Objective:To explore the relationship between depressive symptoms and stressors among post-graduates and the role of coping styles and psychological suzhi between them.Methods:A total of 775 postgradu-ates were recruited.They were assessed with the Center for Epidemiological Studies Depression Scale(CES-D),Psychological Stressors Scale of Postgraduates(PSSP),Simplified Coping Style Questionnaire(SCSQ),and Col-lege Students Psychological Suzhi Brief Mental Health Version(CSPS-B-MH).Results:The detection rate of de-pression among postgraduates was 52.0％.Postgraduate stressors were positively associated with depression(β=0.47).Coping styles played a partial mediating role in the relationship between stressors and depression in postgrad-uates,with the mediation effect accounting for 24.76％of the total effect.Psychological suzhi was shown to moder-ate the effect of stressors on depression(β=-0.07)and positive coping on depression(β=-0.05).Conclusion:Postgraduate stressors could affect depression through the mediating role of coping styles and the moderating role of psychological suzhi.

With the rapid development of population aging in various countries around the world,the health and elderly care industry has been paid high attention.The standardization of smart health and elderly care technology and services is particularly important.This paper firstly reviewed the policies related to healthy elderly care in China.By analyzing the industrial standards and provincial standards issued,this paper focused on the policies proposed by the Shanghai Municipal Government for the standardization of smart health and elderly care,as well as the researches on the standard system and the construction of standard families.Shanghai group standards in the field of smart health and elderly care were summarized,including the guidelines for the construction of standard systems,elderly care service platforms,community elderly cafeterias,portable health monitoring terminals,indoor sports services,and home-based elderly care safety monitoring.A series of case analyses of the standardized implementation of the above aspects were also provided.Through standardization research and practice in recent years,it has been fully demonstrated that the standard research plays an important leading role in the field of smart health and elderly care.

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

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.

Objective:To explore the relationship between depressive symptoms and stressors among post-graduates and the role of coping styles and psychological suzhi between them.Methods:A total of 775 postgradu-ates were recruited.They were assessed with the Center for Epidemiological Studies Depression Scale(CES-D),Psychological Stressors Scale of Postgraduates(PSSP),Simplified Coping Style Questionnaire(SCSQ),and Col-lege Students Psychological Suzhi Brief Mental Health Version(CSPS-B-MH).Results:The detection rate of de-pression among postgraduates was 52.0％.Postgraduate stressors were positively associated with depression(β=0.47).Coping styles played a partial mediating role in the relationship between stressors and depression in postgrad-uates,with the mediation effect accounting for 24.76％of the total effect.Psychological suzhi was shown to moder-ate the effect of stressors on depression(β=-0.07)and positive coping on depression(β=-0.05).Conclusion:Postgraduate stressors could affect depression through the mediating role of coping styles and the moderating role of psychological suzhi.

Creutzfeldt-Jakob disease(CJD)is a rare neurodegenerative disorder characterized by abnor-malities in the prion protein(PrP),the most common form of human prion disease.Although Genome-Wide Association Studies(GWAS)have identified numerous risk genes for CJD,the mechanisms under-lying these risk loci remain poorly understood.This study aims to elucidate novel genetically prioritized candidate proteins associated with CJD in the human brain through an integrative analytical pipeline.Uti-lizing datasets from Protein Quantitative Trait Loci(pQTL)(NpQTL1=152,NpQTL2=376),expres-sion QTL(eQTL)(N=452),and the CJD GWAS(NCJD=4 110,NControls=13 569),we imple-mented a systematic analytical pipeline.This pipeline included Proteome-Wide Association Study(PWAS),Mendelian randomization(MR),Bayesian colocalization,and Transcriptome-Wide Associa-tion Study(TWAS)to identify novel genetically prioritized candidate proteins implicated in CJD patho-genesis within the brain.Through PWAS,we identified that the altered abundance of six brain proteins was significantly associated with CJD.Two genes,STX6 and PDIA4,were established as lead causal genes for CJD,supported by robust evidence(False Discovery Rate＜0.05 in MR analysis;PP4/(PP3+PP4)≥0.75 in Bayesian colocalization).Specifically,elevated levels of STX6 and PDIA4 were asso-ciated with an increased risk of CJD.Additionally,TWAS demonstrated that STX6 and PDIA4 were asso-ciated with CJD at the transcriptional level.

Objective To explore the clinical characteristics, treatment methods, and prognostic risk factors of cesarean scar endometriosis (CSE), and to provide evidence for standardized management. Methods A retrospective analysis was conducted on the clinical data of patients with postoperatively pathologically confirmed CSE who underwent primary surgical treatment at the Obstetrics and Gynecology Hospital of Fudan University from January 1, 2015, to June 30, 2024. According to the deepest tissue involved as determined during intraoperative exploration, patients were classified into three types: typeⅠ(fascia type), typeⅡ(anterior sheath muscle type), and type Ⅲ (peritoneal type). Differences in general patient characteristics among the subtypes were compared, and recurrence was followed up. Results A total of 321 patients were included, with age of (31.82±3.82) years. 95.6% patients had Pfannenstiel incision. The latent period was (34.45±26.43) months, patients younger than 35 years (P＝0.005), with lactation duration less than 6 months (P＜0.001), and with maximum lesion diameter less than 4 cm (P＝0.011) had shorter latent periods. Typical symptoms of cyclical pain with mass enlargement at the scar site were present in 84.4% of patients. Preoperative ultrasound and magnetic resonance imaging (MRI) both had a positive predictive value of 100% for CSE, but MRI was superior to ultrasound in accuracy rate of assessing lesion infiltration depth (96.9% vs 50.5%) and measuring lesion size. Patients with type Ⅲ lesions had the highest proportion with maximum lesion diameter ＞3 cm (52.8%) and requiring mesh repair (25.0%). The follow-up period was (21.3±12.6) months, and the recurrence rate was 1.82%. Younger age (P＝0.030) and multiple lesions (P＝0.048) were influencing factors for postoperative recurrence. Conclusions The latent period of CSE is correlated with age, lactation duration and lesion size. Younger patients and those with multiple lesions have a higher risk of recurrence.

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.

Creutzfeldt-Jakob disease(CJD)is a rare neurodegenerative disorder characterized by abnor-malities in the prion protein(PrP),the most common form of human prion disease.Although Genome-Wide Association Studies(GWAS)have identified numerous risk genes for CJD,the mechanisms under-lying these risk loci remain poorly understood.This study aims to elucidate novel genetically prioritized candidate proteins associated with CJD in the human brain through an integrative analytical pipeline.Uti-lizing datasets from Protein Quantitative Trait Loci(pQTL)(NpQTL1=152,NpQTL2=376),expres-sion QTL(eQTL)(N=452),and the CJD GWAS(NCJD=4 110,NControls=13 569),we imple-mented a systematic analytical pipeline.This pipeline included Proteome-Wide Association Study(PWAS),Mendelian randomization(MR),Bayesian colocalization,and Transcriptome-Wide Associa-tion Study(TWAS)to identify novel genetically prioritized candidate proteins implicated in CJD patho-genesis within the brain.Through PWAS,we identified that the altered abundance of six brain proteins was significantly associated with CJD.Two genes,STX6 and PDIA4,were established as lead causal genes for CJD,supported by robust evidence(False Discovery Rate＜0.05 in MR analysis;PP4/(PP3+PP4)≥0.75 in Bayesian colocalization).Specifically,elevated levels of STX6 and PDIA4 were asso-ciated with an increased risk of CJD.Additionally,TWAS demonstrated that STX6 and PDIA4 were asso-ciated with CJD at the transcriptional level.