1.Analysis of risk factors, pathogenic bacteria characteristics, and drug resistance of postoperative surgical site infection in adults with limb fractures.
Yan-Jun WANG ; Zi-Hou ZHAO ; Shuai-Kun LU ; Guo-Liang WANG ; Shan-Jin MA ; Lin-Hu WANG ; Hao GAO ; Jun REN ; Zhong-Wei AN ; Cong-Xiao FU ; Yong ZHANG ; Wen LUO ; Yun-Fei ZHANG
Chinese Journal of Traumatology 2025;28(4):241-251
PURPOSE:
We carried out the study aiming to explore and analyze the risk factors, the distribution of pathogenic bacteria, and their antibiotic-resistance characteristics influencing the occurrence of surgical site infection (SSI), to provide valuable assistance for reducing the incidence of SSI after traumatic fracture surgery.
METHODS:
A retrospective case-control study enrolling 3978 participants from January 2015 to December 2019 receiving surgical treatment for traumatic fractures was conducted at Tangdu Hospital of Air Force Medical University. Baseline data, demographic characteristics, lifestyles, variables related to surgical treatment, and pathogen culture were harvested and analyzed. Univariate analyses and multivariate logistic regression analyses were used to reveal the independent risk factors of SSI. A bacterial distribution histogram and drug-sensitive heat map were drawn to describe the pathogenic characteristics.
RESULTS:
Included 3978 patients 138 of them developed SSI with an incidence rate of 3.47% postoperatively. By logistic regression analysis, we found that variables such as gender (males) (odds ratio (OR) = 2.012, 95% confidence interval (CI): 1.235 - 3.278, p = 0.005), diabetes mellitus (OR = 5.848, 95% CI: 3.513 - 9.736, p < 0.001), hypoproteinemia (OR = 3.400, 95% CI: 1.280 - 9.031, p = 0.014), underlying disease (OR = 5.398, 95% CI: 2.343 - 12.438, p < 0.001), hormonotherapy (OR = 11.718, 95% CI: 6.269 - 21.903, p < 0.001), open fracture (OR = 29.377, 95% CI: 9.944 - 86.784, p < 0.001), and intraoperative transfusion (OR = 2.664, 95% CI: 1.572 - 4.515, p < 0.001) were independent risk factors for SSI, while, aged over 59 years (OR = 0.132, 95% CI: 0.059 - 0.296, p < 0.001), prophylactic antibiotics use (OR = 0.082, 95% CI: 0.042 - 0.164, p < 0.001) and vacuum sealing drainage use (OR = 0.036, 95% CI: 0.010 - 0.129, p < 0.001) were protective factors. Pathogens results showed that 301 strains of 38 species of bacteria were harvested, among which 178 (59.1%) strains were Gram-positive bacteria, and 123 (40.9%) strains were Gram-negative bacteria. Staphylococcus aureus (108, 60.7%) and Enterobacter cloacae (38, 30.9%) accounted for the largest proportion. The susceptibility of Gram-positive bacteria to Vancomycin and Linezolid was almost 100%. The susceptibility of Gram-negative bacteria to Imipenem, Amikacin, and Meropenem exceeded 73%.
CONCLUSION
Orthopedic surgeons need to develop appropriate surgical plans based on the risk factors and protective factors associated with postoperative SSI to reduce its occurrence. Meanwhile, it is recommended to strengthen blood glucose control in the early stage of admission and for surgeons to be cautious and scientific when choosing antibiotic therapy in clinical practice.
Humans
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Surgical Wound Infection/epidemiology*
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Male
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Female
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Risk Factors
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Retrospective Studies
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Middle Aged
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Adult
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Case-Control Studies
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Fractures, Bone/surgery*
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Aged
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Drug Resistance, Bacterial
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Logistic Models
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Anti-Bacterial Agents/therapeutic use*
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Incidence
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Bacteria/drug effects*
2.Veronica anagallis-aquatica L. iridoid glycosides alleviate heart failure via metabolites homoveratrumic acid and 2-hydroxy-3,4-dimethoxybenzoic acid mediated by the gut microbiota.
Manjiong WANG ; Xiaobo GUO ; Hanfang LIU ; Xiao LI ; Yue YAO ; Qing FU ; Yu JIN ; Shuaishuai NI ; Xiaokang LI ; Chaojiang XIAO ; Bei JIANG ; Conglong XIA ; Jian LI ; Yixiang XU
Acta Pharmaceutica Sinica B 2025;15(6):3338-3342
The iridoid glycosides from Veronica anagallis-aquatica L. alleviate heart failure by modulating the gut microbiota and influencing the production of two metabolites with potential antihypertrophic effects, HVA and 2OH-VA.Image 1.
3.Inflammatory Bowel Disease and Dementia: Evidence Triangulation from a Meta-Analysis of Observational Studies and Mendelian Randomization Study.
Di LIU ; Mei Ling CAO ; Shan Shan WU ; Bing Li LI ; Yi Wen JIANG ; Teng Fei LIN ; Fu Xiao LI ; Wei Jie CAO ; Jin Qiu YUAN ; Feng SHA ; Zhi Rong YANG ; Jin Ling TANG
Biomedical and Environmental Sciences 2025;38(1):56-66
OBJECTIVE:
Observational studies have found associations between inflammatory bowel disease (IBD) and the risk of dementia, including Alzheimer's dementia (AD) and vascular dementia (VD); however, these findings are inconsistent. It remains unclear whether these associations are causal.
METHODS:
We conducted a meta-analysis by systematically searching for observational studies on the association between IBD and dementia. Mendelian randomization (MR) analysis based on summary genome-wide association studies (GWASs) was performed. Genetic correlation and Bayesian co-localization analyses were used to provide robust genetic evidence.
RESULTS:
Ten observational studies involving 80,565,688 participants were included in this meta-analysis. IBD was significantly associated with dementia (risk ratio [ RR] =1.36, 95% CI = 1.04-1.78; I 2 = 84.8%) and VD ( RR = 2.60, 95% CI = 1.18-5.70; only one study), but not with AD ( RR = 2.00, 95% CI = 0.96-4.13; I 2 = 99.8%). MR analyses did not supported significant causal associations of IBD with dementia (dementia: odds ratio [ OR] = 1.01, 95% CI = 0.98-1.03; AD: OR = 0.98, 95% CI = 0.95-1.01; VD: OR = 1.02, 95% CI = 0.97-1.07). In addition, genetic correlation and co-localization analyses did not reveal any genetic associations between IBD and dementia.
CONCLUSION
Our study did not provide genetic evidence for a causal association between IBD and dementia risk. The increased risk of dementia observed in observational studies may be attributed to unobserved confounding factors or detection bias.
Humans
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Mendelian Randomization Analysis
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Inflammatory Bowel Diseases/complications*
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Dementia/etiology*
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Observational Studies as Topic
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Genome-Wide Association Study
5.Multi-parametric MRI radiomics-based nomogram model for predicting the lymphovascular space invasion of endometrial endometrioid adenocarcinoma
Xiao-Liang MA ; Min-Hua SHEN ; Feng-Hua MA ; Guo-Fu ZHANG ; Jian-Jun ZHOU ; Meng-Su ZENG ; Jin-Wei QIANG
Fudan University Journal of Medical Sciences 2024;51(3):306-314,322
Objective To investigate the feasibility and value of a multi-parametric MRI radiomics-based nomogram model for pretreatment predicting the lymphovascular space invasion(LVSI)of endometrial endometrioid adenocarcinoma(EEA).Methods Preoperative MRI and baseline clinical characteristics of 205 EEA patients were prospectively collected from Oct 2020 to Jan 2022 in the Obstetrics and Gynecology Hospital,Fudan University,and randomly divided into training set(n=123)and validation set(n=82)in a 6∶4 ratio.The whole-tumor region of interest was manually drawn on T2-weighted imaging,diffusion-weighted imaging(apparent diffusion coefficient),and dynamic contrast-enhanced MRI,respectively,for radiomics features extraction.In the training set,univariate and multivariate Logistic regression analysis were used to select independent clinical predictors of LVSI(+)and construct the clinical model.The least absolute shrinkage and selection operator(LASSO)regression and multivariate Logistic regression analysis were used to select optimal radiomics features to form a radiomics signature.A combined nomogram model was established by integrating clinical independent predictors and the radiomics signature,and validated in the validation set.The predicting performance and clinical net benefit were evaluated by using the area under the receiver operating characteristic curve(AUC)and clinical decision curve analysis,respectively.Results Of the 205 EEA cases,144 cases were LVSI(-)and 61 cases were LVSI(+).Menopausal status,CA125,and CA199 were independent clinical predictors for the LVSI(+),and contributing to a clinical model with AUCs of 0.714(training)and 0.731(validation).From 8 240 extracted radiomics features,five were selected to construct a MRI radiomics signature after de-redundancy and LASSO dimensionality reduction,yielding AUCs of 0.860(training)and 0.759(validation).The combined nomogram model showed AUCs of 0.887(training)and 0.807(validation),outperforming others and achieving maximum clinical benefit in a large range of threshold probability in both training and validation sets.Conclusion The multi-parametric MRI-based nomogram model has the potential for pretreatment predicting the LVSI status of EEA,providing valuable information for clinical management decision-making and improving patient's clinical benefits.
6.MRI-based habitat radiomics analysis for identifying molecular subtypes of endometrial cancer:a feasible study from two institutions
Wen-Tao JIN ; Tian-Ping WANG ; Xiao-Jun CHEN ; Guo-Fu ZHANG ; Hai-Ming LI ; He ZHANG
Fudan University Journal of Medical Sciences 2024;51(6):890-899
Objective To develop an MRI-based habitat radiomics model for the preoperative prediction of endometrial cancer(EC)molecular subtypes.Methods Patients with pathologically proven EC from two hospitals were included in the training(n=270)and testing(n=70)cohorts.All patients had preoperative MRI and histological and molecular diagnoses.First,the tumor was divided into habitat subregions based on diffusion-weighted imaging(DWI)and contrast-enhanced(CE)images.Subsequently,habitat radiomic features were extracted from different subregions of T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),DWI,and CE images.Three machine learning classifiers,including logistic regression,support vector machines,and random forests,were applied to develop predictive models for p53-abnormal endometrial cancer,with model performance validated.The model demonstrating the best overall predictive performance was selected as the habitat radiomics model.Using the same procedure,a whole-region radiomics model based on T1WI,T2WI,DWI,and CE sequences and a clinical model were constructed.The performance of the models was evaluated using receiver operating characteristic curves,and DeLong's test was employed to compare differences between the models.Decision curve analysis was used to assess the clinical benefits of the models'application.Results After feature selection,eight habitat radiomic features were retained to construct the habitat radiomics model,ten features for the whole-region radiomics model,and three clinical features for the clinical model.The habitat radiomics model achieved the highest area under the curve(AUC),with 0.855(0.788-0.922)in the training cohort and 0.769(0.631-0.907)in the testing cohort.DeLong's test showed that the habitat radiomics model outperformed the whole-region radiomics model in the training cohort(P=0.001),but there was no significant difference in the testing cohort(P=0.543).In both cohorts,the habitat radiomics model outperformed the clinical model(P=0.007,training cohort;P=0.038,testing cohort).Decision curve analysis(DCA)demonstrated that this model provided clinical benefit for diagnosis within a threshold probability range of approximately 0.2-0.8.Conclusion The MRI-based habitat radiomics model can accurately predict p53-abnormal EC,outperforming both the whole-region radiomics model and the clinical model,and is useful for the non-invasive molecular subtyping of endometrial cancer before surgery.
8.Carrier screening for 223 monogenic diseases in Chinese population:a multi-center study in 33 104 individuals
Wei HOU ; Xiaolin FU ; Xiaoxiao XIE ; Chunyan ZHANG ; Jiaxin BIAN ; Xiao MAO ; Juan WEN ; Chunyu LUO ; Hua JIN ; Qian ZHU ; Qingwei QI ; Yeqing QIAN ; Jing YUAN ; Yanyan ZHAO ; Ailan YIN ; Shutie LI ; Yulin JIANG ; Manli ZHANG ; Rui XIAO ; Yanping LU
Journal of Southern Medical University 2024;44(6):1015-1023
Objective To investigate the epidemiological characteristics and mutation spectrum of monogenic diseases in Chinese population through a large-scale,multicenter carrier screening.Methods This study was conducted among a total of 33 104 participants(16 610 females)from 12 clinical centers across China.Carrier status for 223 genes was analyzed using high-throughput sequencing and different PCR methods.Results The overall combined carrier frequency was 55.58%for 197 autosomal genes and 1.84%for 26 X-linked genes in these participants.Among the 16 669 families,874 at-risk couples(5.24%)were identified.Specifically,584 couples(3.50%)were at risk for autosomal genes,306(1.84%)for X-linked genes,and 16 for both autosomal and X-linked genes.The most frequently detected autosomal at-risk genes included GJB2(autosomal recessive deafness type 1A,393 couples),HBA1/HBA2(α-thalassemia,36 couples),PAH(phenylketonuria,14 couples),and SMN1(spinal muscular atrophy,14 couples).The most frequently detected X-linked at-risk genes were G6PD(G6PD deficiency,236 couples),DMD(Duchenne muscular dystrophy,23 couples),and FMR1(fragile X syndrome,17 couples).After excluding GJB2 c.109G>A,the detection rate of at-risk couples was 3.91%(651/16 669),which was lowered to 1.72%(287/16 669)after further excluding G6PD.The theoretical incidence rate of severe monogenic birth defects was approximately 4.35‰(72.5/16 669).Screening for a battery of the top 22 most frequent genes in the at-risk couples could detect over 95%of at-risk couples,while screening for the top 54 genes further increased the detection rate to over 99%.Conclusion This study reveals the carrier frequencies of 223 monogenic genetic disorders in the Chinese population and provides evidence for carrier screening strategy development and panel design tailored to the Chinese population.In carrier testing,genetic counseling for specific genes or gene variants can be challenging,and the couples need to be informed of these difficulties before testing and provided with options for not screening these genes or gene variants.
9.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.
10.Changing resistance profiles of Staphylococcus isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yuling XIAO ; Mei KANG ; Yi XIE ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Ping JI ; Fengbo ZHANG ; 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 ; Wenhui HUANG ; 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(5):570-580
Objective To investigate the changing distribution and antibiotic resistance profiles of clinical isolates of Staphylococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Staphylococcus according to the unified protocol of CHINET(China Antimicrobial Surveillance Network)using disk diffusion method and commercial automated systems.The CHINET antimicrobial resistance surveillance data from 2015 to 2021 were interpreted according to the 2021 CLSI breakpoints and analyzed using WHONET 5.6.Results During the period from 2015 to 2021,a total of 204,771 nonduplicate strains of Staphylococcus were isolated,including 136,731(66.8%)strains of Staphylococcus aureus and 68,040(33.2%)strains of coagulase-negative Staphylococcus(CNS).The proportions of S.aureus isolates and CNS isolates did not show significant change.S.aureus strains were mainly isolated from respiratory specimens(38.9±5.1)%,wound,pus and secretions(33.6±4.2)%,and blood(11.9±1.5)%.The CNS strains were predominantly isolated from blood(73.6±4.2)%,cerebrospinal fluid(12.1±2.5)%,and pleural effusion and ascites(8.4±2.1)%.S.aureus strains were mainly isolated from the patients in ICU(17.0±7.3)%,outpatient and emergency(11.6±1.7)%,and department of surgery(11.2±0.9)%,whereas CNS strains were primarily isolated from the patients in ICU(32.2±9.7)%,outpatient and emergency(12.8±4.7)%,and department of internal medicine(11.2±1.9)%.The prevalence of methicillin-resistant strains was 32.9%in S.aureus(MRSA)and 74.1%in CNS(MRCNS).Over the 7-year period,the prevalence of MRSA decreased from 42.1%to 29.2%,and the prevalence of MRCNS decreased from 82.1%to 68.2%.MRSA showed higher resistance rates to all the antimicrobial agents tested except trimethoprim-sulfamethoxazole than methicillin-susceptible S.aureus(MSSA).Over the 7-year period,MRSA strains showed decreasing resistance rates to gentamicin,rifampicin,and levofloxacin,MRCNS showed decreasing resistance rates to gentamicin,erythromycin,rifampicin,and trimethoprim-sulfamethoxazole,but increasing resistance rate to levofloxacin.No vancomycin-resistant strains were detected.The prevalence of linezolid-resistant MRCNS increased from 0.2%to 2.3%over the 7-year period.Conclusions Staphylococcus remains the major pathogen among gram-positive bacteria.MRSA and MRCNS were still the principal antibiotic-resistant gram-positive bacteria.No S.aureus isolates were found resistant to vancomycin or linezolid,but linezolid-resistant strains have been detected in MRCNS isolates,which is an issue of concern.

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