1.Optimization of extraction process for Shenxiong Huanglian Jiedu Granules based on AHP-CRITIC hybrid weighting method, grey correlation analysis, and BP-ANN.
Zi-An LI ; De-Wen LIU ; Xin-Jian LI ; Bing-Yu WU ; Qun LAN ; Meng-Jia GUO ; Jia-Hui SUN ; Nan-Yang LIU ; Hui PEI ; Hao LI ; Hong YI ; Jin-Yu WANG ; Liang-Mian CHEN
China Journal of Chinese Materia Medica 2025;50(10):2674-2683
By employing the analytic hierarchy process(AHP), the CRITIC method(a weight determination method based on indicator correlations), and the AHP-CRITIC hybrid weighting method, the weight coefficients of evaluation indicators were determined, followed by a comprehensive score comparison. The grey correlation analysis was then performed to analyze the results calculated using the hybrid weighting method. Subsequently, a backpropagation-artificial neural network(BP-ANN) model was constructed to predict the extraction process parameters and optimize the extraction process for Shenxiong Huanglian Jiedu Granules(SHJG). In the extraction process, an L_9(3~4) orthogonal experiment was designed to optimize three factors at three levels, including extraction frequency, water addition amount, and extraction time. The evaluation indicators included geniposide, berberine, ginsenoside Rg_1 + Re, ginsenoside Rb_1, ferulic acid, and extract yield. Finally, the optimal extraction results obtained by the orthogonal experiment, grey correlation analysis, and BP-ANN method were compared, and validation experiments were conducted. The results showed that the optimal extraction process involved two rounds of aqueous extraction, each lasting one hour; the first extraction used ten times the amount of added water, while the second extraction used eight times the amount. In the validation experiments, the average content of each indicator component was higher than the average content obtained in the orthogonal experiment, with a higher comprehensive score. The optimized extraction process parameters were reliable and stable, making them suitable for subsequent preparation process research.
Drugs, Chinese Herbal/analysis*
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Neural Networks, Computer
2.Research progress on the role of efferocytosis in liver diseases.
Kaixin WANG ; Hui LI ; Haijian DONG ; Qun NIU ; Xikun YANG ; Xiaoyan ZENG ; Xuan WU
Chinese Journal of Cellular and Molecular Immunology 2025;41(1):71-76
Efferocytosis refers to the process of phagocytes engulfing and clearing the cells after programmed cell death. In recent years, an increasing number of studies have shown that the mechanisms of efferocytosis are closely related to drug-induced liver injury, hepatic ischemia-reperfusion injury, viral hepatitis, cholestatic liver diseases, metabolic-associated fatty liver disease, alcoholic liver disease, and other liver disorders. This review summarized the research progress on the role of efferocytosis in liver diseases, with the hope of providing new targets for the prevention and treatment of liver diseases.
Humans
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Liver Diseases/metabolism*
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Animals
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Phagocytosis/physiology*
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Phagocytes
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Efferocytosis
3.Cross-sectional survey of healthcare-associated infection in 5 736 medical institutions across China in 2024
Cui ZENG ; Wuqiang GAO ; Fu QIAO ; Hui ZHAO ; Xu FANG ; Linping LI ; Xiuwen CHEN ; Jiansen CHEN ; Dan LI ; Yuan ZHOU ; Lingli YU ; Qinglan MENG ; Xia MOU ; Lijuan XIONG ; Weiguang LI ; Ding LIU ; Jiaqing XIAO ; Limei OU ; Baozhen LI ; Jun YIN ; Haojun ZHANG ; Qiang FU ; Qun LU ; Biao WU ; Ya-wei XING ; Shumei SUN ; Shuncai WANG ; Longmin DU ; Jingping ZHANG ; Wen-ying HE ; Gui CHENG ; Nan REN ; Xun HUANG ; Anhua WU
Chinese Journal of Infection Control 2025;24(11):1572-1583
Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27%.The number of HAI episodes was 38 032,and case prevalence rate was 1.38%.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66%to 2.35%.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65%.The most common infection site was the lower respiratory tract(44.66%),followed by the urinary tract(12.94%),surgical site(9.32%),upper respiratory tract(7.02%),and bloodstream infection(5.78%).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02%),department of neurosurgery(5.51%),and department(group)of hematology(5.34%).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10%)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76%),Pseudomonas aeruginosa(13.33%),Escherichia coli(12.79%),Acinetobacter baumannii(9.23%),and Staphylococcus aureus(7.88%).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71%.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18%.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46%.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.
4.Expert consensus on infection prevention and control of Creutzfeldt-Jakob disease in medical institutions
Tianxiang GE ; Yangyang JIA ; Chunhui LI ; Jianrong HUANG ; Xiujuan MENG ; Xiaodong GAO ; Jingping ZHANG ; Fu QIAO ; Lijuan XIONG ; Hui LIANG ; Wei LI ; Haiyan LOU ; Wenjuan WU ; Tianxin XIANG ; Jiansen CHEN ; Biao ZHU ; Kaijin XU ; Zhihui ZHOU ; Hongliu CAI ; Meihong YU ; Yan ZHANG ; Yanwan SHANGGUAN ; Haiting FENG ; Hangping YAO ; Lei GUO ; Tieer GAN ; Weihong ZHANG ; Jimin SUN ; Ye LU ; Qun LU ; Meng CAI ; Jin SHEN ; Yunsong YU ; Anhua WU ; Liu-yi LI ; Tingting QU
Chinese Journal of Infection Control 2025;24(4):437-450
Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.
5.Expert consensus on infection prevention and control of Creutzfeldt-Jakob disease in medical institutions
Tianxiang GE ; Yangyang JIA ; Chunhui LI ; Jianrong HUANG ; Xiujuan MENG ; Xiaodong GAO ; Jingping ZHANG ; Fu QIAO ; Lijuan XIONG ; Hui LIANG ; Wei LI ; Haiyan LOU ; Wenjuan WU ; Tianxin XIANG ; Jiansen CHEN ; Biao ZHU ; Kaijin XU ; Zhihui ZHOU ; Hongliu CAI ; Meihong YU ; Yan ZHANG ; Yanwan SHANGGUAN ; Haiting FENG ; Hangping YAO ; Lei GUO ; Tieer GAN ; Weihong ZHANG ; Jimin SUN ; Ye LU ; Qun LU ; Meng CAI ; Jin SHEN ; Yunsong YU ; Anhua WU ; Liu-yi LI ; Tingting QU
Chinese Journal of Infection Control 2025;24(4):437-450
Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.
6.Cross-sectional survey of healthcare-associated infection in 5 736 medical institutions across China in 2024
Cui ZENG ; Wuqiang GAO ; Fu QIAO ; Hui ZHAO ; Xu FANG ; Linping LI ; Xiuwen CHEN ; Jiansen CHEN ; Dan LI ; Yuan ZHOU ; Lingli YU ; Qinglan MENG ; Xia MOU ; Lijuan XIONG ; Weiguang LI ; Ding LIU ; Jiaqing XIAO ; Limei OU ; Baozhen LI ; Jun YIN ; Haojun ZHANG ; Qiang FU ; Qun LU ; Biao WU ; Ya-wei XING ; Shumei SUN ; Shuncai WANG ; Longmin DU ; Jingping ZHANG ; Wen-ying HE ; Gui CHENG ; Nan REN ; Xun HUANG ; Anhua WU
Chinese Journal of Infection Control 2025;24(11):1572-1583
Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27%.The number of HAI episodes was 38 032,and case prevalence rate was 1.38%.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66%to 2.35%.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65%.The most common infection site was the lower respiratory tract(44.66%),followed by the urinary tract(12.94%),surgical site(9.32%),upper respiratory tract(7.02%),and bloodstream infection(5.78%).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02%),department of neurosurgery(5.51%),and department(group)of hematology(5.34%).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10%)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76%),Pseudomonas aeruginosa(13.33%),Escherichia coli(12.79%),Acinetobacter baumannii(9.23%),and Staphylococcus aureus(7.88%).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71%.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18%.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46%.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.
7.Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data.
Cong LI ; Xiao-Yan ZHANG ; Yun-Hong WU ; Xiao-Lei YANG ; Hua-Rong YU ; Hong-Bo JIN ; Ying-Bo LI ; Zhao-Hui ZHU ; Rui LIU ; Na LIU ; Yi XIE ; Lin-Li LYU ; Xin-Hong ZHU ; Hong TANG ; Hong-Fang LI ; Hong-Li LI ; Xiang-Jun ZENG ; Zai-Xing CHEN ; Xiao-Fang FAN ; Yan WANG ; Zhi-Juan WU ; Zun-Qiu WU ; Ya-Qun GUAN ; Ming-Ming XUE ; Bin LUO ; Ai-Mei WANG ; Xin-Wang YANG ; Ying YING ; Xiu-Hong YANG ; Xin-Zhong HUANG ; Ming-Fei LANG ; Shi-Min CHEN ; Huan-Huan ZHANG ; Zhong ZHANG ; Wu HUANG ; Guo-Biao XU ; Jia-Qi LIU ; Tao SONG ; Jing XIAO ; Yun-Long XIA ; You-Fei GUAN ; Liang ZHU
Acta Physiologica Sinica 2024;76(6):937-942
As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence*
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Humans
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Consensus
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Computer Security/standards*
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Confidentiality/ethics*
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Informed Consent/ethics*
8.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.
9.Study of economic burden of healthcare-associated infection caused by hemorrhagic stroke based on propensity score matching and generalized linear model
Yuan-Yuan LI ; Hui XU ; Song CHENG ; Shu-Chao WU ; Qun-Jian CUI
Chinese Journal of Infection Control 2024;23(7):819-825
Objective To study the economic burden caused by healthcare-associated infection(HAI)in patients with hemorrhagic stroke.Methods Patients with hemorrhagic stroke in a tertiary first-class hospital from January 1,2021 to December 31,2022 were surveyed retrospectively.Data on demographic characteristics,clinical informa-tion,and hospitalization expenses were collected.According to the occurrence of HAI,patients were divided into the HAI group and control group.The length of hospital stay,increase in hospitalization expense,and hospital eco-nomic burden of the HAI group and control group were studied by propensity score matching(PSM)method and generalized linear model method.Results A total of 688 patients were included in the study,with 266 cases experi-encing HAI and a HAI incidence of 38.66%.After propensity score matching,199 patients in the HAI group were successfully matched.Compared with the control group,the median length of hospital stay in the HAI group doub-led,increasing by 16 days(Z=11.779,P<0.001);the median hospitalization expense increased by 34 597.42 Yuan,with an increase of 85%(Z=6.299,P<0.001).Based on the generalized linear model method,length of hospital days attributed to HAI increased by 1.24 times,hospitalization expense increased by 76%(both P<0.001).Except surgical expenses,the HAI group had higher single medical expenses than the control group(all P<0.05).Economic burden to hospital caused by HAI was 541 900 Yuan.Conclusion HAI significantly increases the economic burden of hemorrhagic stroke patients and hospitals,and prolongs the length of hospital stay.Clinical staff should enhance the awareness on infection control,reduce the incidence of HAI,and save medical resources.
10.Mechanism of Danzhi Jiangtang capsule protecting mitochondrial function and reducing vascular calcification via LncRNA TUG1/β-catenin signaling pathway
Ying-Qun NI ; Yi-Xuan LIN ; Si-Hai WANG ; Qin LU ; Jin-Zhi LUO ; Chun-Qin WU ; ZHAO-Hui FANG
Chinese Pharmacological Bulletin 2024;40(5):899-906
Aim To explore how Danzhi Jiangtang cap-sules(DJC)safeguard the mitochondrial activity of vascular smooth muscle cells(VSMCs)by controlling the LncRNA TUG1/β-catenin signaling pathway to de-crease vascular calcification(VC).Methods Vascu-lar smooth muscle cell calcification models were in-duced with β-glycerin and diabetic vascular calcifica-tion rat models were induced with vitamin D3+high-fat diet.Von Kossa staining was applied to detect cal-cification of cells and vascular tissue.Colorimetric method of phthalein complex was used to determine calcium content.P-nitrobenzene phosphate colorimetry was employed to assess alkaline phosphatase(ALP)activity.RT-qPCR was used to analyze the expression of VSMCs'osteoblast transformation related genes bone morphogenetic protein2(BMP2),smooth muscle actin alpha(α-SMA),taurine up-regulated1,LncRNA Tug1(Lnc-RNA TUG1),and β-catenin.Western blotting was utilized to detect the protein expression of BMP2,α-SMA and β-catenin.The mitochondrial membrane potential was detected by JC-1 fluorescence probe.Mitochondrial structure was observed by trans-mission electron microscope.Results DJC reduced LncRNA TUG1 expression,down-regulated β-catenin expression,decreased ALP activity and calcium depo-sition,protected mitochondrial function,restored mem-brane potential,and decreased osteoblastic transforma-tion of VSMCs induced by glycerin phosphate.Impor-tantly,DJC attenuated diabetic lower limb VC by down-regulating the expression of LncRNA TUG1,β-catenin,and elevating the expression of α-SMA.Con-clusions DJC capsules significantly improved VSMCs by protecting mitochondrial function by LncRNA TUG1/β-catenin signaling to reduce VSMCs'osteo-blast transformation.

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