Objective:To investigate whether the rs612529 C/T and rs11669663 G/A in VSTM1 gene are associated with leukocyte signaling inhibitory receptor-1(SIRL-1)expression and an increased risk for systemic lupus erythematosus(SLE)in a Han Chinese cohort.Methods:A total of 200 patients with SLE and 218 healthy controls(HC)were enrolled.Relevant laboratory characteris-tics of patients with SLE were also collected.Genotyping of rs612529 C/T and rs11669663 G/A were performed by Sanger sequencing technology.SIRL-1 expression was assessed in peripheral blood neutrophils and monocytes was detected by flow cytometry.Levels of autoantibodies associated with SLE were detected by ELISA.Results:In both SLE group and HC,the C allele of rs612529 was asso-ciated with a decreased expression level of SIRL-1 on monocytes,with a gradual increased in SIRL-1 protein level from the CC over the CT to the TT genotype.C allele of rs612529 was associated with higher serum anti-dsDNA antibody titers in patients with SLE(P<0.05).In the case of rs11669663 G/A,no significant association of genotypes with SLE susceptibility was detected.Conclusion:VSTM1 rs612529 C/T may contribute to SLE disease activity and regulate SIRL-1 expression on monocytes in the Han Chinese cohort.

The feasibility for the development of road-rail medical vehicles was discussed.The gap between China's ground medical evacuation system and medical evacuation requirements was analyzed,and the limitations of the existing mobile medical units in China were introduced.The key points for developing road-rail medical vehicles were discussed.The road-rail medical vehicle would be an ideal tool for casualty treatment and rapid evacuation at war time and peace time,which could be a future development direction of the road-rail vehicle and medical train.[Chinese Medical Equipment Journal,2025,46(10):84-90]

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

The feasibility for the development of road-rail medical vehicles was discussed.The gap between China's ground medical evacuation system and medical evacuation requirements was analyzed,and the limitations of the existing mobile medical units in China were introduced.The key points for developing road-rail medical vehicles were discussed.The road-rail medical vehicle would be an ideal tool for casualty treatment and rapid evacuation at war time and peace time,which could be a future development direction of the road-rail vehicle and medical train.[Chinese Medical Equipment Journal,2025,46(10):84-90]

Objective:To investigate factors influencing frequent episodes (≥ 4 episodes within 1 year) in children with moderate-to-severe atopic dermatitis (AD) in China.Methods:A national multicenter cross-sectional study was conducted. Patients under the age of 18 years diagnosed with moderate-to-severe AD were enrolled at dermatology clinics in 18 medical institutions across 12 provinces and municipalities in China between June 12 and August 8, 2023. At the time of the visit, their guardians completed a structured questionnaire covering demographic characteristics, clinical features of AD, personal and family history, factors associated with frequent episodes of moderate-to-severe AD, compliance with treatment, and disease awareness. Statistical analyses included t tests, one-way analysis of variance, rank-sum tests, and chi-square tests, with multiple-response analysis applied for multiple-choice questions. Results:A total of 965 valid questionnaires were collected, and 965 children with moderate-to-severe AD were included. Among them, there were 531 males and 434 females, 678 (70.3%) were aged 2 - < 12 years, 837 (86.7%) were from urban areas, the age at onset was 2.47 ± 3.03 years, and the median frequency of AD episodes in the past year was 4 times. These children were divided into 2 groups based on the median episode frequency: < 4-episode group (439 cases, 45.5%) and ≥ 4-episode group (526 cases, 54.5%). Compared with the < 4-episode group, children in the ≥ 4-episode group showed younger ages at onset (2.22 ± 2.98 years vs. 2.76 ± 3.06 years, P = 0.006) and higher proportions of patients with comorbid allergic diseases in both the children themselves (82.9% [436/526] vs. 69.7% [306/439], χ2 = 23.42, P < 0.001) and their relatives (66.0% [347/526] vs. 57.4% [252/439], χ2 = 7.46, P = 0.006). Children in the ≥ 4- episode group also had higher monthly usage of moisturizers (150 [30, 300] g vs. 60 [6, 200] g) and daily frequency of moisturizer use, greater disease awareness, but more severe fear of medication use (all P < 0.05). The region and the human development index level were both significantly associated with the episode frequency (both P < 0.001), with the highest proportion of children from South China in the ≥ 4- episode group (36.3%, 191/526). Children in the ≥ 4-episode group also had a longer duration of topical glucocorticoid use than those in the < 4-episode group ( Z = -2.21, P = 0.027). External triggers associated with AD episodes mainly included heat exposure (50.36%, 486/965), hot water bathing (40.73%, 393/965), seafood (23.52%, 227/965), and dust mites (33.37%, 322/965) . Conclusion:In children with moderate-to-severe AD in China, factors influencing frequent episodes may include residence in southern or economically developed regions, earlier age at onset, having a personal or family history of allergic diseases, and fear of medication use.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.