and accuracy of 82.7% in the validation cohort. CONCLUSION MRI-based radiomic models could be helpful for personalised risk stratification and treatment in NPC patients receiving IC.
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Affiliation(s)
- A Wang
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - H Xu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - C Zhang
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - J Ren
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - J Liu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - P Zhou
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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25
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Ren J, Li D, Zhu G, Yang W, Ru Y, Feng T, Qin X, Hao R, Duan X, Liu X, Zheng H. Deletion of MGF-110-9L gene from African swine fever virus weakens autophagic degradation of TBK1 as a mechanism for enhancing type I interferon production. FASEB J 2023; 37:e22934. [PMID: 37144880 DOI: 10.1096/fj.202201856r] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/19/2023] [Accepted: 04/11/2023] [Indexed: 05/06/2023]
Abstract
African swine fever (ASF) caused by African swine fever virus (ASFV) is a devastating disease for the global pig industry and economic benefit. The limited knowledge on the pathogenesis and infection mechanisms of ASF restricts progress toward vaccine development and ASF control. Previously, we illustrated that deletion of the MGF-110-9L gene from highly virulent ASFV CN/GS/2018 strains (ASFV∆9L) results in attenuated virulence in swine, but the underlying mechanism remains unclear. In this study, we found that the difference in virulence between wild-type ASFV (wt-ASFV) and ASFV∆9L strains was mainly caused by the difference in TANK Binding Kinase 1 (TBK1) reduction. TBK1 reduction was further identified to be mediated by the autophagy pathway and this degradative process requires the up-regulation of a positive autophagy regulation molecule- Phosphatidylinositol-4-Phosphate 3-Kinase Catalytic Subunit Type 2 Beta (PIK3C2B). Moreover, TBK1 over-expression was confirmed to inhibit ASFV replication in vitro. In summary, these results indicate that wt-ASFV counteracts type I interferon (IFN) production by degrading TBK1, while ASFVΔ9L enhanced type I IFN production by weakening TBK1 reduction, clarifying the mechanism that ASFVΔ9L present the attenuated virulence in vitro.
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Affiliation(s)
- Jingjing Ren
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Dan Li
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Guoqiang Zhu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Wenping Yang
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Yi Ru
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Tao Feng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xiaodong Qin
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Rongzeng Hao
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xianghan Duan
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xiangtao Liu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Haixue Zheng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
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26
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Zeng H, Su Y, Gong X, Zheng L, Zhang L, Meng P, Zhou Q, Ren J. Competitive adsorption behavior of typical heavy metal ions from acid mine drainage by multigroup-functionalization cellulose: qualitative and quantitative mechanism. Environ Sci Pollut Res Int 2023; 30:68191-68205. [PMID: 37119495 DOI: 10.1007/s11356-023-27188-7] [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] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/19/2023] [Indexed: 05/27/2023]
Abstract
In response to Cd, Pb, and Cu pollution in acid mine drainage (AMD), a multigroup cellulose material (TCIS) containing thiol (-SH), carboxyl (-COOH), and imine (-C = N) groups was prepared through oxidation and grafting reactions. At pH 5, the maximum Cd(II), Pb(II), and Cu(II) adsorption performances of TCIS were 53.60, 120.6, and 36.01 mg/g, respectively. In the binary system, the interaction between metal ions was mainly inhibited by competitive adsorption. Cu(II) exhibited the most fierce inhibitory effect and had a relatively stable adsorption performance. In the ternary system, the adsorption order was Cu(II) > Cd(II) > Pb(II). In density functional theory (DFT) calculations, we combined the molecular electrostatic potentials, binding energies, differential charges, and total potentials to illustrate the competitive behavior of metal ions at different binding sites. Moreover, X-ray photoelectron spectroscopy (XPS) and DFT analysis revealed that the adsorption process of TCIS was dominated by the above functional groups, which caused competitive adsorption among Cd(II), Pb(II), and Cu(II).
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Affiliation(s)
- Hao Zeng
- School of Environment, South China Normal University, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China
| | - Yaoming Su
- South China Institute of Environmental Sciences, Guangzhou, 510655, People's Republic of China
| | - Xing Gong
- School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China
| | - Liuchun Zheng
- School of Environment, South China Normal University, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China.
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou, 510006, People's Republic of China.
| | - Lijuan Zhang
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510640, People's Republic of China
| | - Peipei Meng
- College of Environment, Jinan University, Guangzhou, 510632, People's Republic of China
| | - Qianya Zhou
- School of Environment, South China Normal University, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China
| | - Jingjing Ren
- School of Environment, South China Normal University, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China
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27
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Li D, Ren J, Zhu G, Wu P, Yang W, Ru Y, Feng T, Liu H, Zhang J, Peng J, Tian H, Liu X, Zheng H. Deletions of MGF110-9L and MGF360-9L from African swine fever virus are highly attenuated in swine and confer protection against homologous challenge. J Biol Chem 2023; 299:104767. [PMID: 37142221 PMCID: PMC10236468 DOI: 10.1016/j.jbc.2023.104767] [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: 07/28/2022] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 05/06/2023] Open
Abstract
African swine fever, caused by a large icosahedral DNA virus (African swine fever virus, ASFV), is a highly contagious disease in domestic and feral swine, thus posing a significant economic threat to the global swine industry. Currently, there are no effective vaccines or the available methods to control ASFV infection. Attenuated live viruses with deleted virulence factors are considered to be the most promising vaccine candidates; however, the mechanism by which these attenuated viruses confer protection is unclear. Here, we used the Chinese ASFV CN/GS/2018 as a backbone and used homologous recombination to generate a virus in which MGF110-9L and MGF360-9L, two genes antagonize host innate antiviral immune response, were deleted (ASFV-ΔMGF110/360-9L). This genetically modified virus was highly attenuated in pigs and provided effective protection of pigs against parental ASFV challenge. Importantly, we found ASFV-ΔMGF110/360-9L infection induced higher expression of Toll-like receptor 2 (TLR2) mRNA compared with parental ASFV as determined by RNA-Seq and RT-PCR analysis. Further immunoblotting results showed that parental ASFV and ASFV-ΔMGF110/360-9L infection inhibited Pam3CSK4-triggered activating phosphorylation of proinflammatory transcription factor NF-κB subunit p65 and phosphorylation of NF-κB inhibitor IκBα levels, although NF-κB activation was higher in ASFV-ΔMGF110/360-9L-infected cells compared with parental ASFV-infected cells. Additionally, we show overexpression of TLR2 inhibited ASFV replication and the expression of ASFV p72 protein, whereas knockdown of TLR2 had the opposite effect. Our findings suggest that the attenuated virulence of ASFV-ΔMGF110/360-9L might be mediated by increased NF-κB and TLR2 signaling.
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Affiliation(s)
- Dan Li
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jingjing Ren
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Guoqiang Zhu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Panxue Wu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Wenping Yang
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Yi Ru
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Tao Feng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Huanan Liu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jing Zhang
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jiangling Peng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Hong Tian
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xiangtao Liu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Haixue Zheng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China.
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28
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An FP, Bai WD, Balantekin AB, Bishai M, Blyth S, Cao GF, Cao J, Chang JF, Chang Y, Chen HS, Chen HY, Chen SM, Chen Y, Chen YX, Cheng J, Cheng J, Cheng YC, Cheng ZK, Cherwinka JJ, Chu MC, Cummings JP, Dalager O, Deng FS, Ding YY, Diwan MV, Dohnal T, Dolzhikov D, Dove J, Dugas KV, Duyang HY, Dwyer DA, Gallo JP, Gonchar M, Gong GH, Gong H, Gu WQ, Guo JY, Guo L, Guo XH, Guo YH, Guo Z, Hackenburg RW, Han Y, Hans S, He M, Heeger KM, Heng YK, Hor YK, Hsiung YB, Hu BZ, Hu JR, Hu T, Hu ZJ, Huang HX, Huang JH, Huang XT, Huang YB, Huber P, Jaffe DE, Jen KL, Ji XL, Ji XP, Johnson RA, Jones D, Kang L, Kettell SH, Kohn S, Kramer M, Langford TJ, Lee J, Lee JHC, Lei RT, Leitner R, Leung JKC, Li F, Li HL, Li JJ, Li QJ, Li RH, Li S, Li SC, Li WD, Li XN, Li XQ, Li YF, Li ZB, Liang H, Lin CJ, Lin GL, Lin S, Ling JJ, Link JM, Littenberg L, Littlejohn BR, Liu JC, Liu JL, Liu JX, Lu C, Lu HQ, Luk KB, Ma BZ, Ma XB, Ma XY, Ma YQ, Mandujano RC, Marshall C, McDonald KT, McKeown RD, Meng Y, Napolitano J, Naumov D, Naumova E, Nguyen TMT, Ochoa-Ricoux JP, Olshevskiy A, Park J, Patton S, Peng JC, Pun CSJ, Qi FZ, Qi M, Qian X, Raper N, Ren J, Morales Reveco C, Rosero R, Roskovec B, Ruan XC, Russell B, Steiner H, Sun JL, Tmej T, Treskov K, Tse WH, Tull CE, Tung YC, Viren B, Vorobel V, Wang CH, Wang J, Wang M, Wang NY, Wang RG, Wang W, Wang X, Wang Y, Wang YF, Wang Z, Wang Z, Wang ZM, Wei HY, Wei LH, Wen LJ, Whisnant K, White CG, Wong HLH, Worcester E, Wu DR, Wu Q, Wu WJ, Xia DM, Xie ZQ, Xing ZZ, Xu HK, Xu JL, Xu T, Xue T, Yang CG, Yang L, Yang YZ, Yao HF, Ye M, Yeh M, Young BL, Yu HZ, Yu ZY, Yue BB, Zavadskyi V, Zeng S, Zeng Y, Zhan L, Zhang C, Zhang FY, Zhang HH, Zhang JL, Zhang JW, Zhang QM, Zhang SQ, Zhang XT, Zhang YM, Zhang YX, Zhang YY, Zhang ZJ, Zhang ZP, Zhang ZY, Zhao J, Zhao RZ, Zhou L, Zhuang HL, Zou JH. Improved Measurement of the Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay. Phys Rev Lett 2023; 130:211801. [PMID: 37295075 DOI: 10.1103/physrevlett.130.211801] [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: 10/03/2022] [Revised: 02/10/2023] [Accepted: 04/27/2023] [Indexed: 06/12/2023]
Abstract
Reactor neutrino experiments play a crucial role in advancing our knowledge of neutrinos. In this Letter, the evolution of the flux and spectrum as a function of the reactor isotopic content is reported in terms of the inverse-beta-decay yield at Daya Bay with 1958 days of data and improved systematic uncertainties. These measurements are compared with two signature model predictions: the Huber-Mueller model based on the conversion method and the SM2018 model based on the summation method. The measured average flux and spectrum, as well as the flux evolution with the ^{239}Pu isotopic fraction, are inconsistent with the predictions of the Huber-Mueller model. In contrast, the SM2018 model is shown to agree with the average flux and its evolution but fails to describe the energy spectrum. Altering the predicted inverse-beta-decay spectrum from ^{239}Pu fission does not improve the agreement with the measurement for either model. The models can be brought into better agreement with the measurements if either the predicted spectrum due to ^{235}U fission is changed or the predicted ^{235}U, ^{238}U, ^{239}Pu, and ^{241}Pu spectra are changed in equal measure.
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Affiliation(s)
- F P An
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - W D Bai
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - M Bishai
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Blyth
- Department of Physics, National Taiwan University, Taipei
| | - G F Cao
- Institute of High Energy Physics, Beijing
| | - J Cao
- Institute of High Energy Physics, Beijing
| | - J F Chang
- Institute of High Energy Physics, Beijing
| | - Y Chang
- National United University, Miao-Li
| | - H S Chen
- Institute of High Energy Physics, Beijing
| | - H Y Chen
- Department of Engineering Physics, Tsinghua University, Beijing
| | - S M Chen
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Y Chen
- Sun Yat-Sen (Zhongshan) University, Guangzhou
- Shenzhen University, Shenzhen
| | - Y X Chen
- North China Electric Power University, Beijing
| | - J Cheng
- North China Electric Power University, Beijing
| | - J Cheng
- North China Electric Power University, Beijing
| | - Y-C Cheng
- Department of Physics, National Taiwan University, Taipei
| | - Z K Cheng
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - M C Chu
- Chinese University of Hong Kong, Hong Kong
| | | | - O Dalager
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - F S Deng
- University of Science and Technology of China, Hefei
| | - Y Y Ding
- Institute of High Energy Physics, Beijing
| | - M V Diwan
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Dohnal
- Charles University, Faculty of Mathematics and Physics, Prague
| | - D Dolzhikov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - J Dove
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - K V Dugas
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | | | - D A Dwyer
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J P Gallo
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - M Gonchar
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - G H Gong
- Department of Engineering Physics, Tsinghua University, Beijing
| | - H Gong
- Department of Engineering Physics, Tsinghua University, Beijing
| | - W Q Gu
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Y Guo
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - L Guo
- Department of Engineering Physics, Tsinghua University, Beijing
| | - X H Guo
- Beijing Normal University, Beijing
| | - Y H Guo
- Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an
| | - Z Guo
- Department of Engineering Physics, Tsinghua University, Beijing
| | | | - Y Han
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - S Hans
- Brookhaven National Laboratory, Upton, New York 11973
| | - M He
- Institute of High Energy Physics, Beijing
| | - K M Heeger
- Wright Laboratory and Department of Physics, Yale University, New Haven, Connecticut 06520
| | - Y K Heng
- Institute of High Energy Physics, Beijing
| | - Y K Hor
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Y B Hsiung
- Department of Physics, National Taiwan University, Taipei
| | - B Z Hu
- Department of Physics, National Taiwan University, Taipei
| | - J R Hu
- Institute of High Energy Physics, Beijing
| | - T Hu
- Institute of High Energy Physics, Beijing
| | - Z J Hu
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - H X Huang
- China Institute of Atomic Energy, Beijing
| | - J H Huang
- Institute of High Energy Physics, Beijing
| | | | - Y B Huang
- Guangxi University, No. 100 Daxue East Road, Nanning
| | - P Huber
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - D E Jaffe
- Brookhaven National Laboratory, Upton, New York 11973
| | - K L Jen
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - X L Ji
- Institute of High Energy Physics, Beijing
| | - X P Ji
- Brookhaven National Laboratory, Upton, New York 11973
| | - R A Johnson
- Department of Physics, University of Cincinnati, Cincinnati, Ohio 45221
| | - D Jones
- Department of Physics, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122
| | - L Kang
- Dongguan University of Technology, Dongguan
| | - S H Kettell
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Kohn
- Department of Physics, University of California, Berkeley, California 94720
| | - M Kramer
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - T J Langford
- Wright Laboratory and Department of Physics, Yale University, New Haven, Connecticut 06520
| | - J Lee
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J H C Lee
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - R T Lei
- Dongguan University of Technology, Dongguan
| | - R Leitner
- Charles University, Faculty of Mathematics and Physics, Prague
| | - J K C Leung
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - F Li
- Institute of High Energy Physics, Beijing
| | - H L Li
- Institute of High Energy Physics, Beijing
| | - J J Li
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Q J Li
- Institute of High Energy Physics, Beijing
| | - R H Li
- Institute of High Energy Physics, Beijing
| | - S Li
- Dongguan University of Technology, Dongguan
| | - S C Li
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - W D Li
- Institute of High Energy Physics, Beijing
| | - X N Li
- Institute of High Energy Physics, Beijing
| | - X Q Li
- School of Physics, Nankai University, Tianjin
| | - Y F Li
- Institute of High Energy Physics, Beijing
| | - Z B Li
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - H Liang
- University of Science and Technology of China, Hefei
| | - C J Lin
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G L Lin
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - S Lin
- Dongguan University of Technology, Dongguan
| | - J J Ling
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - J M Link
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - L Littenberg
- Brookhaven National Laboratory, Upton, New York 11973
| | - B R Littlejohn
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - J C Liu
- Institute of High Energy Physics, Beijing
| | - J L Liu
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - J X Liu
- Institute of High Energy Physics, Beijing
| | - C Lu
- Joseph Henry Laboratories, Princeton University, Princeton, New Jersey 08544
| | - H Q Lu
- Institute of High Energy Physics, Beijing
| | - K B Luk
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
- The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - B Z Ma
- Shandong University, Jinan
| | - X B Ma
- North China Electric Power University, Beijing
| | - X Y Ma
- Institute of High Energy Physics, Beijing
| | - Y Q Ma
- Institute of High Energy Physics, Beijing
| | - R C Mandujano
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - C Marshall
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - K T McDonald
- Joseph Henry Laboratories, Princeton University, Princeton, New Jersey 08544
| | - R D McKeown
- California Institute of Technology, Pasadena, California 91125
- College of William and Mary, Williamsburg, Virginia 23187
| | - Y Meng
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - J Napolitano
- Department of Physics, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122
| | - D Naumov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - E Naumova
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - T M T Nguyen
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - J P Ochoa-Ricoux
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - A Olshevskiy
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - J Park
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - S Patton
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J C Peng
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - C S J Pun
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - F Z Qi
- Institute of High Energy Physics, Beijing
| | - M Qi
- Nanjing University, Nanjing
| | - X Qian
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Raper
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - J Ren
- China Institute of Atomic Energy, Beijing
| | - C Morales Reveco
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - R Rosero
- Brookhaven National Laboratory, Upton, New York 11973
| | - B Roskovec
- Charles University, Faculty of Mathematics and Physics, Prague
| | - X C Ruan
- China Institute of Atomic Energy, Beijing
| | - B Russell
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Steiner
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - J L Sun
- China General Nuclear Power Group, Shenzhen
| | - T Tmej
- Charles University, Faculty of Mathematics and Physics, Prague
| | - K Treskov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - W-H Tse
- Chinese University of Hong Kong, Hong Kong
| | - C E Tull
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Y C Tung
- Department of Physics, National Taiwan University, Taipei
| | - B Viren
- Brookhaven National Laboratory, Upton, New York 11973
| | - V Vorobel
- Charles University, Faculty of Mathematics and Physics, Prague
| | - C H Wang
- National United University, Miao-Li
| | - J Wang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - M Wang
- Shandong University, Jinan
| | - N Y Wang
- Beijing Normal University, Beijing
| | - R G Wang
- Institute of High Energy Physics, Beijing
| | - W Wang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
- College of William and Mary, Williamsburg, Virginia 23187
| | - X Wang
- College of Electronic Science and Engineering, National University of Defense Technology, Changsha
| | - Y Wang
- Nanjing University, Nanjing
| | - Y F Wang
- Institute of High Energy Physics, Beijing
| | - Z Wang
- Institute of High Energy Physics, Beijing
| | - Z Wang
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Z M Wang
- Institute of High Energy Physics, Beijing
| | - H Y Wei
- Brookhaven National Laboratory, Upton, New York 11973
| | - L H Wei
- Institute of High Energy Physics, Beijing
| | - L J Wen
- Institute of High Energy Physics, Beijing
| | | | - C G White
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - H L H Wong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - E Worcester
- Brookhaven National Laboratory, Upton, New York 11973
| | - D R Wu
- Institute of High Energy Physics, Beijing
| | - Q Wu
- Shandong University, Jinan
| | - W J Wu
- Institute of High Energy Physics, Beijing
| | - D M Xia
- Chongqing University, Chongqing
| | - Z Q Xie
- Institute of High Energy Physics, Beijing
| | - Z Z Xing
- Institute of High Energy Physics, Beijing
| | - H K Xu
- Institute of High Energy Physics, Beijing
| | - J L Xu
- Institute of High Energy Physics, Beijing
| | - T Xu
- Department of Engineering Physics, Tsinghua University, Beijing
| | - T Xue
- Department of Engineering Physics, Tsinghua University, Beijing
| | - C G Yang
- Institute of High Energy Physics, Beijing
| | - L Yang
- Dongguan University of Technology, Dongguan
| | - Y Z Yang
- Department of Engineering Physics, Tsinghua University, Beijing
| | - H F Yao
- Institute of High Energy Physics, Beijing
| | - M Ye
- Institute of High Energy Physics, Beijing
| | - M Yeh
- Brookhaven National Laboratory, Upton, New York 11973
| | - B L Young
- Iowa State University, Ames, Iowa 50011
| | - H Z Yu
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Z Y Yu
- Institute of High Energy Physics, Beijing
| | - B B Yue
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - V Zavadskyi
- Brookhaven National Laboratory, Upton, New York 11973
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - S Zeng
- Institute of High Energy Physics, Beijing
| | - Y Zeng
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - L Zhan
- Institute of High Energy Physics, Beijing
| | - C Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - F Y Zhang
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - H H Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - J W Zhang
- Institute of High Energy Physics, Beijing
| | - Q M Zhang
- Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an
| | - S Q Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - X T Zhang
- Institute of High Energy Physics, Beijing
| | - Y M Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Y X Zhang
- China General Nuclear Power Group, Shenzhen
| | - Y Y Zhang
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - Z J Zhang
- Dongguan University of Technology, Dongguan
| | - Z P Zhang
- University of Science and Technology of China, Hefei
| | - Z Y Zhang
- Institute of High Energy Physics, Beijing
| | - J Zhao
- Institute of High Energy Physics, Beijing
| | - R Z Zhao
- Institute of High Energy Physics, Beijing
| | - L Zhou
- Institute of High Energy Physics, Beijing
| | - H L Zhuang
- Institute of High Energy Physics, Beijing
| | - J H Zou
- Institute of High Energy Physics, Beijing
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Xiao ZJ, Ren J, Han Y, Shen F, Zheng JM, Qin WJ, Huan Y. [The anatomic zone localization based on biparametric MRI for the prediction of the risk degree of prostate cancer]. Zhonghua Yi Xue Za Zhi 2023; 103:1455-1460. [PMID: 37198107 DOI: 10.3760/cma.j.cn112137-20221219-02677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Objective: To investigate the anatomic zone localization based on biparametric magnetic resonance imaging (bpMRI) for the prediction of the risk degree in patients with prostate cancer. Methods: A total of 92 patients with prostate cancer confirmed by radical surgery in First Affiliated Hospital, Air Force Medical University, from January 2017 to December 2021 were collected. All patients underwent bpMRI (non-enhanced scan and DWI). According to ISUP grade, those patients were divided into low-risk group [≤grade 2, n=26, aged 71 (64.0, 5.2) years] and high-risk group[≥grade 3, n=66, aged 70.5 (63.0, 74.0) years]. The interobserver consistency test for ADC values was evaluated using the intraclass correlation coefficients (ICC). The differences in total prostate specific antigen (tPSA) between the two groups were compared and the χ2 test was used to compare the differences in the risk of prostate cancer in the transitional and peripheral zone. Independent correlation factors for prostate cancer risk were analyzed by logistic regression using high and low risk of prostate cancer as dependent variables, including factors such as anatomical zone, tPSA, apparent diffusion coefficient mean (ADCmean), apparent diffusion coefficient minimum (ADCmin) and age. Receiver operating characteristic (ROC) curves were plotted to assess the efficacy of the combined models of anatomical zone, tPSA, and anatomical partitioning+tPSA for diagnosing prostate cancer risk. Results: The ICC values of the ADCmean and ADCmin between the observers were 0.906 and 0.885, respectively, with good agreement. The tPSA in the low-risk group was lower than that in the high-risk group [19.64 (10.29, 35.18) ng/ml vs 72.42 (24.79, 187.98) ng/ml; P<0.001]; the risk of prostate cancer in the peripheral zone was higher than that in the transitional zone, and the difference was statistically significant (P<0.01). Multifactorial regression showed that anatomical zones (OR=0.120, 95%CI:0.029-0.501, P=0.004) and tPSA (OR=1.059, 95%CI:1.022-1.099, P=0.002) were risk factors for prostate cancer risk. The diagnostic efficacy of the combined model (AUC=0.895, 95%CI: 0.831-0.958) was better than the predictive efficacy of the single model for both anatomical partitioning (AUC=0.717, 95%CI:0.597-0.837) and tPSA (AUC=0.801, 95%CI: 0.714-0.887) (Z=3.91, 2.47; all P<0.05). Conclusions: The malignant degree of prostate cancer in peripheral zone was higher than that in transitional zone. Combination of anatomic zone located by bpMRI and tPSA can be used to predict the risk of prostate cancer before surgery, expected to provide support for patients to develop personalized treatment strategies.
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Affiliation(s)
- Z J Xiao
- Department of Radiology, First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - J Ren
- Department of Radiology, First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - Y Han
- Department of Radiology, First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - F Shen
- Department of Radiology, First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - J M Zheng
- Department of Radiology, First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - W J Qin
- Department of Urology, First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
| | - Y Huan
- Department of Radiology, First Affiliated Hospital, Air Force Medical University, Xi'an 710032, China
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Duan R, Ye K, Li Y, Sun Y, Zhu J, Ren J. Heart failure-related genes associated with oxidative stress and the immune landscape in lung cancer. Front Immunol 2023; 14:1167446. [PMID: 37275875 PMCID: PMC10232804 DOI: 10.3389/fimmu.2023.1167446] [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: 02/16/2023] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
Background Lung cancer is a common comorbidity of heart failure (HF). The early identification of the risk factors for lung cancer in patients with HF is crucial to early diagnosis and prognosis. Furthermore, oxidative stress and immune responses are the two critical biological processes shared by HF and lung cancer. Therefore, our study aimed to select the core genes in HF and then investigate the potential mechanisms underlying HF and lung cancer, including oxidative stress and immune responses through the selected genes. Methods Differentially expressed genes (DEGs) were analyzed for HF using datasets extracted from the Gene Expression Omnibus database. Functional enrichment analysis was subsequently performed. Next, weighted gene co-expression network analysis was performed to select the core gene modules. Support vector machine models, the random forest method, and the least absolute shrinkage and selection operator (LASSO) algorithm were applied to construct a multigene signature. The diagnostic values of the signature genes were measured using receiver operating characteristic curves. Functional analysis of the signature genes and immune landscape was performed using single-sample gene set enrichment analysis. Finally, the oxidative stress-related genes in these signature genes were identified and validated in vitro in lung cancer cell lines. Results The DEGs in the GSE57338 dataset were screened, and this dataset was then clustered into six modules using weighted gene co-expression network analysis; MEblue was significantly associated with HF (cor = -0.72, p < 0.001). Signature genes including extracellular matrix protein 2 (ECM2), methyltransferase-like 7B (METTL7B), meiosis-specific nuclear structural 1 (MNS1), and secreted frizzled-related protein 4 (SFRP4) were selected using support vector machine models, the LASSO algorithm, and the random forest method. The respective areas under the curve of the receiver operating characteristic curves of ECM2, METTL7B, MNS1, and SFRP4 were 0.939, 0.854, 0.941, and 0.926, respectively. Single-sample gene set enrichment analysis revealed significant differences in the immune landscape of the patients with HF and healthy subjects. Functional analysis also suggested that these signature genes may be involved in oxidative stress. In particular, METTL7B was highly expressed in lung cancer cell lines. Meanwhile, the correlation between METTL7B and oxidative stress was further verified using flow cytometry. Conclusion We identified that ECM2, METTL7B, MNS1, and SFRP4 exhibit remarkable diagnostic performance in patients with HF. Of note, METTL7B may be involved in the co-occurrence of HF and lung cancer by affecting the oxidative stress immune responses.
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Zhu G, Ren J, Li D, Ru Y, Qin X, Feng T, Tian H, Lu B, Shi D, Shi Z, Yang W, Zheng H. Combinational Deletions of MGF110-9L and MGF505-7R Genes from the African Swine Fever Virus Inhibit TBK1 Degradation by an Autophagy Activator PIK3C2B To Promote Type I Interferon Production. J Virol 2023; 97:e0022823. [PMID: 37162350 DOI: 10.1128/jvi.00228-23] [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] [Indexed: 05/11/2023] Open
Abstract
African swine fever (ASF), caused by the African swine fever virus (ASFV), is a transboundary infectious disease of domestic pigs and wild boars, resulting in significant swine production losses. Currently, no effective commercial ASF vaccines or therapeutic options are available. A previous study has shown that deletions of ASFV MGF110-9L and MGF505-7R genes (ASFV-Δ110-9L/505-7R) attenuated virulence in pigs and provided complete protection against parental lethal ASFV CN/GS/2018 (wild-type ASFV [ASFV-WT]) challenge, but the underlying mechanism is unclear. This study found that ASFV-Δ110-9L/505-7R weakened TBK1 degradation compared with ASFV-WT through RNA sequencing (RNA-seq) and Western blotting analyses. Furthermore, we confirmed that ASFV-Δ110-9L/505-7R blocked the degradation of TBK1 through the autophagy pathway. We also identified that the downregulation of an autophagy-related protein PIK3C2B was involved in the inhibition of TBK1 degradation induced by ASFV-Δ110-9L/505-7R. Additionally, we also confirmed that PIK3C2B promoted ASFV-Δ110-9L/505-7R replication in vitro. Together, this study elucidated a novel mechanism of virulence change of ASFV-Δ110-9L/505-7R, revealing a new mechanism of ASF live attenuated vaccines (LAVs) and providing theoretical guidance for the development of ASF vaccines. IMPORTANCE African swine fever (ASF) is a contagious and lethal hemorrhagic disease of pigs caused by the African swine fever virus (ASFV), leading to significant economic consequences for the global pig industry. The development of an effective and safe ASF vaccine has been unsuccessful. Previous studies have shown that live attenuated vaccines (LAVs) of ASFV are the most effective vaccine candidates to prevent ASF. Understanding the host responses caused by LAVs of ASFV is important in optimizing vaccine design and diversifying the resources available to control ASF. Recently, our laboratory found that the live attenuated ASFV-Δ110-9L/505-7R provided complete protection against parental ASFV-WT challenge. This study further demonstrated that ASFV-Δ110-9L/505-7R inhibits TBK1 degradation mediated by an autophagy activator PIK3C2B to increase type I interferon production. These results revealed an important mechanism for candidate vaccine ASFV-Δ110-9L/505-7R, providing strategies for exploring the virulence of multigene-deleted live attenuated ASFV strains and the development of vaccines.
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Affiliation(s)
- Guoqiang Zhu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China
| | - Jingjing Ren
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Dan Li
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Yi Ru
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Xiaodong Qin
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Tao Feng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Hong Tian
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Bingzhou Lu
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Dongfang Shi
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China
| | - Zhengwang Shi
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Wenping Yang
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Haixue Zheng
- State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China
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Duan R, Zhang Q, Ren J, Zhang X, Zhu J, Sun Y, Ye K, Li S, Liu Y, Wang L, Zhao M, Zhu L, Qiu Y, Ren W, Qin H, Chen M. The association between GLIM criteria defined malnutrition and 2-year unplanned hospital admission in outpatients with unintentional weight loss: A retrospective cohort study. JPEN J Parenter Enteral Nutr 2023. [PMID: 37094973 DOI: 10.1002/jpen.2506] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/10/2023] [Accepted: 04/16/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND This study aimed to assess malnutrition using the Global Leadership Initiative on Malnutrition (GLIM) criteria and Subjective Global Assessment (SGA) at baseline and determine the GLIM criteria that best predicted unplanned hospitalization in unintentional weight loss (UWL) outpatients. METHODS We performed a retrospective cohort study of 257 adult outpatients with UWL. The GLIM criteria and SGA agreement were reported using Cohen's kappa coefficient. Kaplan-Meier survival curves and adjusted Cox regression analyses were used for survival data. Logistic regression was used for the other correlation analysis. RESULTS This study collected data from 257 patients for 2 years. Based on the GLIM criteria and SGA, malnutrition prevalence was 79.0% and 72.0%, respectively (k = 0.728, P < 0.001). Using SGA as a standard, GLIM had a sensitivity of 97.8%, a specificity of 69.4%, a positive predictive value of 89.2%, and a negative predictive value of 92.6%. Malnutrition was associated with higher rates of unplanned hospital admission independent of other prognostic factors (GLIM: hazard ratio (HR) 2.85, 95% confidence interval (CI): 1.22 to 6.68; SGA: HR 2.07, 95% CI: 1.13 to 3.79). Of the five GLIM criteria-related diagnostic combinations, disease burden or inflammation was the most important to predict unplanned hospital admission in multivariable analysis (HR 3.27, 95% CI: 2.03 to 5.28). CONCLUSION There was good agreement between the GLIM criteria and SGA. GLIM-defined malnutrition, as well as all five GLIM criteria-related diagnosis combinations, had the potential to predict unplanned hospital admissions in UWL outpatients within 2 years. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ruoshu Duan
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Qi Zhang
- Department of Colorectal Surgery, Cancer Hospital of the University of Chinese Academy of Sciences/Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Jingjing Ren
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Xiaochen Zhang
- Department of Medical Oncology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Jiahong Zhu
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Yujing Sun
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Kangli Ye
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Shuai Li
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Ying Liu
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Lei Wang
- Department of Clinical Nutrition, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Min Zhao
- Department of Clinical Nutrition, The Six Medical Center of PLA General Hospital, Beijing, 100048, China
| | - Lu Zhu
- Health Management Center, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Yan Qiu
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Wen Ren
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Hongli Qin
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Mingmin Chen
- Department of General Practice, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
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An FP, Bai WD, Balantekin AB, Bishai M, Blyth S, Cao GF, Cao J, Chang JF, Chang Y, Chen HS, Chen HY, Chen SM, Chen Y, Chen YX, Chen ZY, Cheng J, Cheng ZK, Cherwinka JJ, Chu MC, Cummings JP, Dalager O, Deng FS, Ding YY, Ding XY, Diwan MV, Dohnal T, Dolzhikov D, Dove J, Duyang HY, Dwyer DA, Gallo JP, Gonchar M, Gong GH, Gong H, Gu WQ, Guo JY, Guo L, Guo XH, Guo YH, Guo Z, Hackenburg RW, Han Y, Hans S, He M, Heeger KM, Heng YK, Hor YK, Hsiung YB, Hu BZ, Hu JR, Hu T, Hu ZJ, Huang HX, Huang JH, Huang XT, Huang YB, Huber P, Jaffe DE, Jen KL, Ji XL, Ji XP, Johnson RA, Jones D, Kang L, Kettell SH, Kohn S, Kramer M, Langford TJ, Lee J, Lee JHC, Lei RT, Leitner R, Leung JKC, Li F, Li HL, Li JJ, Li QJ, Li RH, Li S, Li SC, Li WD, Li XN, Li XQ, Li YF, Li ZB, Liang H, Lin CJ, Lin GL, Lin S, Ling JJ, Link JM, Littenberg L, Littlejohn BR, Liu JC, Liu JL, Liu JX, Lu C, Lu HQ, Luk KB, Ma BZ, Ma XB, Ma XY, Ma YQ, Mandujano RC, Marshall C, McDonald KT, McKeown RD, Meng Y, Napolitano J, Naumov D, Naumova E, Nguyen TMT, Ochoa-Ricoux JP, Olshevskiy A, Pan HR, Park J, Patton S, Peng JC, Pun CSJ, Qi FZ, Qi M, Qian X, Raper N, Ren J, Morales Reveco C, Rosero R, Roskovec B, Ruan XC, Russell B, Steiner H, Sun JL, Tmej T, Treskov K, Tse WH, Tull CE, Viren B, Vorobel V, Wang CH, Wang J, Wang M, Wang NY, Wang RG, Wang W, Wang X, Wang Y, Wang YF, Wang Z, Wang Z, Wang ZM, Wei HY, Wei LH, Wei W, Wen LJ, Whisnant K, White CG, Wong HLH, Worcester E, Wu DR, Wu Q, Wu WJ, Xia DM, Xie ZQ, Xing ZZ, Xu HK, Xu JL, Xu T, Xue T, Yang CG, Yang L, Yang YZ, Yao HF, Ye M, Yeh M, Young BL, Yu HZ, Yu ZY, Yue BB, Zavadskyi V, Zeng S, Zeng Y, Zhan L, Zhang C, Zhang FY, Zhang HH, Zhang JL, Zhang JW, Zhang QM, Zhang SQ, Zhang XT, Zhang YM, Zhang YX, Zhang YY, Zhang ZJ, Zhang ZP, Zhang ZY, Zhao J, Zhao RZ, Zhou L, Zhuang HL, Zou JH. Precision Measurement of Reactor Antineutrino Oscillation at Kilometer-Scale Baselines by Daya Bay. Phys Rev Lett 2023; 130:161802. [PMID: 37154643 DOI: 10.1103/physrevlett.130.161802] [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: 12/01/2022] [Accepted: 02/24/2023] [Indexed: 05/10/2023]
Abstract
We present a new determination of the smallest neutrino mixing angle θ_{13} and the mass-squared difference Δm_{32}^{2} using a final sample of 5.55×10^{6} inverse beta-decay (IBD) candidates with the final-state neutron captured on gadolinium. This sample is selected from the complete dataset obtained by the Daya Bay reactor neutrino experiment in 3158 days of operation. Compared to the previous Daya Bay results, selection of IBD candidates has been optimized, energy calibration refined, and treatment of backgrounds further improved. The resulting oscillation parameters are sin^{2}2θ_{13}=0.0851±0.0024, Δm_{32}^{2}=(2.466±0.060)×10^{-3} eV^{2} for the normal mass ordering or Δm_{32}^{2}=-(2.571±0.060)×10^{-3} eV^{2} for the inverted mass ordering.
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Affiliation(s)
- F P An
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - W D Bai
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - M Bishai
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Blyth
- Department of Physics, National Taiwan University, Taipei
| | - G F Cao
- Institute of High Energy Physics, Beijing
| | - J Cao
- Institute of High Energy Physics, Beijing
| | - J F Chang
- Institute of High Energy Physics, Beijing
| | - Y Chang
- National United University, Miao-Li
| | - H S Chen
- Institute of High Energy Physics, Beijing
| | - H Y Chen
- Department of Engineering Physics, Tsinghua University, Beijing
| | - S M Chen
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Y Chen
- Sun Yat-Sen (Zhongshan) University, Guangzhou
- Shenzhen University, Shenzhen
| | - Y X Chen
- North China Electric Power University, Beijing
| | - Z Y Chen
- Institute of High Energy Physics, Beijing
| | - J Cheng
- North China Electric Power University, Beijing
| | - Z K Cheng
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - M C Chu
- Chinese University of Hong Kong, Hong Kong
| | | | - O Dalager
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - F S Deng
- University of Science and Technology of China, Hefei
| | - Y Y Ding
- Institute of High Energy Physics, Beijing
| | | | - M V Diwan
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Dohnal
- Charles University, Faculty of Mathematics and Physics, Prague
| | - D Dolzhikov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - J Dove
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | | | - D A Dwyer
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J P Gallo
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - M Gonchar
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - G H Gong
- Department of Engineering Physics, Tsinghua University, Beijing
| | - H Gong
- Department of Engineering Physics, Tsinghua University, Beijing
| | - W Q Gu
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Y Guo
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - L Guo
- Department of Engineering Physics, Tsinghua University, Beijing
| | - X H Guo
- Beijing Normal University, Beijing
| | - Y H Guo
- Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an
| | - Z Guo
- Department of Engineering Physics, Tsinghua University, Beijing
| | | | - Y Han
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - S Hans
- Brookhaven National Laboratory, Upton, New York 11973
| | - M He
- Institute of High Energy Physics, Beijing
| | - K M Heeger
- Wright Laboratory and Department of Physics, Yale University, New Haven, Connecticut 06520
| | - Y K Heng
- Institute of High Energy Physics, Beijing
| | - Y K Hor
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Y B Hsiung
- Department of Physics, National Taiwan University, Taipei
| | - B Z Hu
- Department of Physics, National Taiwan University, Taipei
| | - J R Hu
- Institute of High Energy Physics, Beijing
| | - T Hu
- Institute of High Energy Physics, Beijing
| | - Z J Hu
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - H X Huang
- China Institute of Atomic Energy, Beijing
| | - J H Huang
- Institute of High Energy Physics, Beijing
| | | | - Y B Huang
- Guangxi University, No.100 Daxue East Road, Nanning
| | - P Huber
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - D E Jaffe
- Brookhaven National Laboratory, Upton, New York 11973
| | - K L Jen
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - X L Ji
- Institute of High Energy Physics, Beijing
| | - X P Ji
- Brookhaven National Laboratory, Upton, New York 11973
| | - R A Johnson
- Department of Physics, University of Cincinnati, Cincinnati, Ohio 45221
| | - D Jones
- Department of Physics, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122
| | - L Kang
- Dongguan University of Technology, Dongguan
| | - S H Kettell
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Kohn
- Department of Physics, University of California, Berkeley, California 94720
| | - M Kramer
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - T J Langford
- Wright Laboratory and Department of Physics, Yale University, New Haven, Connecticut 06520
| | - J Lee
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J H C Lee
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - R T Lei
- Dongguan University of Technology, Dongguan
| | - R Leitner
- Charles University, Faculty of Mathematics and Physics, Prague
| | - J K C Leung
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - F Li
- Institute of High Energy Physics, Beijing
| | - H L Li
- Institute of High Energy Physics, Beijing
| | - J J Li
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Q J Li
- Institute of High Energy Physics, Beijing
| | - R H Li
- Institute of High Energy Physics, Beijing
| | - S Li
- Dongguan University of Technology, Dongguan
| | - S C Li
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - W D Li
- Institute of High Energy Physics, Beijing
| | - X N Li
- Institute of High Energy Physics, Beijing
| | - X Q Li
- School of Physics, Nankai University, Tianjin
| | - Y F Li
- Institute of High Energy Physics, Beijing
| | - Z B Li
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - H Liang
- University of Science and Technology of China, Hefei
| | - C J Lin
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G L Lin
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - S Lin
- Dongguan University of Technology, Dongguan
| | - J J Ling
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - J M Link
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - L Littenberg
- Brookhaven National Laboratory, Upton, New York 11973
| | - B R Littlejohn
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - J C Liu
- Institute of High Energy Physics, Beijing
| | - J L Liu
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - J X Liu
- Institute of High Energy Physics, Beijing
| | - C Lu
- Joseph Henry Laboratories, Princeton University, Princeton, New Jersey 08544
| | - H Q Lu
- Institute of High Energy Physics, Beijing
| | - K B Luk
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
- The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - B Z Ma
- Shandong University, Jinan
| | - X B Ma
- North China Electric Power University, Beijing
| | - X Y Ma
- Institute of High Energy Physics, Beijing
| | - Y Q Ma
- Institute of High Energy Physics, Beijing
| | - R C Mandujano
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - C Marshall
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - K T McDonald
- Joseph Henry Laboratories, Princeton University, Princeton, New Jersey 08544
| | - R D McKeown
- California Institute of Technology, Pasadena, California 91125
- College of William and Mary, Williamsburg, Virginia 23187
| | - Y Meng
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - J Napolitano
- Department of Physics, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122
| | - D Naumov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - E Naumova
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - T M T Nguyen
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - J P Ochoa-Ricoux
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - A Olshevskiy
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - H-R Pan
- Department of Physics, National Taiwan University, Taipei
| | - J Park
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - S Patton
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J C Peng
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - C S J Pun
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - F Z Qi
- Institute of High Energy Physics, Beijing
| | - M Qi
- Nanjing University, Nanjing
| | - X Qian
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Raper
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - J Ren
- China Institute of Atomic Energy, Beijing
| | - C Morales Reveco
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - R Rosero
- Brookhaven National Laboratory, Upton, New York 11973
| | - B Roskovec
- Charles University, Faculty of Mathematics and Physics, Prague
| | - X C Ruan
- China Institute of Atomic Energy, Beijing
| | - B Russell
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Steiner
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - J L Sun
- China General Nuclear Power Group, Shenzhen
| | - T Tmej
- Charles University, Faculty of Mathematics and Physics, Prague
| | - K Treskov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - W-H Tse
- Chinese University of Hong Kong, Hong Kong
| | - C E Tull
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B Viren
- Brookhaven National Laboratory, Upton, New York 11973
| | - V Vorobel
- Charles University, Faculty of Mathematics and Physics, Prague
| | - C H Wang
- National United University, Miao-Li
| | - J Wang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - M Wang
- Shandong University, Jinan
| | - N Y Wang
- Beijing Normal University, Beijing
| | - R G Wang
- Institute of High Energy Physics, Beijing
| | - W Wang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
- College of William and Mary, Williamsburg, Virginia 23187
| | - X Wang
- College of Electronic Science and Engineering, National University of Defense Technology, Changsha
| | - Y Wang
- Nanjing University, Nanjing
| | - Y F Wang
- Institute of High Energy Physics, Beijing
| | - Z Wang
- Institute of High Energy Physics, Beijing
| | - Z Wang
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Z M Wang
- Institute of High Energy Physics, Beijing
| | - H Y Wei
- Brookhaven National Laboratory, Upton, New York 11973
| | - L H Wei
- Institute of High Energy Physics, Beijing
| | - W Wei
- Shandong University, Jinan
| | - L J Wen
- Institute of High Energy Physics, Beijing
| | | | - C G White
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - H L H Wong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - E Worcester
- Brookhaven National Laboratory, Upton, New York 11973
| | - D R Wu
- Institute of High Energy Physics, Beijing
| | - Q Wu
- Shandong University, Jinan
| | - W J Wu
- Institute of High Energy Physics, Beijing
| | - D M Xia
- Chongqing University, Chongqing
| | - Z Q Xie
- Institute of High Energy Physics, Beijing
| | - Z Z Xing
- Institute of High Energy Physics, Beijing
| | - H K Xu
- Institute of High Energy Physics, Beijing
| | - J L Xu
- Institute of High Energy Physics, Beijing
| | - T Xu
- Department of Engineering Physics, Tsinghua University, Beijing
| | - T Xue
- Department of Engineering Physics, Tsinghua University, Beijing
| | - C G Yang
- Institute of High Energy Physics, Beijing
| | - L Yang
- Dongguan University of Technology, Dongguan
| | - Y Z Yang
- Department of Engineering Physics, Tsinghua University, Beijing
| | - H F Yao
- Institute of High Energy Physics, Beijing
| | - M Ye
- Institute of High Energy Physics, Beijing
| | - M Yeh
- Brookhaven National Laboratory, Upton, New York 11973
| | - B L Young
- Iowa State University, Ames, Iowa 50011
| | - H Z Yu
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Z Y Yu
- Institute of High Energy Physics, Beijing
| | - B B Yue
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - V Zavadskyi
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - S Zeng
- Institute of High Energy Physics, Beijing
| | - Y Zeng
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - L Zhan
- Institute of High Energy Physics, Beijing
| | - C Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - F Y Zhang
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - H H Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - J W Zhang
- Institute of High Energy Physics, Beijing
| | - Q M Zhang
- Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an
| | - S Q Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - X T Zhang
- Institute of High Energy Physics, Beijing
| | - Y M Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Y X Zhang
- China General Nuclear Power Group, Shenzhen
| | - Y Y Zhang
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - Z J Zhang
- Dongguan University of Technology, Dongguan
| | - Z P Zhang
- University of Science and Technology of China, Hefei
| | - Z Y Zhang
- Institute of High Energy Physics, Beijing
| | - J Zhao
- Institute of High Energy Physics, Beijing
| | - R Z Zhao
- Institute of High Energy Physics, Beijing
| | - L Zhou
- Institute of High Energy Physics, Beijing
| | - H L Zhuang
- Institute of High Energy Physics, Beijing
| | - J H Zou
- Institute of High Energy Physics, Beijing
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Fang L, Li G, Ren J, Duan J, Dong J, Liu Z. Integrated analysis for treatment scheme of sodium-glucose cotransporter 2 inhibitors in patients with diabetic kidney disease: a real-world study. Sci Rep 2023; 13:5969. [PMID: 37045938 PMCID: PMC10097684 DOI: 10.1038/s41598-023-33211-1] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 04/09/2023] [Indexed: 04/14/2023] Open
Abstract
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are recommended for type 2 diabetes mellitus patients with impaired renal function, but the actual situation of SGLT2i using is unclear. Therefore, in this real-world study, we analyzed the treatment scheme and clinical characteristics of SGLT2i in patients with diabetic kidney disease (DKD). We included DKD patients hospitalized in the First Affiliated Hospital of Zhengzhou University from October 2017 to March 2020. The Apriori algorithm of association rules was used to analysis treatment scheme prescribing SGLT2i and other different combinations of hypoglycemic drugs. SGLT2i was used in 781 (12.3%) of 6336 DKD patients, both number and proportion of patients using SGLT2i increased from 2017 to 2020 (1.9% to 33%). Nighty-eight percent of all DKD patients using SGLT2i were combined with other glucose-lowering agents, and insulin, metformin and alpha-glucosidase inhibitors are most commonly used in combination with hypoglycemic drugs. Multivariate analysis showed that compared with non-SGLT2i group, patients using SGLT2i were associated with younger age, higher BMI, higher HbA1c, preserved kidney function, dyslipidemia and combined with ACEI/ARB and statins. In this real-world study, use of SGLT2i in DKD patients is still low. Most patients performed younger age and in the early stages of chronic kidney disease with poor glycemic control. Clinical inertia should be overcome to fully exert the cardiorenal protective effects of SGLT2 inhibitors, with attention to rational drug use.
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Affiliation(s)
- Li Fang
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-Data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Guangpu Li
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-Data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingjing Ren
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-Data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiayu Duan
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China.
- Henan Province Research Center for Kidney Disease, Zhengzhou, China.
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China.
- Clinical Research Center of Big-Data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Jiancheng Dong
- Clinical Research Center of Big-Data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Zhangsuo Liu
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China.
- Henan Province Research Center for Kidney Disease, Zhengzhou, China.
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China.
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Qin H, Li S, Liu J, Ren J, Yu M. Follow-up survey of general practitioners in Zhejiang Province post-completion of position transition training in 2017-2020. BMC Med Educ 2023; 23:182. [PMID: 36964607 PMCID: PMC10038699 DOI: 10.1186/s12909-023-04151-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 03/11/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Position transition training for general practitioners in Zhejiang Province started in 2017 and has since been held once a year. By the beginning of 2022, four training sessions were completed. The purpose of this survey was to establish the current situation of trainees after their graduation and provide reference for the evaluation of the training effect. METHODS Of the 738 trainees who completed the training, 253 were contacted and followed up. A self-designed questionnaire was used to conduct the survey through online filling in. The content included questions to elucidate the following information: whereabouts after the training, registration as a general practitioner, undertaken general practice teaching and scientific research work, current occupational environment, improvement of post competence after receiving position transition training, willingness to complete survey, willingness to participate in future training programs, etc. RESULTS: A number of 253 valid questionnaires were collected with a recovery rate of 100%. Notably, 93.68% of the participants successfully completed their training and obtained the Training Certificate of General Practitioners. Further, 83.4% were registered as general practitioners, 82.94% of which added on the basis of the original registered scope of practice. Currently, most of them work in primary health care institutions, primarily occupied with medical treatment, chronic disease management, COVID-19 prevention and control, health education, and prevention and health care. Of them, 27.01% were currently undertaking teaching work, and only 3.32% of them were conducting scientific research work related to general practice. The overall satisfaction of the trainees in the three theoretical training bases was above 90%, with no statistically significant difference among them (P > 0.05). Importantly, 84.11% of the followed-up personnel hoped to continue to participate in similar training in the future to improve their general practitioner core competences. CONCLUSION The position transition training in Zhejiang Province has achieved good results, but the details of training and the implementation of policies in individual regions need to be improved. Most of the graduates were willing to continue their education, especially in general practitioners with special interests.
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Affiliation(s)
- Hongli Qin
- Department of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Shuai Li
- Department of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Juanjuan Liu
- Department of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Jingjing Ren
- Department of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
| | - Meiyue Yu
- Zhejiang University, Hangzhou, 310003, China
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Qiu Y, Ma J, Zhu J, Liu Y, Ren W, Zhang S, Ren J. Deaths and disability-adjusted life years of hypertension in China, South Korea, and Japan: A trend over the past 29 years. Front Cardiovasc Med 2023; 10:1080682. [PMID: 37008311 PMCID: PMC10050598 DOI: 10.3389/fcvm.2023.1080682] [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: 11/16/2022] [Accepted: 02/24/2023] [Indexed: 03/17/2023] Open
Abstract
BackgroundHypertension has been confirmed as an independent risk factor for cardiovascular disease and death. Few data were analyzed on deaths and disability-adjusted life years (DALYs) caused by hypertension in East Asia. We aimed to provide an overview of burden attributable to high blood pressure in China in the past 29 years, compared with those in Japan and South Korea.MethodsData were collected from the 2019 Global Burden of Disease study on diseases due to high systolic blood pressure (SBP). We retrieved the age-standardized mortality rate (ASMR) and DALYs rate (ASDR) by gender, age, location, and sociodemographic index. The death and DALY trends were evaluated by estimated annual percentage change, with 95% confidence interval.FindingsConsiderable differences were detected in the diseases attributable to high SBP in China, Japan, and South Korea. In 2019, the ASMR and ASDR of diseases due to high SBP in China were 153.34 (126.19, 182.49) per 100,000 population and 2,844.27 (2,391.91, 3,321.12) per 100,000 population, respectively, which was about 3.50-fold of those in another two countries. The elders and males had higher ASMR and ASDR in the three countries. Between 1990 and 2019, the declining trends were less pronounced in China for both the deaths and DALYs.ConclusionsThe deaths and DALYs due to hypertension declined in China, Japan, and South Korea in the past 29 years, with China having the greatest burden.
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Affiliation(s)
- Yan Qiu
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Correspondence: Yan Qiu Jingjing Ren
| | - Junzhuang Ma
- Department of General Practice, The First Division Hospital of Xinjiang Production and Construction Group, Aksu, China
| | - Jiahong Zhu
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ying Liu
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wen Ren
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shuaishuai Zhang
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jingjing Ren
- Department of General Practice, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Correspondence: Yan Qiu Jingjing Ren
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Xie M, Zhu Y, Wang X, Ren J, Guo H, Huang B, Wang S, Wang P, Liu Y, Liu Y, Zhang J. Predictive prognostic value of glomerular C3 deposition in IgA nephropathy. J Nephrol 2023; 36:495-505. [PMID: 35781866 DOI: 10.1007/s40620-022-01363-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [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: 02/28/2022] [Accepted: 05/23/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND IgAN is the most common primary glomerulonephritis worldwide. However, the pathogenesis of IgAN remains unknown. Currently, there is evidence that C3 deposition plays a role in disease development. This study aimed to investigate clinical, pathological features, and prognosis of adult IgAN patients with C3 deposition, as well as explore the role of complement activation in disease progression. METHODS A total of 821 patients with biopsy-proven IgAN were included in this study. Patients were divided into three different groups according to their C3 deposition intensity. Clinical and pathological characteristics were compared between groups. Logistic analysis was used to estimate the relationship between C3 deposition and the Oxford scoring system. Univariate and multivariate Cox proportional hazard regression models were used to analyze the effect of the presence of C3 deposits on the prognosis of patients with IgA nephropathy. Kaplan-Meier survival analysis was used to evaluate the cumulative incidence of renal progression between groups. RESULTS Patients with C3 deposition exhibited more severe clinical and pathological features and had a higher score according to the Oxford scoring system. With the increasing intensity of C3 deposition, patients present more hematuria, crescents, heavier interstitial inflammatory cell infiltration and a higher score on segmental sclerosis lesions. Logistic regression identified a positive relationship between C3 deposition and histopathology. Univariate and multivariate Cox regression indicated that C3 deposition was an independent risk factor for IgAN severity. The Kaplan-Meier survival curves indicated that patients with positive C3 deposition had a worse prognosis compared to those without C3 deposition. CONCLUSIONS Patients with positive glomerular C3 deposition presented with more severe clinical and histopathological characteristics and a higher score on the Oxford scoring system. With the increasing intensity of C3 deposition, IgAN patients were more likely to present with high level of microscopic hematuria, fibrous crescents, interstitial inflammatory cell infiltration, and a higher score on segmental sclerosis lesions. C3 deposition at the time of renal biopsy is likely an independent risk factor for IgA nephropathy severity and progression.
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Affiliation(s)
- Minhua Xie
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuze Zhu
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Xutong Wang
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Jingjing Ren
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Haonan Guo
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Bo Huang
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Shulei Wang
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Peiheng Wang
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Yiming Liu
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Yingchun Liu
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China
| | - Junjun Zhang
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, Henan, China.
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Ren J, Yang L, Pi C, Cui X, Wu Y. Rhodium(III)‐Catalyzed Divergent C−H Functionalization of
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‐Aryl Amidines with Iodonium Ylides: Access to Carbazolones and Zwitterionic Salts. Adv Synth Catal 2023. [DOI: 10.1002/adsc.202300173] [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] [Indexed: 03/07/2023]
Affiliation(s)
- J. Ren
- Henan Key Laboratory of Chemical Biology and Organic Chemistry Key Laboratory of Applied Chemistry of Henan Universities Green Catalysis Center and College of Chemistry Zhengzhou University Zhengzhou 450052 P. R. China
| | - L. Yang
- Henan Key Laboratory of Chemical Biology and Organic Chemistry Key Laboratory of Applied Chemistry of Henan Universities Green Catalysis Center and College of Chemistry Zhengzhou University Zhengzhou 450052 P. R. China
| | - C. Pi
- Henan Key Laboratory of Chemical Biology and Organic Chemistry Key Laboratory of Applied Chemistry of Henan Universities Green Catalysis Center and College of Chemistry Zhengzhou University Zhengzhou 450052 P. R. China
| | - X. Cui
- Henan Key Laboratory of Chemical Biology and Organic Chemistry Key Laboratory of Applied Chemistry of Henan Universities Green Catalysis Center and College of Chemistry Zhengzhou University Zhengzhou 450052 P. R. China
| | - Y. Wu
- Henan Key Laboratory of Chemical Biology and Organic Chemistry Key Laboratory of Applied Chemistry of Henan Universities Green Catalysis Center and College of Chemistry Zhengzhou University Zhengzhou 450052 P. R. China
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Ren J, Qu R, Rahman NT, Lewis JM, King ALO, Liao X, Mirza FN, Carlson KR, Huang Y, Gigante S, Evans B, Rajendran BK, Xu S, Wang G, Foss FM, Damsky W, Kluger Y, Krishnaswamy S, Girardi M. Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations. Blood Adv 2023; 7:445-457. [PMID: 35947128 PMCID: PMC9979716 DOI: 10.1182/bloodadvances.2022008168] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/05/2022] [Accepted: 07/20/2022] [Indexed: 02/07/2023] Open
Abstract
The incidence of cutaneous T-cell lymphoma (CTCL) increases with age, and blood involvement portends a worse prognosis. To advance our understanding of the development of CTCL and identify potential therapeutic targets, we performed integrative analyses of paired single-cell RNA and T-cell receptor (TCR) sequencing of peripheral blood CD4+ T cells from patients with CTCL to reveal disease-unifying features. The malignant CD4+ T cells of CTCL showed highly diverse transcriptomic profiles across patients, with most displaying a mature Th2 differentiation and T-cell exhaustion phenotype. TCR-CDR3 peptide prediction analysis suggested limited diversity between CTCL samples, consistent with a role for a common antigenic stimulus. Potential of heat diffusion for affinity-based trajectory embedding transition analysis identified putative precancerous circulating populations characterized by an intermediate stage of gene expression and mutation level between the normal CD4+ T cells and malignant CTCL cells. We further revealed the therapeutic potential of targeting CD82 and JAK that endow the malignant CTCL cells with survival and proliferation advantages.
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Affiliation(s)
- Jingjing Ren
- Department of Dermatology, Yale School of Medicine, New Haven, CT
| | - Rihao Qu
- Department of Immunobiology, Yale School of Medicine, New Haven, CT
- Department of Pathology, Yale School of Medicine, New Haven, CT
| | - Nur-Taz Rahman
- Bioinformatics Support Program, Cushing/Whitney Medical Library, Yale School of Medicine, New Haven, CT
| | - Julia M. Lewis
- Department of Dermatology, Yale School of Medicine, New Haven, CT
| | | | - Xiaofeng Liao
- Department of Pharmacology, Yale School of Medicine, Yale University, New Haven, CT
| | - Fatima N. Mirza
- Department of Dermatology, Yale School of Medicine, New Haven, CT
| | - Kacie R. Carlson
- Department of Dermatology, Yale School of Medicine, New Haven, CT
| | - Yaqing Huang
- Department of Pathology, Yale School of Medicine, New Haven, CT
| | - Scott Gigante
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT
| | - Benjamin Evans
- Yale Center for Research Computing, Yale University, New Haven, CT
| | | | - Suzanne Xu
- Department of Dermatology, Yale School of Medicine, New Haven, CT
| | - Guilin Wang
- Yale Center for Genome Analysis, Yale School of Medicine, New Haven, CT
| | - Francine M. Foss
- Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - William Damsky
- Department of Dermatology, Yale School of Medicine, New Haven, CT
- Department of Pathology, Yale School of Medicine, New Haven, CT
| | - Yuval Kluger
- Department of Pathology, Yale School of Medicine, New Haven, CT
| | | | - Michael Girardi
- Department of Dermatology, Yale School of Medicine, New Haven, CT
- Correspondence: Michael Girardi, Department of Dermatology, Yale University School of Medicine, 333 Cedar St, PO Box 208059, New Haven, CT 06520;
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Li J, Liu D, Ren J, Li G, Zhao Z, Zhao H, Yan Q, Duan J, Liu Z. Integrated analysis of RNA methylation regulators crosstalk and immune infiltration for predictive and personalized therapy of diabetic nephropathy. Hum Genomics 2023; 17:6. [PMID: 36765416 PMCID: PMC9912588 DOI: 10.1186/s40246-023-00457-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 02/04/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND RNA methylation is a widely known post-transcriptional regulation which exists in many cancer and immune system diseases. However, the potential role and crosstalk of five types RNA methylation regulators in diabetic nephropathy (DN) and immune microenvironment remain unclear. METHODS The mRNA expression of 37 RNA modification regulators and RNA modification regulators related genes were identified in 112 samples from 5 Gene Expression Omnibus datasets. Nonnegative Matrix Factorization clustering method was performed to determine RNA modification patterns. The ssGSEA algorithms and the expression of human leukocyte antigen were employed to assess the immune microenvironment characteristics. Risk model based on differentially expression genes responsible for the modification regulators was constructed to evaluate its predictive capability in DN patients. Furthermore, the results were validated by using immunofluorescence co-localizations and protein experiments in vitro. RESULTS We found 24 RNA methylation regulators were significant differently expressed in glomeruli in DN group compared with control group. Four methylation-related genes and six RNA regulators were introduced into riskScore model using univariate Logistic regression and integrated LASSO regression, which could precisely distinguish the DN and healthy individuals. Group with high-risk score was associated with high immune infiltration. Three distinct RNA modification patterns were identified, which has significant differences in immune microenvironment, biological pathway and eGFR. Validation analyses showed the METTL3, ADAR1, DNMT1 were upregulated whereas YTHDC1 was downregulated in DN podocyte cell lines comparing with cells cultured by the normal glucose. CONCLUSION Our study reveals that RNA methylation regulators and immune infiltration regulation play critical roles in the pathogenesis of DN. The bioinformatic analyses combine with verification in vitro could provide robust evidence for identification of predictive RNA methylation regulators in DN.
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Affiliation(s)
- Jia Li
- grid.207374.50000 0001 2189 3846Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,grid.412633.10000 0004 1799 0733Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052 People’s Republic of China ,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052 People’s Republic of China
| | - Dongwei Liu
- grid.207374.50000 0001 2189 3846Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,grid.412633.10000 0004 1799 0733Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052 People’s Republic of China ,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052 People’s Republic of China
| | - Jingjing Ren
- grid.207374.50000 0001 2189 3846Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,grid.412633.10000 0004 1799 0733Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052 People’s Republic of China ,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052 People’s Republic of China
| | - Guangpu Li
- grid.207374.50000 0001 2189 3846Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,grid.412633.10000 0004 1799 0733Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052 People’s Republic of China ,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052 People’s Republic of China
| | - Zihao Zhao
- grid.207374.50000 0001 2189 3846Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,grid.412633.10000 0004 1799 0733Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052 People’s Republic of China ,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052 People’s Republic of China
| | - Huanhuan Zhao
- grid.207374.50000 0001 2189 3846Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,grid.412633.10000 0004 1799 0733Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052 People’s Republic of China ,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052 People’s Republic of China
| | - Qianqian Yan
- grid.207374.50000 0001 2189 3846Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,grid.412633.10000 0004 1799 0733Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 People’s Republic of China ,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052 People’s Republic of China ,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052 People’s Republic of China
| | - Jiayu Duan
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052, People's Republic of China. .,Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People's Republic of China. .,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052, People's Republic of China. .,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052, People's Republic of China.
| | - Zhangsuo Liu
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052, People's Republic of China. .,Traditional Chinese Medicine Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People's Republic of China. .,Henan Province Research Center for Kidney Disease, Zhengzhou, 450052, People's Republic of China. .,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052, People's Republic of China.
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Wei DN, Mi YL, Feng JN, Ren J. [Different rapid maxillary expansion methods in the treatment of adult patients with obstructive sleep apnea hypopnea syndrome]. Zhonghua Kou Qiang Yi Xue Za Zhi 2023; 58:196-200. [PMID: 36746455 DOI: 10.3760/cma.j.cn112144-20220825-00460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common sleep respiratory disorder characterized by upper respiratory collapse during sleep, with a high prevalence and potentially fatal complications. Currently, maxillary transverse deficiency are considered to be an important pathogenic factor of OSAHS. For patients with poor compliance with positive airway pressure therapy, rapid maxillary expansion can increase the volume and ventilation of the upper respiratory tract, which is an alternative treatment. This paper reviewed the current research on surgically assisted rapid palatal expansion, miniscrew assisted rapid palatal expansion, and distraction osteogenesis maxillary expansion in the treatment of adult OSAHS. By comparing the indications, contraindications, complications, efficacy and long-term stability of the three treatment methods, it provided reference for treatment of patients with OSAHS.
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Affiliation(s)
- D N Wei
- Department of Orthodontics, Shanxi Medical University School and Hospital of Stomatology, Taiyuan 030001, China
| | - Y L Mi
- Department of Orthodontics, Shanxi Medical University School and Hospital of Stomatology, Taiyuan 030001, China
| | - J N Feng
- Department of Orthodontics, Shanxi Medical University School and Hospital of Stomatology, Taiyuan 030001, China
| | - J Ren
- Department of Orthodontics, Shanxi Medical University School and Hospital of Stomatology, Taiyuan 030001, China
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Lan S, Yang Z, Ren J, Cheng K, Shen S, Cao L, Wang D. Fluorescence Properties of EDTA Carbon-Dots and Its Application in Iron Ions Detection. RUSS J GEN CHEM+ 2023. [DOI: 10.1134/s1070363223020238] [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: 04/03/2023]
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Li S, Pan X, Wu Y, Tu Y, Hong W, Ren J, Miao J, Wang T, Xia W, Lu J, Chen J, Hu X, Lin Y, Zhang X, Wang X. IL-37 alleviates intervertebral disc degeneration via the IL-1R8/NF-κB pathway. Osteoarthritis Cartilage 2023; 31:588-599. [PMID: 36693558 DOI: 10.1016/j.joca.2023.01.006] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 01/04/2023] [Accepted: 01/12/2023] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Intervertebral disc degeneration (IDD) has been reported to be a major cause of low back pain (LBP). Interleukin (IL)-37 is an anti-inflammatory cytokine of the interleukin-1 family, which exerts salutary physiological effects. In this study, we assessed the protective effect of IL-37 on IDD progression and its underlying mechanisms. METHODS Immunofluorescence (IF) was conducted to measure IL-37 expression in nucleus pulposus tissues. CCK-8 assay and Edu staining were used to examine the vitality of IL-37-treated nucleus pulposus cells (NPCs). Western blot, qPCR, ELISA as well as immunohistochemistry were used to assess senescence associated secreted phenotype (SASP) factors expression; and NF-κB pathway was evaluated by western blot and IF; while IL-1R8 knock-down by siRNAs was performed to ascertain its significance in the senescence phenotype modulated by IL-37. The therapeutic effect of IL-37 on IDD were evaluated in puncture-induced rat model using X-ray, Hematoxylin-Eosin, Safranin O-Fast Green (SO), and alcian blue staining. RESULTS We found IL-37 expression decreased in the IDD process. In vitro, IL-37 suppressed SASP factors level and senescence phenotype in IL-1β treated NPCs. In vivo, IL-37 alleviated the IDD progression in the puncture-induced rat model. Mechanistic studies demonstrated that IL-37 inhibited IDD progression by downregulating NF-κB pathway activation in NPCs by activating IL-1R8. CONCLUSION The present study suggests that IL-37 delays the IDD development through the IL-1R8/NF-κB pathway, which suggests IL-37 as a promising novel target for IDD therapy.
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Affiliation(s)
- S Li
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - X Pan
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Y Wu
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Y Tu
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - W Hong
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - J Ren
- Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The First School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - J Miao
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - T Wang
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - W Xia
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - J Lu
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - J Chen
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - X Hu
- Department of Orthopaedics, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang Province, China
| | - Y Lin
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
| | - X Zhang
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
| | - X Wang
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China; Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, Zhejiang Province, China; The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
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Dong M, Hu N, Hua Y, Xu X, Kandadi M, Guo R, Jiang S, Nair S, Hu D, Ren J. Erratum to: “Chronic Akt activation attenuated lipopolysaccharide-induced cardiac dysfunction via Akt/GSK3β-dependent inhibition of apoptosis and ER stress” [Biochim. Biophys. Acta. 1832(6) 2013 Jun; 848–63. doi:10.1016/j.bbadis.2013.02.023. Epub 2013 Mar 6.PMID: 23474308]. Biochim Biophys Acta Mol Basis Dis 2023; 1869:166567. [DOI: 10.1016/j.bbadis.2022.166567] [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: 11/07/2022]
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Duran J, Donovan D, Nichols J, Unterberg E, Zamperini S, Abrams T, Perillo R, Ren J, Rudakov D, Shafer M, Stangeby P, Taussig D, Wilcox R, Zach M. 13C surface characterization of midplane and crown collector probes on DIII-D. Nuclear Materials and Energy 2022. [DOI: 10.1016/j.nme.2022.101339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Duan X, Li H, Kuang D, Zhang M, Xu W, Liang C, Wang J, Ren J. 143P Efficacy and safety of bronchial arterial chemoembolization (BACE) in combination with tislelizumab for non-small cell lung cancer (NSCLC): A single-arm phase II trial. Immuno-Oncology and Technology 2022. [DOI: 10.1016/j.iotech.2022.100255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Ren W, Wu Z, Liu Y, Qiu Y, Yao J, Ren J. Evaluation of the effect of enhanced immunization in adults: A cross-sectional study in the southeast city of China. Hum Vaccin Immunother 2022; 18:2096972. [PMID: 35878394 DOI: 10.1080/21645515.2022.2096972] [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] [Indexed: 12/15/2022] Open
Abstract
The efficacy of hepatitis B vaccination in adults was evaluated by comparison of the positive seroprotection rates and the hepatitis B surface antibody (anti-HBs) geometric mean titers (GMTs) between intensive intervention areas and non-intensive intervention areas after 8 years post-vaccination in the Zhejiang province. Seven cities (towns) in Zhejiang province were selected as intensive intervention areas, and adults in the demonstration areas receive hepatitis B vaccine voluntarily and for free. Other areas were non-intensive intervention areas. A total of 3587 participants received the full vaccination course (three doses), and blood samples were withdrawn 8 years after the first vaccination comprised the immunized group, and 2000 participants constituted the control group. The anti-HBs positive seroprotection rates of the immunized and control groups were 65.0% and 53.0%, respectively. The anti-HBs GMT of the subjects in the immunized group was 26.30 mIU/mL compared to 9.33 mIU/mL in the control group (P < .001). Significant differences were detected in the 24-35-, 36-45-, and 46-55-year-old subgroups in the positive seroprotection rates and the anti-HBs GMTs (P < .001) between the immunized and control groups. Moreover, significant differences were found in the anti-HBs GMT in the 46-55-year-old subgroup between the two groups (P = .02), while no differences were observed in the positive seroprotection rate (P = .428). In conclusion, adults who did not receive the hepatitis B vaccine in infancy and had negative serological markers of hepatitis B, especially adults <47-years-old, need vaccination.
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Affiliation(s)
- Wen Ren
- The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zikang Wu
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ying Liu
- The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yan Qiu
- The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jun Yao
- Department of Immunology, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Jingjing Ren
- The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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Zhang ZY, Yang LT, Yue Q, Kang KJ, Li YJ, Agartioglu M, An HP, Chang JP, Chen YH, Cheng JP, Dai WH, Deng Z, Fang CH, Geng XP, Gong H, Guo QJ, Guo XY, He L, He SM, Hu JW, Huang HX, Huang TC, Jia HT, Jiang X, Li HB, Li JM, Li J, Li QY, Li RMJ, Li XQ, Li YL, Liang YF, Liao B, Lin FK, Lin ST, Liu SK, Liu YD, Liu Y, Liu YY, Liu ZZ, Ma H, Mao YC, Nie QY, Ning JH, Pan H, Qi NC, Ren J, Ruan XC, Saraswat K, Sharma V, She Z, Singh MK, Sun TX, Tang CJ, Tang WY, Tian Y, Wang GF, Wang L, Wang Q, Wang Y, Wang YX, Wong HT, Wu SY, Wu YC, Xing HY, Xu R, Xu Y, Xue T, Yan YL, Yeh CH, Yi N, Yu CX, Yu HJ, Yue JF, Zeng M, Zeng Z, Zhang BT, Zhang FS, Zhang L, Zhang ZH, Zhao KK, Zhao MG, Zhou JF, Zhou ZY, Zhu JJ. Constraints on Sub-GeV Dark Matter-Electron Scattering from the CDEX-10 Experiment. Phys Rev Lett 2022; 129:221301. [PMID: 36493436 DOI: 10.1103/physrevlett.129.221301] [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] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/25/2022] [Accepted: 10/20/2022] [Indexed: 06/17/2023]
Abstract
We present improved germanium-based constraints on sub-GeV dark matter via dark matter-electron (χ-e) scattering using the 205.4 kg·day dataset from the CDEX-10 experiment. Using a novel calculation technique, we attain predicted χ-e scattering spectra observable in high-purity germanium detectors. In the heavy mediator scenario, our results achieve 3 orders of magnitude of improvement for m_{χ} larger than 80 MeV/c^{2} compared to previous germanium-based χ-e results. We also present the most stringent χ-e cross-section limit to date among experiments using solid-state detectors for m_{χ} larger than 90 MeV/c^{2} with heavy mediators and m_{χ} larger than 100 MeV/c^{2} with electric dipole coupling. The result proves the feasibility and demonstrates the vast potential of a new χ-e detection method with high-purity germanium detectors in ultralow radioactive background.
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Affiliation(s)
- Z Y Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - L T Yang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Yue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K J Kang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - M Agartioglu
- Institute of Physics, Academia Sinica, Taipei 11529
| | - H P An
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | | | - Y H Chen
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J P Cheng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - W H Dai
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Deng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C H Fang
- College of Physics, Sichuan University, Chengdu 610065
| | - X P Geng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Gong
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q J Guo
- School of Physics, Peking University, Beijing 100871
| | - X Y Guo
- YaLong River Hydropower Development Company, Chengdu 610051
| | - L He
- NUCTECH Company, Beijing 100084
| | - S M He
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J W Hu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H X Huang
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - T C Huang
- Sino-French Institute of Nuclear and Technology, Sun Yat-sen University, Zhuhai 519082
| | - H T Jia
- College of Physics, Sichuan University, Chengdu 610065
| | - X Jiang
- College of Physics, Sichuan University, Chengdu 610065
| | - H B Li
- Institute of Physics, Academia Sinica, Taipei 11529
| | - J M Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Y Li
- College of Physics, Sichuan University, Chengdu 610065
| | - R M J Li
- College of Physics, Sichuan University, Chengdu 610065
| | - X Q Li
- School of Physics, Nankai University, Tianjin 300071
| | - Y L Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y F Liang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B Liao
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - F K Lin
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S T Lin
- College of Physics, Sichuan University, Chengdu 610065
| | - S K Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y D Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Y Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y Y Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Z Z Liu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Ma
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y C Mao
- School of Physics, Peking University, Beijing 100871
| | - Q Y Nie
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J H Ning
- YaLong River Hydropower Development Company, Chengdu 610051
| | - H Pan
- NUCTECH Company, Beijing 100084
| | - N C Qi
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J Ren
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - X C Ruan
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - K Saraswat
- Institute of Physics, Academia Sinica, Taipei 11529
| | - V Sharma
- Institute of Physics, Academia Sinica, Taipei 11529
- Department of Physics, Banaras Hindu University, Varanasi 221005, India
| | - Z She
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - M K Singh
- Institute of Physics, Academia Sinica, Taipei 11529
- Department of Physics, Banaras Hindu University, Varanasi 221005, India
| | - T X Sun
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - C J Tang
- College of Physics, Sichuan University, Chengdu 610065
| | - W Y Tang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Tian
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - G F Wang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Wang
- Department of Physics, Beijing Normal University, Beijing 100875
| | - Q Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y X Wang
- School of Physics, Peking University, Beijing 100871
| | - H T Wong
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S Y Wu
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Y C Wu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Y Xing
- College of Physics, Sichuan University, Chengdu 610065
| | - R Xu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Xu
- School of Physics, Nankai University, Tianjin 300071
| | - T Xue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y L Yan
- College of Physics, Sichuan University, Chengdu 610065
| | - C H Yeh
- Institute of Physics, Academia Sinica, Taipei 11529
| | - N Yi
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C X Yu
- School of Physics, Nankai University, Tianjin 300071
| | - H J Yu
- NUCTECH Company, Beijing 100084
| | - J F Yue
- YaLong River Hydropower Development Company, Chengdu 610051
| | - M Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B T Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - F S Zhang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Zhang
- College of Physics, Sichuan University, Chengdu 610065
| | - Z H Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K K Zhao
- College of Physics, Sichuan University, Chengdu 610065
| | - M G Zhao
- School of Physics, Nankai University, Tianjin 300071
| | - J F Zhou
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Z Y Zhou
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - J J Zhu
- College of Physics, Sichuan University, Chengdu 610065
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Dai WH, Jia LP, Ma H, Yue Q, Kang KJ, Li YJ, An HP, C G, Chang JP, Chen YH, Cheng JP, Deng Z, Fang CH, Geng XP, Gong H, Guo QJ, Guo XY, He L, He SM, Hu JW, Huang HX, Huang TC, Jia HT, Jiang X, Karmakar S, Li HB, Li JM, Li J, Li QY, Li RMJ, Li XQ, Li YL, Liang YF, Liao B, Lin FK, Lin ST, Liu SK, Liu YD, Liu Y, Liu YY, Liu ZZ, Mao YC, Nie QY, Ning JH, Pan H, Qi NC, Ren J, Ruan XC, She Z, Singh MK, Sun TX, Tang CJ, Tang WY, Tian Y, Wang GF, Wang L, Wang Q, Wang Y, Wang YX, Wong HT, Wu SY, Wu YC, Xing HY, Xu R, Xu Y, Xue T, Yan YL, Yang LT, Yi N, Yu CX, Yu HJ, Yue JF, Zeng M, Zeng Z, Zhang BT, Zhang FS, Zhang L, Zhang ZH, Zhang ZY, Zhao KK, Zhao MG, Zhou JF, Zhou ZY, Zhu JJ. Exotic Dark Matter Search with the CDEX-10 Experiment at China's Jinping Underground Laboratory. Phys Rev Lett 2022; 129:221802. [PMID: 36493447 DOI: 10.1103/physrevlett.129.221802] [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] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
A search for exotic dark matter (DM) in the sub-GeV mass range has been conducted using 205 kg day data taken from a p-type point contact germanium detector of the CDEX-10 experiment at China's Jinping underground laboratory. New low-mass dark matter searching channels, neutral current fermionic DM absorption (χ+A→ν+A) and DM-nucleus 3→2 scattering (χ+χ+A→ϕ+A), have been analyzed with an energy threshold of 160 eVee. No significant signal was found; thus new limits on the DM-nucleon interaction cross section are set for both models at the sub-GeV DM mass region. A cross section limit for the fermionic DM absorption is set to be 2.5×10^{-46} cm^{2} (90% C.L.) at DM mass of 10 MeV/c^{2}. For the DM-nucleus 3→2 scattering scenario, limits are extended to DM mass of 5 and 14 MeV/c^{2} for the massless dark photon and bound DM final state, respectively.
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Affiliation(s)
- W H Dai
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - L P Jia
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Ma
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Yue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K J Kang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H P An
- Department of Physics, Tsinghua University, Beijing 100084
| | - Greeshma C
- Institute of Physics, Academia Sinica, Taipei 11529
| | | | - Y H Chen
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J P Cheng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Z Deng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C H Fang
- College of Physics, Sichuan University, Chengdu 610065
| | - X P Geng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Gong
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q J Guo
- School of Physics, Peking University, Beijing 100871
| | - X Y Guo
- YaLong River Hydropower Development Company, Chengdu 610051
| | - L He
- NUCTECH Company, Beijing 100084
| | - S M He
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J W Hu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H X Huang
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - T C Huang
- Sino-French Institute of Nuclear and Technology, Sun Yat-sen University, Zhuhai 519082
| | - H T Jia
- College of Physics, Sichuan University, Chengdu 610065
| | - X Jiang
- College of Physics, Sichuan University, Chengdu 610065
| | - S Karmakar
- Institute of Physics, Academia Sinica, Taipei 11529
| | - H B Li
- Institute of Physics, Academia Sinica, Taipei 11529
| | - J M Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Y Li
- College of Physics, Sichuan University, Chengdu 610065
| | - R M J Li
- College of Physics, Sichuan University, Chengdu 610065
| | - X Q Li
- School of Physics, Nankai University, Tianjin 300071
| | - Y L Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y F Liang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B Liao
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - F K Lin
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S T Lin
- College of Physics, Sichuan University, Chengdu 610065
| | - S K Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y D Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Y Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y Y Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Z Z Liu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y C Mao
- School of Physics, Peking University, Beijing 100871
| | - Q Y Nie
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J H Ning
- YaLong River Hydropower Development Company, Chengdu 610051
| | - H Pan
- NUCTECH Company, Beijing 100084
| | - N C Qi
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J Ren
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - X C Ruan
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - Z She
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - M K Singh
- Institute of Physics, Academia Sinica, Taipei 11529
- Department of Physics, Banaras Hindu University, Varanasi 221005
| | - T X Sun
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - C J Tang
- College of Physics, Sichuan University, Chengdu 610065
| | - W Y Tang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Tian
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - G F Wang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Wang
- Department of Physics, Beijing Normal University, Beijing 100875
| | - Q Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y X Wang
- School of Physics, Peking University, Beijing 100871
| | - H T Wong
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S Y Wu
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Y C Wu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Y Xing
- College of Physics, Sichuan University, Chengdu 610065
| | - R Xu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Xu
- School of Physics, Nankai University, Tianjin 300071
| | - T Xue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y L Yan
- College of Physics, Sichuan University, Chengdu 610065
| | - L T Yang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - N Yi
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C X Yu
- School of Physics, Nankai University, Tianjin 300071
| | - H J Yu
- NUCTECH Company, Beijing 100084
| | - J F Yue
- YaLong River Hydropower Development Company, Chengdu 610051
| | - M Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B T Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - F S Zhang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Zhang
- College of Physics, Sichuan University, Chengdu 610065
| | - Z H Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Y Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K K Zhao
- College of Physics, Sichuan University, Chengdu 610065
| | - M G Zhao
- School of Physics, Nankai University, Tianjin 300071
| | - J F Zhou
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Z Y Zhou
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - J J Zhu
- College of Physics, Sichuan University, Chengdu 610065
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50
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Qin H, Qiu Y, Ying M, Ren J. Evaluation of the health promotion effect of hepatitis B prevention and treatment in the Zhejiang demonstration area, China. BMC Public Health 2022; 22:2073. [PMCID: PMC9661761 DOI: 10.1186/s12889-022-14540-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 11/03/2022] [Indexed: 11/16/2022] Open
Abstract
Abstract
Background
To investigate the health literacy level and health promotion effect of hepatitis B prevention and treatment in the demonstration area of Zhejiang Province.
Methods
The National Science and Technology Major Health Education Group took 6 demonstration areas in Zhejiang Province as the whole research site. After the sample size (N=2160 people) was determined, a multistage stratified cluster sampling method was used to conduct a questionnaire survey in 2018 (before health education) and 2019 (after health education). Stata 12 statistical software was used to analyse the status and improvement rate of hepatitis B health literacy of residents in the demonstration area before and after health education and compare the health promotion effects of different health intervention methods.
Results
Before and after health education, there was no significant difference in the basic information of the subjects (P>0.05). After the health education intervention, the level of hepatitis B health literacy of residents in the demonstration area increased by 11.8%, and the difference was statistically significant (P < 0.001). The awareness rate of hepatitis B transmission was low before health education but increased after health education. The form of "Internet +" health education may better improve the residents' health literacy level about hepatitis B prevention and treatment.
Conclusion
After health education, the level of health literacy of residents in the Zhejiang demonstration area about hepatitis B prevention and control significantly improved, but there is room for further improvement. In the future, targeted health education intervention should be carried out, and the health education mode of "Internet +" can achieve better results to effectively prevent and control hepatitis B.
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