1.Identification of Alumen and Ammonium alum Based on XRD, FTIR, TG-DTA Combined with Chemometrics
Bin WANG ; Jingwei ZHOU ; Huangsheng ZHANG ; Jian FENG ; Hanxi LI ; Guorong MEI ; Jiaquan JIANG ; Hongping CHEN ; Fu WANG ; Yuan HU ; Youping LIU ; Shilin CHEN ; Lin CHEN
Chinese Journal of Experimental Traditional Medical Formulae 2025;31(13):178-186
ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction(XRD), Fourier-transform infrared spectroscopy(FTIR) and thermogravimetric-differential thermal analysis(TG-DTA), and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant, Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum, and the XRD characteristic profiles of Alumen were established, and then the common peaks were screened. Based on hierarchical clustering analysis(HCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA), the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection(VIP) value>1. FTIR characteristic profiles of Alumen were established, and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile, the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA, and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However, by comparing the PDF standard cards of XRD, 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles, 10 characteristic peaks were confirmed, corresponding to diffraction angles of 14.560°, 24.316°, 12.620°, 32.122°, 17.898°, 34.642°, 27.496°, 46.048°, 40.697° and 21.973°. In the FTIR profiles, 4 wavenumber ranges(399.193-403.050, 1 186.010-1 471.420, 1 801.190-2 620.790, 3 612.020-3 997.710 cm-1) and 12 characteristic wavenumbers(1 428.994, 1 430.922, 1 432.851, 1 434.779, 1 436.708, 1 438.636, 1 440.565, 1 442.493, 1 444.422, 1 446.350, 1 448.279, 1 450.207 cm-1) were identified. In the TG-DTA profiles, there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD, FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum, and combined with the discriminant features selected through chemometrics, the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.
2.Identification of Alumen and Ammonium alum Based on XRD, FTIR, TG-DTA Combined with Chemometrics
Bin WANG ; Jingwei ZHOU ; Huangsheng ZHANG ; Jian FENG ; Hanxi LI ; Guorong MEI ; Jiaquan JIANG ; Hongping CHEN ; Fu WANG ; Yuan HU ; Youping LIU ; Shilin CHEN ; Lin CHEN
Chinese Journal of Experimental Traditional Medical Formulae 2025;31(13):178-186
ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction(XRD), Fourier-transform infrared spectroscopy(FTIR) and thermogravimetric-differential thermal analysis(TG-DTA), and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant, Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum, and the XRD characteristic profiles of Alumen were established, and then the common peaks were screened. Based on hierarchical clustering analysis(HCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA), the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection(VIP) value>1. FTIR characteristic profiles of Alumen were established, and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile, the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA, and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However, by comparing the PDF standard cards of XRD, 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles, 10 characteristic peaks were confirmed, corresponding to diffraction angles of 14.560°, 24.316°, 12.620°, 32.122°, 17.898°, 34.642°, 27.496°, 46.048°, 40.697° and 21.973°. In the FTIR profiles, 4 wavenumber ranges(399.193-403.050, 1 186.010-1 471.420, 1 801.190-2 620.790, 3 612.020-3 997.710 cm-1) and 12 characteristic wavenumbers(1 428.994, 1 430.922, 1 432.851, 1 434.779, 1 436.708, 1 438.636, 1 440.565, 1 442.493, 1 444.422, 1 446.350, 1 448.279, 1 450.207 cm-1) were identified. In the TG-DTA profiles, there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD, FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum, and combined with the discriminant features selected through chemometrics, the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.
3.Clinical efficacy of valve surgery for infective endocarditis in 343 patients: A retrospective study in a single center
Shuanglei ZHAO ; Zhou LIU ; Bin WANG ; Zhaoqing SUN ; Mingxiu WEN ; Qianxian LI ; Yi HU ; Wenjian JIANG ; Jie HAN ; Jiangang WANG ; Ming GONG
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery 2025;32(08):1133-1139
Objective To analyze the clinical efficacy of valve surgeries for infective endocarditis and the affecting factors, and compare the early- and long-term postoperative outcomes of different surgery approaches. Methods The patients with infective endocarditis who underwent valve replacement/valvuloplasty in our hospital from 2010 to 2022 were retrospectively collected. The clinical data of the patients were analyzed. Results A total of 343 patients were enrolled, including 197 patients with mechanical valve replacement, 62 patients with bioprosthetic valve replacement, and 84 patients with valvuloplasty. There were 238 males and 105 females with an average age of (44.2±14.8) years. Single-valve endocarditis was present in 200 (58.3%) patients, and multivalve involvement was present in 143 (41.7%) patients. Sixty (17.5%) patients had suffered thrombosis before surgery, including cerebral embolisms in 32 patients. The mean follow-up time was (60.6±43.8) months. Early mortality within one month after the surgery occurred in 17 (5.0%) patients, while later mortality occurred in 19 (5.5%) patients. Eight (2.3%) patients underwent postoperative dialysis, 13 (3.8%) patients suffered postoperative stroke, 6 patients underwent reoperation, and 3 patients suffered recurrence of infective endocarditis. Smoking (P=0.002), preoperative embolisms (P=0.001), duration of surgery (P=0.001), and postoperative dialysis (P=0.001) were risk factors for early mortality, and left ventricular ejection fraction ≥60% (P=0.022) was protective factor for early mortality. New York Heart Association classification Ⅲ-Ⅳ (P=0.010) and ≥3 valve procedures (P=0.028) were risk factors for late mortality. The rate of composite endpoint events was significantly lower in the valvuloplasty group than that in the valve replacement group. Conclusion For patients with infective endocarditis, smoking and preoperative embolisms are associated with high postoperative mortality, multiple-valve surgery is associated with a poorer prognosis, and valvuloplasty has advantages over valve replacement and should be attempted in the surgical management of patients with infective endocarditis.
4.The Influence of Social Context on Perceptual Decision Making and Its Computational Neural Mechanisms
Yu-Pei LIU ; Yu-Shu WANG ; Bin ZHAN ; Rui WANG ; Yi JIANG
Progress in Biochemistry and Biophysics 2025;52(10):2568-2584
Perceptual decision making refers to the process by which individuals make choices and judgments based on sensory information, serving as a fundamental ability for human adaptation to complex environments. While traditional research has focused on perceptual decision making in isolated contexts, growing evidence highlights the profound influence of social contexts prevalent in real-world scenarios. As a crucial factor supporting individual survival and development, social context not only provides rich information sources but also shapes perceptual decision making through top-down processing mechanisms, prompting researchers to recognize the inherently social nature of human decisions. Empirical studies have demonstrated that social information, such as others’ choices or group norms, can systematically bias individuals’ perceptual decisions, often manifesting as conformity behaviors. Social influence can also facilitate performance under certain conditions, particularly when individuals can accurately identify and adopt high-quality social information. The impact of social context on perceptual decisions is modulated by a variety of external and internal factors, including group characteristics(e.g., group size, response consistency), attributes of peers (e.g., familiarity, social status, distinctions between human and artificial agents), as well as individual differences such as confidence, personality traits, and developmental stage. The motivations driving social influence encompass three primary mechanisms: improving decision accuracy through informational influence, gaining social acceptance through normative influence, and maintaining positive self-concept. Recent computational approaches have employed diverse theoretical frameworks to provide valuable insights into the cognitive mechanisms underlying social influence in perceptual decision making. Reinforcement learning models demonstrate how social feedback shapes future choices through reward-based updating. Bayesian inference frameworks describe how individuals integrate personal beliefs with social information based on their respective reliabilities, dynamically updating beliefs to optimize decisions under uncertainty. Drift diffusion models offer powerful tools to decompose social influence into distinct cognitive components, allowing researchers to differentiate between changes in perceptual processing and shifts in decision criteria. Collectively, these models establish a comprehensive methodological foundation for disentangling the multiple pathways by which social context shapes perceptual decisions. Neuroimaging and electrophysiological studies provide converging evidence that social context influences perceptual decision making through multi-level neural mechanisms. At early perceptual processing stages, social influence modulates sensory evidence accumulation in parietal cortex and directly alters primary visual cortex activity, while guiding selective attention to stimulus features consistent with social norms through attentional alignment mechanisms. At higher cognitive levels, the reward system (ventral striatum, ventromedial prefrontal cortex) is activated during group-consistent decisions; emotion-processing networks (anterior cingulate cortex, insula, amygdala) regulate experiences of social acceptance and rejection; and mentalizing-related brain regions (dorsomedial prefrontal cortex, temporoparietal junction) support inference of others’ mental states and social information integration. These neural circuits work synergistically to achieve top-down multi-level modulation of perceptual decision making. Understanding the mechanisms by which social context shapes perceptual decision making has broad theoretical and practical implications. These insights inform the optimization of collective decision-making, the design of socially adaptive human-computer interaction systems, and interventions for cognitive disorders such as autism spectrum disorder and anorexia nervosa. Future studies should combine computational modeling and neuroimaging approaches to systematically investigate the multi-level and dynamic nature of social influences on perceptual decision making.
5.Construction of a predictive model for cerebral small vessel disease MRI burden based on β2-microglobulin and lipoprotein(a)
Xiaoyan LI ; Hongge JI ; Tingting WANG ; Yingying LI ; Xijing ZHA ; Bin LI ; Dan JIANG
Chinese Journal of Clinical Medicine 2025;32(4):634-641
Objective To construct a predictive model for cerebral small vessel disease (CSVD) MRI burden based on β2-microglobulin (β2-MG) and lipoprotein(a) [Lp(a)], analyze its predictive value, and validate the model. Methods A total of 138 CSVD patients admitted to Anhui No.2 Provincial People’s Hospital from February 2023 to August 2024 were enrolled. Patients were divided into a low-burden group (n=63) and a moderate/severe burden group (n=75) according to the CSVD MRI burden scoring criteria. The related clinical data were compared between the two groups. Binary logistic regression analysis was used to identify independent factors for CSVD moderate/severe MRI burden. A nomogram predictive model was constructed based on these factors and its performance was evaluated. Results The proportions of male patients, as well as those with a history of diabetes or hypertension, were significantly higher in the moderate/severe burden group than those in the low burden group. Additionally, the age of patients in the moderate/severe burden group was significantly older, and the levels of β2-MG, Lp(a), and homocysteine (Hcy) were higher than those in the low burden group (P<0.01). Binary logistic regression analysis revealed that hypertension, diabetes, β2-MG, and Lp(a) were independent factors for CSVD moderate/severe MRI burden (P<0.05). The nomogram predictive model based on these four factors had a cut-off value of 0.467 0, with an area under curve (AUC) of 0.838 7 (95%CI 0.760 8-0.916 6) in the training set (n=97) and 0.854 1 (95%CI 0.742 1-0.966 1) in the internal validation set (n=41) . The calibration curve demonstrated good agreement between predicted and observed values. Decision curve analysis (DCA) indicated that the nomogram model had good clinical utility. Conclusions The nomogram model based on β2-MG and Lp(a) has high predictive performance in assessing the risk of CSVD moderate/severe MRI burden, with good discrimination and calibration.
6.Clinical characteristics and outcomes of psoriasis patients with COVID-19: A retrospective, multicenter cohort study in China
Yanhua LIU ; Zhongrui XU ; Jian ZHOU ; Aijun CHEN ; Junling ZHANG ; Xiaojing KANG ; Xian JIANG ; Chengzhi LYU ; Chunrui SHI ; Yuling SHI ; Xiaoming LIU ; Fuqiu LI ; Bin YANG ; Yongmei HUANG ; Chen YU ; Gang WANG
Chinese Medical Journal 2024;137(14):1736-1743
Background::Limited information exists regarding the impact of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection on psoriasis patients. The objective of this study was to identify clinical factors associated with the prognosis of psoriasis following SARS-CoV-2 infection.Methods::A retrospective, multicenter study was conducted between March and May 2023. Univariable and multivariable logistic regression analyses were employed to identify factors associated with coronavirus disease 2019 (COVID-19)-related psoriasis outcomes. The study included 2371 psoriasis patients from 12 clinical centers, with 2049 of them having been infected with SARS-CoV-2.Results::Among the infected groups, lower exacerbation rates were observed in individuals treated with biologics compared to those receiving traditional systemic or nonsystemic treatments (22.3% [236/1058] vs. 39.8% [92/231] vs. 37.5% [140/373], P <0.001). Psoriasis progression with lesions (adjusted odds ratio [OR] = 8.197, 95% confidence interval [95% CI] = 5.685–11.820, compared to no lesions), hypertension (adjusted OR = 1.582, 95% CI = 1.068–2.343), traditional systemic (adjusted OR = 1.887, 95% CI= 1.263–2.818), and nonsystemic treatment (adjusted OR= 1.602, 95% CI= 1.117–2.297) were found to be associated with exacerbation of psoriasis after SARS-CoV-2 infection, but not biologics (adjusted OR = 0.931, 95% CI = 0.680–1.274, compared to no treatment), according to multivariable logistic regression analysis. Conclusions::A reduced risk of psoriasis exacerbation after SARS-CoV-2 infection was observed with biologics compared to traditional systemic and nonsystemic treatments. Significant risk factors for exacerbation after infection were identified as existing psoriatic lesions and hypertension.
7.Population pharmacokinetics of Ainuovirine and exposure-response analysis in human immunodeficiency virus-infected individuals
Xiaoxu HAN ; Jin SUN ; Yihang ZHANG ; Taiyi JIANG ; Qingshan ZHENG ; Haiyan PENG ; Yao WANG ; Wei XIA ; Tong ZHANG ; Lijun SUN ; Xinming YUN ; Hong QIN ; Hao WU ; Bin SU
Chinese Medical Journal 2024;137(20):2473-2482
Background::Ainuovirine (ANV) is a new generation of non-nucleoside reverse transcriptase inhibitor for the treatment of human immunodeficiency virus (HIV) type 1 infection. This study aimed to evaluate the population pharmacokinetic (PopPK) profile and exposure-response relationship of ANV among people living with HIV.Methods::Plasma concentration-time data from phase 1 and phase 3 clinical trials of ANV were pooled for developing the PopPK model. Exposure estimates obtained from the final model were used in exposure-response analysis for virologic responses and safety responses.Results::ANV exhibited a nonlinear pharmacokinetic profile, which was best described by a two-compartment model with first-order elimination. There were no significant covariates correlated to the pharmacokinetic parameters of ANV. The PopPK parameter estimate (relative standard error [%]) for clearance adjusted for bioavailability (CL/F) was 6.46 (15.00) L/h, and the clearance of ANV increased after multiple doses. The exposure-response model revealed no significant correlation between the virologic response (HIV-RNA <50 copies/mL) at 48 weeks and the exposure, but the incidence of adverse events increased with the increasing exposure ( P value of steady-state trough concentration and area under the steady-state curve were 0.0177 and 0.0141, respectively). Conclusions::Our PopPK model supported ANV 150 mg once daily as the recommended dose for people living with HIV, requiring no dose adjustment for the studied factors. Optimization of ANV dose may be warranted in clinical practice due to an increasing trend in adverse reactions with increasing exposure.Trial registration::Chinese Clinical Trial Registry https://www.chictr.org.cn (Nos. ChiCTR1800018022 and ChiCTR1800019041).
8.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.
9.Changing distribution and resistance profiles of common pathogens isolated from urine in the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yanming LI ; Mingxiang ZOU ; Wen'en LIU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; 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 ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):287-299
Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8%(62 219/261 893),and gram-negative bacteria 76.2%(199 674/261 893).The most common species were E.coli(46.7%),E.faecium(10.4%),K.pneumoniae(9.8%),E.faecalis(8.7%),P.mirabilis(3.5%),P.aeruginosa(3.4%),SS.agalactiae(2.6%),and E.cloacae(2.1%).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2%,52.8%and 37.0%,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7%,18.5%,16.4%,and 40.3%,respectively.Lower than 10%of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90%of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was<2%.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.
10.Changing resistance profiles of Enterococcus in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Na CHEN ; Ping JI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; 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 ; Chao YAN ; 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 ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):300-308
Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7%)and Enterococcus faecalis(41.5%).The strains were mainly isolated from urinary tract specimens(46.9%±2.6%),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6%).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1%and 1.3%,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7%in 2015 to 3.4%in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3%.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.

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