1.A case-control study on the association of Hashimoto’s thyroiditis and anti-thyroid antibodies with oral lichen planus
LIU Yuan ; CHEN Yan ; CONG Zhaoxia ; LI Yiming ; XUE Rui ; ZHAO Jin
Journal of Prevention and Treatment for Stomatological Diseases 2025;33(9):757-764
Objective:
This study aims to explore the association between oral lichen planus (OLP) and Hashimoto’s thyroiditis (HT) and its anti-thyroid antibodies to provide clinical evidence for thyroid disease screening in patients with OLP.
Methods:
This study was approved by the institutional ethics committee. A total of 125 clinically and histopathologically confirmed patients with OLP were enrolled as the case group, and they were matched with 125 non-OLP controls based on sex and age. Demographic data (gender, age, lesion type, and disease duration) were collected from both groups. Serum levels of thyroid peroxidase antibodies (TPOAb) and thyroglobulin antibodies (TgAb) were measured to analyze their associations with sex, age, lesion type, and disease duration in patients with OLP.
Result:
The prevalence of HT in patients with OLP was 31.20%, significantly higher than that in the control group (9.60%) (χ2=18.504, P<0.001). The prevalence of HT in female patients with OLP (39.13%) was significantly higher than that in male patients (9.09%)(χ2=10.93,P<0.001). The positivity rate of thyroid peroxidase antibodies (TPOAb) in patients with OLP (17.6%) was significantly higher than in the control group (4.0%) (χ2=10.989, P<0.001). The TPOAb positivity rate was significantly higher in female patients (22.83%) than in male patients (3.03%) (χ2=5.210, P=0.014). There was no statistically significant difference in the positivity rate of TgAb between patients with OLP (7.2%) and the control group (3.2%) (P>0.05). Patients with erosive lesions had a significantly higher TPOAb positivity rate (25.0%, 17/68) compared to those with non-erosive lesions (8.77%, 5/57), and the difference was statistically significant (χ2=4.831, P=0.028). Logistic regression analysis revealed that female patients with OLP had an 8.935-fold higher risk of being TPOAb positive compared to males (OR=8.935, 95%CI: 1.134-70.388, P=0.038). Patients with erosive OLP lesions had a 3.199-fold higher risk of TPOAb positivity compared to those with non-erosive lesions (OR=3.199, 95%CI: 1.064-9.618, P=0.038).
Conclusion
The prevalence of HT is higher in patients with OLP, with higher positivity rates of anti-thyroid antibodies observed in female patients and those with erosive OLP lesions. This suggests that thyroid disease screening should be incorporated into the clinical management of patients with OLP, especially for women and patients who present with erosive lesions.
2.Radiomics combined with interpretable machine learning in predicting the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
Jianfeng LI ; Meijuan SUN ; Haiyan PENG ; Wenyou HU ; Fu JIN ; Zhaoxia LI ; Ning WANG
Chinese Journal of Medical Physics 2025;42(5):625-631
The efficacy of preoperative neoadjuvant chemoradiotherapy(nCRT)in locally advanced rectal cancer(LARC)is predicted using radiomic features of the target areas in radiotherapy for rectal cancer and an interpretable machine learning model.The clinical data are collected from 290 LARC patients who are divided into effective and ineffective groups based on tumor regression grade.The extracted radiomic features and clinicopathological data are used to develop prediction models.The optimal model is determined based on AUC performance evaluation,and the explanatory analysis is conducted using nomogram and decision curve.A total of 223 patients are included in the study,with 48 in the effective group.There are 156 patients in the training set(34 in the effective group)and 67 patients in the validation set(14 in the effective group).The nomogram model shows the best performance,with AUC of 0.858 in the training set and 0.844 in internal test set,and decision curve analysis demonstrated its superior net clinical benefit across most threshold ranges than other models.Combining radiomics and clinical variables,the nomogram can effectively predict nCRT outcomes and support clinical decision-making.
3.Changing distribution and antimicrobial resistance profiles of clinical isolates in children:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Qing MENG ; Lintao ZHOU ; Yunsheng CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Chuanqing WANG ; Aimin WANG ; Lei ZHU ; Jinhua MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Zhiyong LÜ ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Fangfang HU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Wei JIA ; Gang LI ; Kaizhen WEN ; Yirong ZHANG ; Yan JIN ; Chunhong SHAO ; Yong ZHAO ; Ping GONG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Sufang GUO ; Jiao FENG ; Ziyong SUN ; Zhongju CHEN ; Wen'en LIU ; Yanming LI ; Xiaobo MA ; Yanping ZHENG ; Dawen GUO ; Jinying ZHAO ; Ruizhong WANG ; Hua FANG ; Lixia ZHANG ; Juan MA ; Jihong LI ; Zhidong HU ; Jin LI ; Yuxing NI ; Jingyong SUN ; Ruyi GUO ; Yan ZHU ; Yi XIE ; Mei KANG ; Yuanhong XU ; Ying HUANG ; Shanmei WANG ; Yafei CHU ; Hua YU ; Xiangning HUANG ; Lianhua WEI ; Fengmei ZOU ; Han SHEN ; Wanqing ZHOU ; Yunzhuo CHU ; Sufei TIAN ; Shunhong XUE ; Hongqin GU ; Xuesong XU ; Chao YAN ; Bixia YU ; Jinju DUAN ; Jianbang KANG ; Jiangshan LIU ; Xuefei HU ; Yunsong YU ; Jie LIN ; Yunjian HU ; Xiaoman AI ; Chunlei YUE ; Jinsong WU ; Yuemei LU
Chinese Journal of Infection and Chemotherapy 2025;25(1):48-58
Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1%of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9%)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5%)was lower than that in inpatient children(31.5%).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1%)than in inpatient children(59.4%).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14%,11.7%,47.8%in outpatients,but 24.2%,20.6%,and 52.8%in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.
4.Surveillance of antimicrobial resistance in clinical isolates of Escherichia coli:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shanmei WANG ; Bing MA ; Yi LI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Aimin WANG ; 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 ; 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 2025;25(1):39-47
Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)%.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0%vs 29.5%).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7%vs 7.8%,0.8%vs 0.1%,both P<0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)%with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9%and 1.8%,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1%).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10%.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P<0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.
5.Radiomics combined with interpretable machine learning in predicting the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
Jianfeng LI ; Meijuan SUN ; Haiyan PENG ; Wenyou HU ; Fu JIN ; Zhaoxia LI ; Ning WANG
Chinese Journal of Medical Physics 2025;42(5):625-631
The efficacy of preoperative neoadjuvant chemoradiotherapy(nCRT)in locally advanced rectal cancer(LARC)is predicted using radiomic features of the target areas in radiotherapy for rectal cancer and an interpretable machine learning model.The clinical data are collected from 290 LARC patients who are divided into effective and ineffective groups based on tumor regression grade.The extracted radiomic features and clinicopathological data are used to develop prediction models.The optimal model is determined based on AUC performance evaluation,and the explanatory analysis is conducted using nomogram and decision curve.A total of 223 patients are included in the study,with 48 in the effective group.There are 156 patients in the training set(34 in the effective group)and 67 patients in the validation set(14 in the effective group).The nomogram model shows the best performance,with AUC of 0.858 in the training set and 0.844 in internal test set,and decision curve analysis demonstrated its superior net clinical benefit across most threshold ranges than other models.Combining radiomics and clinical variables,the nomogram can effectively predict nCRT outcomes and support clinical decision-making.
6.Changing distribution and antimicrobial resistance profiles of clinical isolates in children:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Qing MENG ; Lintao ZHOU ; Yunsheng CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Chuanqing WANG ; Aimin WANG ; Lei ZHU ; Jinhua MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Zhiyong LÜ ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Fangfang HU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Wei JIA ; Gang LI ; Kaizhen WEN ; Yirong ZHANG ; Yan JIN ; Chunhong SHAO ; Yong ZHAO ; Ping GONG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Sufang GUO ; Jiao FENG ; Ziyong SUN ; Zhongju CHEN ; Wen'en LIU ; Yanming LI ; Xiaobo MA ; Yanping ZHENG ; Dawen GUO ; Jinying ZHAO ; Ruizhong WANG ; Hua FANG ; Lixia ZHANG ; Juan MA ; Jihong LI ; Zhidong HU ; Jin LI ; Yuxing NI ; Jingyong SUN ; Ruyi GUO ; Yan ZHU ; Yi XIE ; Mei KANG ; Yuanhong XU ; Ying HUANG ; Shanmei WANG ; Yafei CHU ; Hua YU ; Xiangning HUANG ; Lianhua WEI ; Fengmei ZOU ; Han SHEN ; Wanqing ZHOU ; Yunzhuo CHU ; Sufei TIAN ; Shunhong XUE ; Hongqin GU ; Xuesong XU ; Chao YAN ; Bixia YU ; Jinju DUAN ; Jianbang KANG ; Jiangshan LIU ; Xuefei HU ; Yunsong YU ; Jie LIN ; Yunjian HU ; Xiaoman AI ; Chunlei YUE ; Jinsong WU ; Yuemei LU
Chinese Journal of Infection and Chemotherapy 2025;25(1):48-58
Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1%of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9%)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5%)was lower than that in inpatient children(31.5%).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1%)than in inpatient children(59.4%).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14%,11.7%,47.8%in outpatients,but 24.2%,20.6%,and 52.8%in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.
7.Surveillance of antimicrobial resistance in clinical isolates of Escherichia coli:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shanmei WANG ; Bing MA ; Yi LI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Aimin WANG ; 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 ; 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 2025;25(1):39-47
Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)%.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0%vs 29.5%).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7%vs 7.8%,0.8%vs 0.1%,both P<0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)%with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9%and 1.8%,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1%).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10%.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P<0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.
8.Analysis of oligoclonal bands detection results of 3 217 patients with neurological disorders
Zhenyu NIU ; Haiqiang JIN ; Hongjun HAO ; Yiming ZHENG ; Jing GUO ; Yan YAO ; Feng GAO ; Zhaoxia WANG
Chinese Journal of Laboratory Medicine 2024;47(1):57-64
Objective:To study positive rates and typing of oligoclonal bands (OCB) in patients with neurological disorders, and to reveal the clinical significance and applicational value of OCB test.Methods:A retrospective analysis was performed on the detection results of 3 217 patients with neurological disorders who undertook both serum and cerebrospinal fluid OCBs in the First Hospital of Peking University from January 2012 to August 2022. According to the final diagnosis, the patients were divided into 13 groups including multiple sclerosis (479 cases), neuromyelitis optica spectrum disorders (935 cases), autoimmune encephalitis (192 cases), viral encephalitis (94 cases), nervous system complication after HSCT (232 cases), Guillain-Barré syndrome (644 cases), chronic inflammatory demyelinating polyneuropathy (157 cases), etc. Cerebrospinal fluid and serum OCBs were detected using isoelectric focusing electrophoresis combining immunofixation, then classified into Ⅰ-Ⅴ types according to the morphology. Consequently, positive rates and types were analyzed for each group. χ2 test was used for comparison between groups. Results:The positive rates of cerebrospinal fluid OCB in multiple sclerosis, nervous system complication after hematopoietic stem cell transplantation (HSCT), autoimmune encephalitis, viral encephalitis, neuromyelitis optica spectrum disorders, Guillain-Barré syndrome and chronic inflammatory demyelinating polyneuropathy were respectively 66.8% (320/479), 48.7% (113/232), 46.4%(89/192), 19.1% (18/94), 17.6% (165/935), 9.9% (64/644), 5.1% (8/157). For patients with multiple sclerosis, neuromyelitis optica spectrum disorders, viral encephalitis, and autoimmune encephalitis, Type Ⅱ bands took the majority of cerebrospinal fluid OCB-positive cases with the rates of 94.1% (301/320), 78.7% (70/89), 77.8% (14/18), and 77.6% (128/165) respectively, indicating intrathecal IgG synthesis; for patients with nervous system complication after HSCT, Guillain-Barré syndrome and chronic inflammatory demyelinating polyneuropathy, type Ⅳ bands took the majority of cerebrospinal fluid OCB-positive cases with the rates of 94.7% (107/113), 82.8% (53/64) and 100% (8/8), indicating no obvious intrathecal IgG synthesis. The positive rates of cerebrospinal fluid oligoclonal bands were significantly different among all groups (χ 2=1 268.31, P<0.001). Conclusion:The positive rates of cerebrospinal fluid oligoclonal bands are different among different neurological disorders, in which the positive rate of cerebrospinal fluid OCB is higher with type Ⅱ bands as the majority type in multiple sclerosis, which indicates that the detection and typing of cerebrospinal fluid OCB are helpful for the diagnosis of various neurological diseases, especially for multiple sclerosis.
9.Systemic factors influencing the complexity and surgical prognosis of proliferative diabetic retinopathy
Lijun PU ; Jin LIU ; Zhaoxia MOU ; Songtao YUAN ; Ping XIE ; Qinghuai LIU ; Zizhong HU
Chinese Journal of Experimental Ophthalmology 2024;42(8):729-735
Objective:To evaluate the risk factors for the complexity and surgical prognosis in patients with proliferative diabetic retinopathy (PDR).Methods:A historical cohort study of the CONCEPT trial, including 97 patients (97 eyes) who were diagnosed with PDR and requiring three-channel 23-gauge transconjunctival pars plana vitrectomy (PPV) from June 2017 to January 2018 at the First Affiliated Hospital of Nanjing Medical University.All patients received preoperative intravitreal injection of 0.5 mg conbercpet.Based on the PDR complexity score, patients were divided into >3 group or ≤3 group, and the systematic risk factors were compared between the two groups.The influence of sex, age, hypertension, renal insufficiency, duration of diabetes mellitus, and hemoglobin A1c level on the PDR complexity score was evaluated by multivariate logistic regression analysis.Based on age, patients were divided into <45 years group, 45-<60 years group, and ≥60 years group, and the differences in mean operative time, incidence of intraoperative hemorrhage, surgically induced lacrimation and silicone oil filling, and incidence of hemorrhage on color fundus photos and macular edema by optical coherence tomography at postoperative months 1 and 6 were analyzed among different age groups.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of The First Affiliated Hospital of Nanjing Medical University (No.2017-SR-283).Written informed consent was obtained from each subject.Results:The age of patients with PDR complexity score >3 was 46.5(36.0, 51.8) years, which was less than 54.0(45.5, 61.5) years for PDR complexity score ≤3, and the difference was statistically significant ( Z=1.835, P=0.002).Among the factors predicting PDR complexity score >3, logistic regression analysis indicated that only age was statistically significant ( P=0.005).For each 1-year increase in age, the risk of PDR complexity score >3 would increase by 7.4%( OR: 0.929, 95% CI: 0.883-0.977).Among the systemic factors, there were significant differences in age, history of diabetes, proportion of patients with hypertension and renal insufficiency among the three age groups (all at P<0.05).Among the ocular factors, there were significant differences in the proportion of patients with history of retinal laser treatment, fibrovascular membrane and complexity score >3 among the three groups (all at P<0.05).The proportion of patients with fibrovascular membrane and complexity score >3 in the <45 years group was significantly higher than that in the 45-<60 and ≥60 years groups (all at P<0.05).There were significant differences in the proportion of patients with intraoperative bleeding and silicone oil filling in the three age groups (all at P<0.017).The proportion of intraoperative bleeding and silicone oil filling in <45 years group was significantly higher than that in 45-<60 and ≥60 years groups (all at P<0.05).The macular edema on postoperative month 1 in the <45 years old group was significantly higher than that in the 45-<60 and ≥60 years groups (both at P<0.05). Conclusions:Among systemic factors, age has a significant impact on the increased PDR complexity and contributes to the poor prognosis of patients.There is a higher percentage of intraoperative complications and early postoperative macular edema in patients in the younger age group compared to the older age group.
10.Changing distribution and antimicrobial resistance profiles of clinical isolates from wound pus:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yu ZHANG ; Ying HUANG ; Yuanhong XU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Aimin WANG ; 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(6):690-699
Objective To investigate the distribution and antimicrobial resistance profiles of the clinical isolates from wound pus in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods All the bacterial strains were isolated from wound pus samples from 2015 to 2021.The isolates were identified according to conventional methods.Antimicrobial susceptibility test was conducted by disk diffusion method or commercial automated susceptibility testing systems according to CHINET-specified uniform protocol.The results are interpreted according to the Clinical and Laboratory Standards Institute (CLSI) breakpoints (2021 Edition).Results A total of 90856 bacterial strains were isolated from wound pus samples from 2015 to 2021,of which gram positive bacteria accounted for 36.0% (32729/90856) and gram negative bacteria accounted for 64.0% (58127/90856).The most common bacterial species were Escherichia coli,Staphylococcus aureus,Klebsiella pneumoniae,Pseudomonas aeruginosa,and Enterococcus.About 88.9% of these strains were isolated from inpatients and 11.1% from outpatients.The strains collected from surgery department and internal medicine accounted for (53.4±3.6)% (49191/90856) and (9.6±1.0)% (8960/90856) on average over the 7-year period.E.coli showed low level resistance to carbapenems (1.1%).The prevalence of ESBLs-producing E.coli was 51.1%.More than 35% of the E.coli isolates were resistant to cefotaxime,ciprofloxacin,and trimethoprim-sulfamethoxazole.The prevalence of ESBLs-producing K.pneumoniae was 29.7%.The prevalence of imipenem-resistant and meropenem-resistant K.pneumoniae varied from 2015 to 2021,but reached the peak level (12.5% and 12.7%) in 2020.However,other Enterobacterales species showed low resistance rates to carbapenems.The prevalence of ESBLs-producing Klebsiella oxytoca and Proteus was 18.3% and 32.5%,respectively.About 13.1% and 10.6% of P.aeruginosa isolates were resistant to imipenem and meropenem,respectively.However,71.1% and 72.4% of A.baumannii isolates were resistant to imipenem and meropenem,respectively.The overall prevalence of MRSA was 22.7% in wound pus samples over the 7-year period.Three vancomycin-resistant strains and 122 linezolid-resistant isolates were identified in Enterococcus faecalis.Thirty-one vancomycin-resistant strains and 11 linezolid-resistant strains were detected in Enterococcus faecium.Conclusions The overall prevalence of MRSA,vancomycin-resistant Enterococcus (VRE),linezolid-resistant Enterococcus (LRE),ESBLs-producing Enterobacterales,and carbapenem-resistant organisms (CRO) in the isolates from wound pus samples was relatively lower than the corresponding prevalence in the total clinical isolates collected in the CHINET program.This finding suggests that the antimicrobial resistance profile of bacterial isolates may vary with the source of clinical samples.Therefore,we should strengthen the antimicrobial resistance surveillance for the isolates from different sites of infection.


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