1.Evolution and genetic variation of HA and NA genes of H1N1 influenza virus in Shanghai, 2024
Lufang JIANG ; Wei CHU ; Xuefei QIAO ; Pan SUN ; Senmiao DENG ; Yuxi WANG ; Xue ZHAO ; Jiasheng XIONG ; Xihong LYU ; Linjuan DONG ; Yaxu ZHENG ; Yinzi CHEN ; Chenyan JIANG ; Chenglong XIONG ; Jian CHEN
Shanghai Journal of Preventive Medicine 2025;37(9):719-724
ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.
2.Expert consensus on clinical application of 177Lu-prostate specific membrane antigen radio-ligand therapy in prostate cancer
Guobing LIU ; Weihai ZHUO ; Yushen GU ; Zhi YANG ; Yue CHEN ; Wei FAN ; Jianming GUO ; Jian TAN ; Xiaohua ZHU ; Li HUO ; Xiaoli LAN ; Biao LI ; Weibing MIAO ; Shaoli SONG ; Hao XU ; Rong TIAN ; Quanyong LUO ; Feng WANG ; Xuemei WANG ; Aimin YANG ; Dong DAI ; Zhiyong DENG ; Jinhua ZHAO ; Xiaoliang CHEN ; Yan FAN ; Zairong GAO ; Xingmin HAN ; Ningyi JIANG ; Anren KUANG ; Yansong LIN ; Fugeng LIU ; Cen LOU ; Xinhui SU ; Lijun TANG ; Hui WANG ; Xinlu WANG ; Fuzhou YANG ; Hui YANG ; Xinming ZHAO ; Bo YANG ; Xiaodong HUANG ; Jiliang CHEN ; Sijin LI ; Jing WANG ; Yaming LI ; Hongcheng SHI
Chinese Journal of Clinical Medicine 2024;31(5):844-850,封3
177Lu-prostate specific membrane antigen(PSMA)radio-ligand therapy has been approved abroad for advanced prostate cancer and has been in several clinical trials in China.Based on domestic clinical practice and experimental data and referred to international experience and viewpoints,the expert group forms a consensus on the clinical application of 177Lu-PSMA radio-ligand therapy in prostate cancer to guide clinical practice.
3.Analgesic effect of dezocine combined with ropivacaine on patients undergoing thoracoscopic radical resection of lung cancer
Zhi-Guo YI ; Wen ZHOU ; Yan-Ping SU ; Fang TANG ; Jian-Dong DENG
The Chinese Journal of Clinical Pharmacology 2024;40(8):1116-1120
Objective To explore the analgesic effect of different doses of dezocine combined with ropivacaine for thoracic paravertebral block(TPVB)on patients undergoing thoracoscopic radical resection of lung cancer and the influence on hemodynamics and immune function of patients.Methods Patients with lung cancer who underwent thoracoscopic radical resection were divided into low-dose group and high-dose group according to random number table method.Both groups of patients were given total intravenous anesthesia to complete the surgery.At 15 min before general anesthesia induction,the low-dose group was given TPBV with 0.1 mg·kg-1 dezocine+0.375%ropivacaine for a total of 20 mL,and the high-dose group was given TPBV with 0.15 mg·kg-1 dezocine+0.375%ropivacaine for a total of 20 mL.Comparisons were performed on both groups in terms of analgesic effect,hemodynamic parameters,immune function and occurrence of adverse drug reactions.Results There were 48 cases in low-dose group and 46 cases in high-dose group.In low-dose group,the heart rate values before TPVB,before skin incision,at 5 min after sectioning and at the end of surgery were(78.52±6.54),(70.79±7.07),(74.48±6.68)and(76.69±7.29)beat·min-1,the mean arterial pressure values were(93.16±5.72),(86.38±7.51),(92.15±6.36)and(91.14±6.13)mmHg.In high-dose group,the heart rate values at the above time points were(79.36±7.11),(71.68±6.49),(74.76±7.06)and(76.57±6.52)beat·min-1;the mean arterial pressure values were(93.89±7.18),(85.27±7.41),(90.34±6.52)and(92.43±6.34)mmHg,there were no statistical differences between the two groups(all P>0.05).The resting state scores at 2,6 and 12 h after surgery were(1.38±0.19),(1.54±0.21)and(1.72±0.16)points,the pain scores at motion state were(1.88±0.15),(2.36±0.37)and(3.26±0.38)points in low-dose group;in high-dose group,the resting state scores were(1.32±0.17),(1.58±0.22)and(1.81±0.18)points,the pain scores at motion state were(1.81±0.13),(2.11±0.31)and(3.03±0.36)points,respectively,there were no statistical differences between the two groups(all P>0.05).The number of analgesic pump compressions at 24 h after surgery and the number of cases with analgesic remedy were(5.12±1.26)times and 15 cases in low-dose group and were(4.74±1.03)times and 10 cases in high-dose group,with no statistical differences between the groups(all P>0.05).The percentages of CD3+cells in low-dose group at the end of surgery and at 12 h and 24 h after surgery were(68.51±6.76)%,(54.22±5.43)%and(51.47±6.58)%,the percentages of CD4+cells were(40.29±5.02)%,(34.94±4.79)%and(30.48±5.11)%,CD4+/CD8+ratios were 1.54±0.34,1.36±0.28 and 1.16±0.23;the percentages of CD3+cells in high-dose group were(67.92±7.11)%,(56.58±6.36)%and(54.47±6.89)%,percentages of CD4+cells were(41.33±5.75)%,(35.86±5.21)%and(32.27±4.78)%,the CD4+/CD8+were 1.53±0.35,1.40±0.30 and 1.22±0.26,all with no significant difference(all P>0.05).The incidence of postoperative adverse drug reactions in high-dose group and low-dose group were 32.61%and 14.58%,with significant difference(P<0.05).Conclusion When TPVB regimen of dezocine combined with ropivacaine is used in thoracoscopic radical resection of lung cancer,the analgesic effect of low-dose dezocine is comparable to that of high-dose dezocine,with lower risk of adverse drug reactions.
4.Role of Hedgehog signaling pathway in muscle bone symbiosis in osteo-sarcopenia
Yan-Dong LIU ; Qiang DENG ; Zhong-Feng LI ; Ran-Dong PENG ; Yu-Rong WANG ; Jia-Ming LI ; Ping-Yi MA ; Jian-Qiang DU
The Chinese Journal of Clinical Pharmacology 2024;40(16):2433-2437
This article elaborates on the complex cross-talk and close relationship between muscles and bones involved in this disease,as well as its pathogenesis.It also summarizes that the difficulty of its treatment lies in the need to simultaneously consider both muscles and bones.And elaborated on the key role of the Hedgehog signaling pathway in embryonic development,tissue morphology establishment,and human tissue regeneration and repair.Investigated the remodeling effect of the Hedgehog signaling pathway on skeletal muscle from three aspects:Proliferation and differentiation of muscle stem cells,precursor cell and muscle fiber generation,inhibition of inflammation,and regulation of immunity;this article elucidates the role of the Hedgehog signaling pathway in bone reconstruction from two aspects.
5.Construction and validation of predictive models for intravenous immunoglobulin–resistant Kawasaki disease using an interpretable machine learning approach
Linfan DENG ; Jian ZHAO ; Ting WANG ; Bin LIU ; Jun JIANG ; Peng JIA ; Dong LIU ; Gang LI
Clinical and Experimental Pediatrics 2024;67(8):405-414
Background:
Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice.
Methods:
Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model.
Results:
Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method.
Conclusion
Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.
6.Construction and validation of predictive models for intravenous immunoglobulin–resistant Kawasaki disease using an interpretable machine learning approach
Linfan DENG ; Jian ZHAO ; Ting WANG ; Bin LIU ; Jun JIANG ; Peng JIA ; Dong LIU ; Gang LI
Clinical and Experimental Pediatrics 2024;67(8):405-414
Background:
Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice.
Methods:
Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model.
Results:
Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method.
Conclusion
Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.
7.Construction and validation of predictive models for intravenous immunoglobulin–resistant Kawasaki disease using an interpretable machine learning approach
Linfan DENG ; Jian ZHAO ; Ting WANG ; Bin LIU ; Jun JIANG ; Peng JIA ; Dong LIU ; Gang LI
Clinical and Experimental Pediatrics 2024;67(8):405-414
Background:
Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice.
Methods:
Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model.
Results:
Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method.
Conclusion
Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.
8.Construction and validation of predictive models for intravenous immunoglobulin–resistant Kawasaki disease using an interpretable machine learning approach
Linfan DENG ; Jian ZHAO ; Ting WANG ; Bin LIU ; Jun JIANG ; Peng JIA ; Dong LIU ; Gang LI
Clinical and Experimental Pediatrics 2024;67(8):405-414
Background:
Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice.
Methods:
Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model.
Results:
Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method.
Conclusion
Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.
9.Mechanism of Poecilobdella Manillensis Lyophilized Powder on Hyperuricemia Based on Network Pharmacology, RNA-seq Technology and Experimental Validation
Yunyi DONG ; Yike LIU ; Xiaolin DENG ; Jian LIANG
Chinese Journal of Modern Applied Pharmacy 2024;41(12):1671-1681
OBJECTIVE
To investigate the multi-target mechanism of action of Poecilobdella manillensis lyophilized powder(SZ) against hyperuricemia(HUA) based on network pharmacology and transcriptomics approaches, combined with animal experiments.
METHODS
Utilizing Symmap, SwissTargetPrediction, and Pharmmapper databases, the potential active components and corresponding targets of SZ were obtained. Through the Gene Cards and OMIM databases, HUA-related targets were obtained. By taking the intersection mapping, the common targets of SZ and HUA were identified. Cytoscape 3.9.0 software was used to construct a drug component-disease target interaction network, and in combination with the STRING database, a protein interaction network was built and core targets were screened. The DAVID database was used to perform GO biological function annotation and KEGG pathway enrichment analysis on the intersecting targets. A mouse model of HUA was constructed using potassium oxyzate combined with high purine diet, and the effects of SZ on these mice were examined using ELISA and biochemical index detection. qRT-PCR was used to validate the results of RNA-Seq and network pharmacology enrichment analysis.
RESULTS
Network pharmacological analysis identified 11 major bioactive substances in SZ and 72 potential targets involved in the treatment of hyperuricemia, involving multiple biological processes and different signaling pathways. It was shown that SZ significantly reduced serum uric acid, creatinine and urea nitrogen levels in hyperuricemic mice by inhibiting xanthineoxidase activity. SZ also reduced the levels of URAT1 while increasing the levels of ABCG2. RNA sequencing analysis revealed that there were 112, 536 and 107 differentially expressed genes in the model group vs treated group, control group vs model group and control group vs treated group, respectively. qRT-PCR results indicated that SZ downregulated the expression of genes related to Th17 cell differentiation as well as mRNA of genes on IL-17 and PI3K/Akt signaling pathways.
CONCLUSION
SZ has therapeutic effects on hyperuricemia. The mechanism of action maybe related to the inhibition of hepatic xanthineoxidase activity, down-regulation of URAT1 levels, up-regulation of ABCG2 levels, affecting the differentiation of Th17 cells and thus the IL-17 signaling pathway, thereby reducing the inflammatory response.
10.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.


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