1.Surveillance of bacterial resistance in tertiary hospitals across China:results of CHINET Antimicrobial Resistance Surveillance Program in 2022
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Wei LI ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Yanyan LIU ; Yong AN
Chinese Journal of Infection and Chemotherapy 2024;24(3):277-286
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5%were gram-positive and 70.5%were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3%,76.7%and 77.9%,respectively.Overall,94.0%of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8%of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2%in the isolates from children and 95.7%in the isolates from adults.The resistance rate to carbapenems was lower than 13.1%in most Enterobacterales species except for Klebsiella,21.7%-23.1%of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1%to 13.3%.The prevalence of meropenem-resistant strains decreased from 23.5%in 2019 to 18.0%in 2022 in Pseudomonas aeruginosa,and decreased from 79.0%in 2019 to 72.5%in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.
2.Analysis Method for Freshness of Stored Paddy Rice Based on Volatile Components and Multivariate Statistical Analysis
Rui GUO ; Pan-Pan LI ; Wei ZHANG ; Nan-Xi WANG ; Yong-Tan YANG
Chinese Journal of Analytical Chemistry 2024;52(9):1244-1253
By using paddy rice harvested between 2019 and 2023 as the research object,the volatile components of rice grains were detected by headspace solid-phase microextraction coupled with gas chromatography-triple quadrupole mass spectrometry.Qualitative analysis of the compounds was complemented by a standard mass spectrometry database and retention index,while a selected ion monitoring approach was established to quantify the contents of each component through the internal standard method.Multivariate statistical analyses including principal component analysis and orthogonal partial least squares discriminant analysis were employed to identify differential compounds related to freshness of the paddy rice.Subsequently,a classification model for identifying stored paddy rice based on volatile component analysis was developed.A total of 44 kinds of volatile compounds were identified across different harvest years,including aldehydes,alcohols,ketones,acids,esters,phenols and furans.The results of the multivariate statistical analysis revealed that the content-based orthogonal partial least squares discriminant analysis model could effectively distinguish 2023 harvested paddy rice from those harvested between 2019 and 2022 into two distinct categories.Notably,compounds such as hexanoic acid and nonanoic acid along with twelve others were identified as differential compounds based on variable improtance in projection(VIP)values exceeding 1 and p values less than 0.05.The classification model established through volatile component analysis was expected to provide a theoretical foundation for assessing the freshness of stored paddy rice.
3.Tildrakizumab for moderate-to-severe plaque psoriasis in Chinese patients: A 12-week randomized placebo-controlled phase III trial with long-term extension
Chen YU ; Songmei GENG ; Bin YANG ; Yunhua DENG ; Fuqiu LI ; Xiaojing KANG ; Mingye BI ; Furen ZHANG ; Yi ZHAO ; Weili PAN ; Zhongwei TIAN ; Jinhua XU ; Zhenghua ZHANG ; Nan YU ; Xinsuo DUAN ; Shuping GUO ; Qing SUN ; Weiquan LI ; Juan TAO ; Zhijun LIU ; Yuanyuan YIN ; Gang WANG
Chinese Medical Journal 2024;137(10):1190-1198
Background::There is a need for effective and safe therapies for psoriasis that provide sustained benefits. The aim of this study was to assess the efficacy and safety of tildrakizumab, an anti-interleukin-23p19 monoclonal antibody, for treating moderate-to-severe plaque psoriasis in Chinese patients.Methods::In this multi-center, double-blind, phase III trial, patients with moderate-to-severe plaque psoriasis were enrolled and randomly assigned (1:1) to receive subcutaneous tildrakizumab 100 mg or placebo at weeks 0 and 4. Patients initially assigned to placebo were switched to receive tildrakizumab at weeks 12, 16, and every 12 weeks thereafter. Patients in the tildrakizumab group continued with tildrakizumab at week 16, and every 12 weeks until week 52. The primary endpoint was the Psoriasis Area and Severity Index (PASI 75) response rate at week 12.Results::At week 12, tildrakizumab demonstrated significantly higher PASI 75 response rates (66.4% [73/110] vs. 12.7% [14/110]; difference, 51.4% [95% confidence interval (CI), 40.72, 62.13]; P <0.001) and Physician’s Global Assessment (60.9% [67/110] vs. 10.0% [11/110]; difference, 49.1% [95% CI, 38.64, 59.62]; P <0.001) compared to placebo. PASI 75 response continued to improve over time in both tildrakizumab and placebo-switching to tildrakizumab groups, reaching maximal efficacy after 28 weeks (86.8% [92/106] vs. 82.4% [89/108]) and maintained up to 52 weeks (91.3% [95/104] vs. 87.4% [90/103]). Most treatment-emergent adverse events were mild and not related to tildrakizumab. Conclusion::Tildrakizumab demonstrated durable efficacy through week 52 and was well tolerated in Chinese patients with moderate-to-severe plaque psoriasis.Trial registration::ClinicalTrials.gov, NCT05108766.
4.Analysis of the biosynthesis pathways of phenols in the leaves of Tetrastigma hemsleyanum regulated by supplemental blue light based on transcriptome sequencing
Hui-long XU ; Nan YANG ; Yu-yan HONG ; Meng-ting PAN ; Yu-chun GUO ; Shi-ming FAN ; Wen XU
Acta Pharmaceutica Sinica 2024;59(10):2864-2870
Analyze the changes in phenolic components and gene expression profiles of
5.Antimicrobial resistance profile of clinical isolates in hospitals across China:report from the CHINET Antimicrobial Resistance Surveillance Program,2023
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Wei LI ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Hua FANG ; Penghui ZHANG ; Bixia YU ; Ping GONG ; Haixia SHI ; Kaizhen WEN ; Yirong ZHANG ; Xiuli YANG ; Yiqin ZHAO ; Longfeng LIAO ; Jinhua WU ; Hongqin GU ; Lin JIANG ; Meifang HU ; Wen HE ; Jiao FENG ; Lingling YOU ; Dongmei WANG ; Dong'e WANG ; Yanyan LIU ; Yong AN ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Jianping WANG ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Cunshan KOU ; Shunhong XUE ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Xiaoyan ZENG ; Wen LI ; Yan GENG ; Zeshi LIU
Chinese Journal of Infection and Chemotherapy 2024;24(6):627-637
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in healthcare facilities in major regions of China in 2023.Methods Clinical isolates collected from 73 hospitals across China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2023 Clinical & Laboratory Standards Institute (CLSI) breakpoints.Results A total of 445199 clinical isolates were collected in 2023,of which 29.0% were gram-positive and 71.0% were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species (excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi) (MRSA,MRSE and MRCNS) was 29.6%,81.9% and 78.5%,respectively.Methicillin-resistant strains showed significantly higher resistance rates to most antimicrobial agents than methicillin-susceptible strains (MSSA,MSSE and MSCNS).Overall,92.9% of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 91.4% of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis had significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 93.1% in the isolates from children and and 95.9% in the isolates from adults.The resistance rate to carbapenems was lower than 15.0% for most Enterobacterales species except for Klebsiella,22.5% and 23.6% of which were resistant to imipenem and meropenem,respectively .Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.6% to 10.0%.The resistance rate to imipenem and meropenem was 21.9% and 17.4% for Pseudomonas aeruginosa,respectively,and 67.5% and 68.1% for Acinetobacter baumannii,respectively.Conclusions Increasing resistance to the commonly used antimicrobial agents is still observed in clinical bacterial isolates.However,the prevalence of important crabapenem-resistant organisms such as crabapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a slightly decreasing trend.This finding suggests that strengthening bacterial resistance surveillance and multidisciplinary linkage are important for preventing the occurrence and development of bacterial resistance.
6.Antimicrobial resistance profile of clinical isolates in hospitals across China:report from the CHINET Antimicrobial Resistance Surveillance Program,2023
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Wei LI ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Hua FANG ; Penghui ZHANG ; Bixia YU ; Ping GONG ; Haixia SHI ; Kaizhen WEN ; Yirong ZHANG ; Xiuli YANG ; Yiqin ZHAO ; Longfeng LIAO ; Jinhua WU ; Hongqin GU ; Lin JIANG ; Meifang HU ; Wen HE ; Jiao FENG ; Lingling YOU ; Dongmei WANG ; Dong'e WANG ; Yanyan LIU ; Yong AN ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Jianping WANG ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Cunshan KOU ; Shunhong XUE ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Xiaoyan ZENG ; Wen LI ; Yan GENG ; Zeshi LIU
Chinese Journal of Infection and Chemotherapy 2024;24(6):627-637
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in healthcare facilities in major regions of China in 2023.Methods Clinical isolates collected from 73 hospitals across China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2023 Clinical & Laboratory Standards Institute (CLSI) breakpoints.Results A total of 445199 clinical isolates were collected in 2023,of which 29.0% were gram-positive and 71.0% were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species (excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi) (MRSA,MRSE and MRCNS) was 29.6%,81.9% and 78.5%,respectively.Methicillin-resistant strains showed significantly higher resistance rates to most antimicrobial agents than methicillin-susceptible strains (MSSA,MSSE and MSCNS).Overall,92.9% of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 91.4% of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis had significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 93.1% in the isolates from children and and 95.9% in the isolates from adults.The resistance rate to carbapenems was lower than 15.0% for most Enterobacterales species except for Klebsiella,22.5% and 23.6% of which were resistant to imipenem and meropenem,respectively .Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.6% to 10.0%.The resistance rate to imipenem and meropenem was 21.9% and 17.4% for Pseudomonas aeruginosa,respectively,and 67.5% and 68.1% for Acinetobacter baumannii,respectively.Conclusions Increasing resistance to the commonly used antimicrobial agents is still observed in clinical bacterial isolates.However,the prevalence of important crabapenem-resistant organisms such as crabapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a slightly decreasing trend.This finding suggests that strengthening bacterial resistance surveillance and multidisciplinary linkage are important for preventing the occurrence and development of bacterial resistance.
7. Effects of R-loops on Genome Stability
Tu-Nan XU ; Yan-Jun GUO ; Wei-Wei PAN
Chinese Journal of Biochemistry and Molecular Biology 2023;39(9):1238-1246
The R-loop is a three-stranded nucleic acid structure, which consists of a RNA: DNA hybrid and a DNA single strand. R-loop can be divided into two types: physiological and pathological. The physiological R-loop is involved in many physiological processes such as DNA replication, transcription, and gene expression regulation, while the pathological R-loop induces DNA damage and genome rearrangement. There are many factors that affect the formation of R-loops. Unregulated R-loops destroy genomic stability by interfering with DNA replication and double-strand DNA break repair, and can cause cancer. Therefore, the regulation of R-loops is very important. RNA/ DNA helicase Senataxin (SETX), DEAD-box helicase 5 (DDX5), ribonuclease H (RNase H) and DNA topoisomerase I (topo) play an important role in regulating the balance of R-loops in vivo. Among them, SETX is one of the most characteristic R-loop decomposing enzymes, which can dissolve the R-loops produced during transcriptional termination sites, replication-transcriptional conflicts and DNA damage repair. Senataxin mutations will lead to ataxia with eye movement apraxia type 2 (AOA2) and amyotrophic lateral sclerosis type 4 (ALS4). Currently there are still many unsolved issues, although many in-depth studies of R-loops have been carried out. Therefore, the structure and function of physiological and pathological R-loops still need to be further explored. This review mainly focuses on the definition and classification of R-loops, the factors that affect the formation of R-loops, the influence of R-loops on genomic stability and R-loop-related diseases, and explores the possibility of using R-loops as a therapeutic target in the future.
8.Effect of diosgenin on mTOR/FASN/HIF-1α/VEGFA expression in rats with non-alcoholic fatty liver disease.
Guo-Liang YIN ; Hong-Yi LIANG ; Peng-Peng LIANG ; Ya-Nan FENG ; Su-Wen CHEN ; Xiang-Yi LIU ; Wen-Chao PAN ; Feng-Xia ZHANG
China Journal of Chinese Materia Medica 2023;48(7):1760-1769
The present study aimed to investigate the effect of diosgenin on mammalian target of rapamycin(mTOR), fatty acid synthase(FASN), hypoxia inducible factor-1α(HIF-1α), and vascular endothelial growth factor A(VEGFA) expression in liver tissues of rats with non-alcoholic fatty liver disease(NAFLD) and explore the mechanism of diosgenin on lipogenesis and inflammation in NAFLD. Forty male SD rats were divided into a normal group(n=8) fed on the normal diet and an experimental group(n=32) fed on the high-fat diet(HFD) for the induction of the NAFLD model. After modeling, the rats in the experimental group were randomly divided into an HFD group, a low-dose diosgenin group(150 mg·kg~(-1)·d~(-1)), a high-dose diosgenin group(300 mg·kg~(-1)·d~(-1)), and a simvastatin group(4 mg·kg~(-1)·d~(-1)), with eight rats in each group. The drugs were continuously given by gavage for eight weeks. The levels of triglyceride(TG), total cholesterol(TC), low-density lipoprotein cholesterol(LDL-C), alanine transaminase(ALT), and aspartate transaminase(AST) in the serum were detected by the biochemical method. The content of TG and TC in the liver was detected by the enzyme method. Enzyme-linked immunosorbent assay(ELISA) was used to measure interleukin 1β(IL-1β) and tumor necrosis factor α(TNF-α) in the serum. Lipid accumulation in the liver was detected by oil red O staining. Pathological changes of liver tissues were detected by hematoxylin-eosin(HE) staining. The mRNA and protein expression levels of mTOR, FASN, HIF-1α, and VEGFA in the liver of rats were detected by real-time fluorescence-based quantitative polymerase chain reaction(PCR) and Western blot, respectively. Compared with the normal group, the HFD group showed elevated body weight and levels of TG, TC, LDL-C, ALT, AST, IL-1β, and TNF-α(P<0.01), increased lipid accumulation in the liver(P<0.01), obvious liver steatosis, up-regulated mRNA expression levels of mTOR, FASN, HIF-1α, and VEGFA(P<0.01), and increased protein expression levels of p-mTOR, FASN, HIF-1α, and VEGFA(P<0.01). Compared with the HFD group, the groups with drug treatment showed lowered body weight and levels of TG, TC, LDL-C, ALT, AST, IL-1β, and TNF-α(P<0.05, P<0.01), reduced lipid accumulation in the liver(P<0.01), improved liver steatosis, decreased mRNA expression levels of mTOR, FASN, HIF-1α, and VEGFA(P<0.05, P<0.01), and declining protein expression levels of p-mTOR, FASN, HIF-1α, and VEGFA(P<0.01). The therapeutic effect of the high-dose diosgenin group was superior to that of the low-dose diosgenin group and the simvastatin group. Diosgenin may reduce liver lipid synthesis and inflammation and potentiate by down-regulating the mTOR, FASN, HIF-1α, and VEGFA expression, playing an active role in preventing and treating NAFLD.
Rats
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Male
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Animals
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Non-alcoholic Fatty Liver Disease/metabolism*
;
Vascular Endothelial Growth Factor A/metabolism*
;
Tumor Necrosis Factor-alpha/metabolism*
;
Cholesterol, LDL
;
Rats, Sprague-Dawley
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Liver
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Inflammation/metabolism*
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Diet, High-Fat/adverse effects*
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TOR Serine-Threonine Kinases/metabolism*
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RNA, Messenger/metabolism*
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Body Weight
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Mammals
9.Analysis of titer stability and inactivation kinetics of harvest solution of SARS-CoV-2
GUO Bing-feng ; HAN Bin ; HAO Yi-nan ; WANG Kui ; YIN Ji-xiang ; LI Yan ; LI Nan ; LING Xiang-ping ; PAN Ruo-wen
Chinese Journal of Biologicals 2023;36(2):129-132+144
Objective To investigate the titer stability of the harvest solution of severe acute respiratory syndrome coronavirus2(SARS-CoV-2)at 2 ~ 8 ℃ and the inactivation effect of β-propiolactone inactivator on the virus.Methods Three batches of SARS-CoV-2 harvest solution(batch numbers:202111001,202111002 and 202111003)were stored at 2 ~ 8 ℃ for 12 d and sampled every 3 d(0,3,6,9 and 12 d)for detection of the titers by Karber method;Three batches of virus harvest solution equilibrated overnight at 2 ~ 8 ℃ were inactivated by adding β-propiolactone at a volume fraction of 1∶4 000 and detected for the titers at different inactivation time points(0,0.5,1,1.5,2,3,4,8,16 and 24 h),of which samples inactivated for 8,16 and 24 h were taken for inactivation verification,and samples inactivated for 24 h were observed by transmission electron microscope.Results The titers of SARS-CoV-2 decreased with the prolongation of storage time at 2 ~8 ℃,which showed no obvious decrease during 0 ~ 3 d,while decreased from the initial 7.75,6 and 7.5 lgCCID_(50)/mL to5.75,4.625 and 6.25 lgCCID_(50)/mL on day 12,indicating that the virus activity showed a gradual decrease trend at 2 ~8 ℃;With the inactivation time,the virus titer decreased continuously and could not be detected after inactivation for 3 h.Transmission electron microscope observation showed that the inactivated virus particles were intact and the spike protein was evenly distributed.Conclusion The virulence of SARS-CoV-2 stored at 2 ~ 8 ℃ was unstable,so the subsequent inactivation and purification process should be carried out as soon as possible;The titer of virus could not be detected after3 h of inactivation,which provided a reference for the determination of the inactivation process.
10.Related factors of urinary tract infections in inpatients based on real world data.
Chun Hong BIAN ; Yue PAN ; Ya Nan TAN ; Li Min ZHANG ; Rong Qi WANG ; Guo Jun ZHANG
Chinese Journal of Preventive Medicine 2022;56(11):1636-1641
To analyze the risk factors for urinary tract infection (UTI) among inpatients. The case data of 1 875 inpatients receiving urinary bacterial culture in Beijing Haidian Hospital from October 2019 to May 2021 were analyzed retrospectively. According to the etiological diagnostic criteria of UTI, they were divided into infection group and non-infection group. The species and distribution of pathogens in the infection group were analyzed, and the case data and laboratory indexes were subjected to univariate analysis. The variables with statistical significance were selected for binary logistic regression to analyze the risk factors of urinary tract infection and establish a prediction model. The receiver operating characteristic (ROC) curve was drawn for each parameter included in the model, and the area under the curve (AUC) was calculated. The diagnostic and predictive efficacy of each parameter alone and their combination for UTI were evaluated. So, a total of 1 162 patients with non-infection group and 713 patients with UTI were detected. Among the cultured pathogens, the constituent ratio of Gram-negative bacteria, Gram-positive bacteria and fungi was 57.2%(408/713), 35.9%(256/713) and 6.9%(49/713) respectively. Multivariate analysis showed that, Age, duration of urinary catheterization>7 d, stroke and orthopedic surgery were the risk factors of UTI among inpatients. The use of antibiotics is a protective factor for urinary tract infections. The prediction model of UTI was established by the risk factors, age, duration of urinary catheterization>7 d, stroke, orthopedic surgery, urinary leukocyte esterase, urinary nitrite and Coefficient of variability of red blood cell volume distribution width (RDW-CV). The AUC of the combination of the eight parameters in diagnosing and predicting UTI was 0.835 (95%CI: 0.816-0.855), with the sensitivity of 70.7% and the specificity of 82.8%. In conclusion,the combination of the eight parameters can better assist in the diagnosis and prediction of UTI, and provide an experimental basis for clinicians to judge UTI.
Humans
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Retrospective Studies
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Inpatients
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Urinary Tract Infections/microbiology*
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Urinalysis
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Stroke


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