1.Identification of telomere-related diagnostic markers in osteoarthritis based on bioinformatics analysis and machine learning
Sheng XU ; Jia YE ; Xiaochong CAI
The Korean Journal of Physiology and Pharmacology 2025;29(3):359-372
Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.
2.Identification of telomere-related diagnostic markers in osteoarthritis based on bioinformatics analysis and machine learning
Sheng XU ; Jia YE ; Xiaochong CAI
The Korean Journal of Physiology and Pharmacology 2025;29(3):359-372
Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.
3.Identification of telomere-related diagnostic markers in osteoarthritis based on bioinformatics analysis and machine learning
Sheng XU ; Jia YE ; Xiaochong CAI
The Korean Journal of Physiology and Pharmacology 2025;29(3):359-372
Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.
4.Identification of telomere-related diagnostic markers in osteoarthritis based on bioinformatics analysis and machine learning
Sheng XU ; Jia YE ; Xiaochong CAI
The Korean Journal of Physiology and Pharmacology 2025;29(3):359-372
Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.
5.Identification of telomere-related diagnostic markers in osteoarthritis based on bioinformatics analysis and machine learning
Sheng XU ; Jia YE ; Xiaochong CAI
The Korean Journal of Physiology and Pharmacology 2025;29(3):359-372
Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.
6.Clinical Manifestations,Molecular Genetics and Gonadal Pathology of 416 Patients with Disorders of Sex Development:A Single-Center Cohort Study
Wanjun LIN ; Cuili LIANG ; Wen FU ; Liyu ZHANG ; Wei JIA ; Jinhua HU ; Wen ZHANG ; Yunting LIN ; Huilin NIU ; Liping FAN ; Zhikun LU ; Duan LI ; Zongcai LIU ; Huiying SHENG ; Xi YIN ; Xiaodan CHEN ; Guochang LIU ; Jing CHENG ; Li LIU
JOURNAL OF RARE DISEASES 2024;3(3):310-317
Objective To investigate the clinical manifestations,molecular genetics and gonadal pathol-ogy characteristics of patients with disorders of sex development(DSD),and to summarize the clinical experi-ence of identifying rare diseases from common symptoms.Methods The clinical data of 416 patients with DSD diagnosed and treated in the multidisciplinary center of DSD of Guangzhou Women and Children's Medical Cen-ter from May 2018 to August 2023 were retrospectively analyzed,summarized and discussed.Results Accord-ing to chromosome karyotype,416 cases of DSD were classified into three types:92 cases(22.1%)of abnormal sex chromosome karyotype,285 cases(68.5%)of 46,XY karyotype and 39 cases(9.4%)of 46,XX karyotype.Among the 92 patients with abnormal sex chromosome karyotype,59 cases were raised as males,18 cases(30.5%)complained of short penis with hypospadias and cryptorchidism.The most common karyotype was 45,X/46,XY(58 cases,63.0%).Among the 285 patients with 46,XY karyotype,238 cases were raised as males,and 63 cases(26.5%)complained of short penis and hypospadias;47 cases were raised as females,and 13 ca-ses(27.7%)complained of inguinal mass.A total of 216 patients with 46,XY karyotype were subjected to whole exome gene detection,and 155 cases(71.8%)were found to have molecular pathogenesis with the clinical phe-notype.Among the 39 patients with 46,XX karyotype,19 cases were raised as males,and 8 cases(42.1%)com-plained of short penis and hypospadias.In the 18 cases of gonad biopsy,17 cases showed testicular tissue in go-nads.Whole exome sequencing was performed in 14 cases.NR5A1 gene heterozygous mutation,SRY gene muta-tion and SOX3 gene mutation were found in 2 cases,respectively(14.3%).Twenty cases were raised as females,and 14 cases(70.0%)complained of clitoral hypertrophy.Gonad biopsy was performed in 8 cases,with 7 cases of ovotestis(87.5%)and 1 case of NR5A1 gene heterozygous mutation(14.3%).Conclusions The etiologies of DSD are complex and diverse,and the clinical manifestations are various,which can be manifested as hypospa-dias,micropenis,cryptorchidism and other common symptoms of the urinary system.Different etiologies have dif-ferent treatment options.Therefore,chromosome karyotype,molecular genetic testing and gonadal pathology can be used to clarify the cause of disease,especially for rare diseases,improve the detection rate,reduce the rate of missed diagnosis,and ensure reasonable treatment,especially sex selection.
7.Clinicopathological characteristics of diffuse midline gliomas with H3K27-altered:an analysis of 102 children cases
Jinhua LI ; Lifeng WANG ; Jia WANG
Chinese Journal of Clinical and Experimental Pathology 2024;40(7):696-700
Purpose To investigate the clinicopathological characteristics of pediatric diffuse midline gliomas(DMG)with H3K27-altered.Methods Clinical data of 102 patients with diffuse midline glioma with H3K27-altered were collected,HE and immunohistochemistry EnVision two-step staining was used,and Sanger sequencing for molecular detection was used to ana-lyze their clinical and pathological characteristics and reviewed relevant literatures.Results The age of patients ranged from 1 to 14 years(median age,7 years).Brainstem was the predilec-tion site(81.4%)while other sites included basal ganglia,pin-eal,etc(18.6%).The main clinical manifestations were dizzi-ness,headache,gait instability,etc.The lesions mainly showed hypointensity or isointensity on T1 and hyperintensity on T2 in MRI.Histology showed high-grade gliomas in 64 cases(62.7%)and low-grade gliomas in 38 cases(37.3%).Immunohisto-chemistry showed downexpression or loss of H3K27me3 in 81 ca-ses(100%),H3K27M expression in 100 cases(98%)cases,and EZHIP expression in 2 cases(2%).BRAF V600E expres-sion was detected in only 2.2%(2/89)while p53 mutation in 53.6%(52/97).Additionally,loss of ATRX expression was de-tected in 19.1%(18/94)while the expression of IDH1 was neg-ative in all cases(89/89).Molecular tests showed that of BRAF mutation was 1.7%(1/59)and EGFR mutation was 9.1%(1/11).Conclusion Children DMG with H3K27-altered tend to occur in the brainstem.The histological grade is mostly high.Immunohistochemistry staining of H3K27me3 show varying de-grees of deficiency.Molecular variants include H3K27M muta-tion or EZHIP overexpression and EGFR mutation.
8.Establishment and application of drug use evaluation standards for lauromacrogol
Tian HU ; Li JIA ; Yanhua CUI ; Rui WU ; Jinhua ZHANG
Chinese Journal of Pharmacoepidemiology 2024;33(5):500-507
Objective To establish the drug use evaluation(DUE)standard of lauromacrogol,evaluate its clinical use,and provide a reference for rational clinical application.Methods Based on the drug instructions,related relevant guidelines and literature of lauromacrogol,the DUE standard was formulated through the expert consultation method.A retrospective study was conducted to evaluate the drug utilization of lauromacrogol in hospitalized patients in 3 201 Hospital of Xi'an Jiao tong University in Shaanxi province from January 2021 to August 2022.Results A total of 143 medical records were included,48(33.57%)of which fully met the evaluation criteria.Unreasonable use was mainly manifested as inappropriate indications(65.03%)and unsuitable usage and dosage(1.40%).The unreasonable indications are mainly due to the existence of off-label medication.Conclusion The established DUE standard of lauromacrogol has a certain reference effect on the rational use of lauromacrogol in clinical practice.Irrational usages still exists in the clinical application of lauromacrogol in the hospital,and interventions should be developed to optimize its clinical application and promote the rational drug use.
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 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.

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