Objective: To understand the current status of short video addiction among vocational nursing students in higher vocational colleges (hereinafter referred to as "nursing students") and its related factors, so as to provide a reference for formulating online education programs in colleges. Methods: From March to May 2025, a stratified random sample of 2 223 nursing students from four vocational colleges in Henan Province was selected. Short Video Addiction Scale for College Students, Short form Egna Minnen av Barndoms Uppfostran for Chinese, Peer Rejection Scale, and University of California at Los Angels Loneliness Scale were used for investigation. Chi square test and multivariate Logistic regression analysis were used to explore the related factors of short video addiction among nursing students. Results: The detection rate of short video addiction of higher vocational nursing students was 26.95%, and the scores for avoidance, loss of control, inefficiency and withdrawal were (8.05±2.97) (10.24±3.09) (4.99±1.88) and (11.97±4.10), respectively. Multivariate Logistic regression analysis showed that sophomore year 2 ( OR=1.83, 95%CI =1.39-2.40), higher maternal education level (secondary school/vocational college: OR =1.34, 95% CI =1.06-1.68; college/undergraduate: OR =1.38, 95% CI =1.05-1.82), paternal overprotection ( OR=1.59, 95%CI =1.27-2.00), high peer rejection ( OR=1.40, 95%CI =1.19-1.66), and strong loneliness ( OR=1.57, 95%CI =1.07-2.28) were associated with a higher risk of short video addiction among nursing students (all P <0.05). Paternal affectionate and warm rearing style ( OR=0.82, 95%CI = 0.71- 0.95) was associated with a lower risk of short video addiction ( P <0.05). Conclusions The detection rate of short video addiction among nursing students is relatively high. Short video addiction is related to the nursing students grade, maternal education level, paternal overprotection and affectionate rearing style, peer rejection, and loneliness.

ObjectiveTo investigate the metabolic alterations of serum short chain fatty acids （SCFAs） in pediatric patients with atopic dermatitis （AD） and their correlation with different clinical phenotypes using targeted metabolomics. MethodsThis study enrolled 87 AD patients and 67 healthy controls （HC）. Serum levels of eight SCFAs were quantified by ultra-high-performance liquid chromatography-mass spectrometry. The associations between SCFAs and AD were assessed using various statistical methods. ResultsCompared with the HC group， levels of acetic acid （AA）， propionic acid （PA）， and caproic acid （CA） （P=0.002，P=0.002，P=0.043） decreased in the AD group. Logistic regression analysis identified AA （OR=0.449， 95% CI： 0.289–0.698） and PA （OR = 0.487， 95% CI： 0.324–0.732） as protective factors against AD. The combination of AA and PA yielded an area under the curve （AUC） greater than 0.7， indicating good diagnostic efficacy. Age-stratified analysis revealed that AA reduction was predominant in childhood， whereas PA reduction was predominant in adolescence. Pathway enrichment analysis showed significant enrichment of fatty acid biosynthesis （FDR=0.341， P=0.003） and vitamin K metabolism （FDR=1， P=0.039） pathways. Furthermore， subgroup analyses based on disease severity， personal/family history of atopy， and sex revealed no significant differences in SCFAs levels among the groups. ConclusionDifferential serum SCFAs and their enriched metabolic pathways may be implicated in the pathogenesis of AD.

AIM:To investigate the mechanism of action of Tamarix chinensis Lour.on streptozotocin-induced type 2 diabetes mellitus(T2DM)through network pharmacology,molecular docking and ex-perimental validation.METHODS:Using the TCSMP database and Swiss Target Prediction tools screen the active components and predict potential tar-gets in Tamarix chinensis Lour..Retrieving potential disease targets associated with T2DM from data-bases such as GeneCards,OMIM,and DisGeNET.The intersection targets of Tamarix chinensis Lour.and T2DM disease was obtained through Venny platform.The STRING database was used to con-structed PPI network,and Cytoscape 3.8.0 soft-ware was use to visualized.GO function enrich-ment and KEGG pathway enrichment analysis were performed through the Metascape database.Dock-ing of important target proteins and compounds was carried out by AutoDock software.SPF grade male rats were randomly divided into normal group,model group,MET group(88.5 mg/kg),TE high-dose(800 mg/kg)group,TE medium-dose(400 mg/kg)group and TE low-dose(200 mg/kg)group(n=10).High-fat and high sugar feed com-bined with low dose STZ(45 mg/kg)was used to in-duce T2DM rat model,and the rats were adminis-tered orally for 5 weeks.Fasting blood glucose(FBG),insulin(FINS)level and HOMA-IR index,bio-chemical indicators[superoxide dismutase(SOD),malondialdehyde(MDA),glycosylated hemoglobin A1c(HbA1c)and inflammatory factor[interleukin-1β(IL-1β),tumor necrosis factor α(TNF-α),vascu-lar cell adhesion molecular(VCAM-1)levels of the rats were also observed;morphological changes of renal tissue was observe by HE staining.RESULTS:Based on the screening conditions of oral bioavail-ability(OB)≥ 30％and drug like properties(DL)≥0.18,a total of 19 main active ingredients with po-tential therapeutic effects on T2DM were screened from Tamarix chinensis Lour.,including ergosta-5,24(28)-dien-3,7,16-triol,quercetin-3,3'-dimethyl ether,kaempferol,quercetin,and others.By analyz-ing the potential targets of Tamarix chinensis Lour.for treating T2DM,a total of 185 potential target genes were screened,including SRC,EGFR,HSP90AA1,AKT1,ESR1,H1F1A,TNF,PIK3R1,etc,in-volving cancer signaling pathways,insulin resis-tance,MAPK signaling pathways,PI3K Akt signaling pathways,etc.Molecular docking results showed that the binding energies were all less than-5.0 kcal/mol,indicating that a strong binding abili-ty between the active ingredients screened by Tam-arix chinensis Lour.and the potential targets for the treatment of T2DM.The animal experiment re-sults showed that compared with the model group,the weight loss of rats in the MET and TE groups was slowed down,and the levels of FBG,FINS,MDA,HbA1c,IL-1β,TNF-α,VCAM-1,HOMA-IR in-dex were reduced,the SOD level was increased,and the differences were statistically significant(P＜0.05,P＜0.01),Renal tissue cellular morphology also showed notable improvement.Most importantly,all these results demonstrating dose-dependent ef-fects.CONCLUSION:Tamarix chinensis Lour.dis-plays a significant therapeutic effect on T2DM through multi-component,multi-target,and multi-pathway synergistic actions to improve blood glu-cose levels.The findings of this study provide a the-oretical basis for the clinical application of Tamarix chinensis Lour.in the treatment of T2DM.

Objective To explore the relationship between disease risk perception and objective risk in stroke risk population,and to provide theoretical basis for prevention and control of stroke.Methods From February to May 2023,560 residents who participated in medical examinations in 2 community health service centers of Zhengzhou and Luohe were selected as respondents.The objective risk of stroke was assessed,and the general data questionnaire and Risk Perception Questionnaire for People at High Risk of Stroke were used for investigation.Logistic regression model and restricted cubic spline model were used to analyze the correlation and the dose-response relationship between disease risk perception and objective stroke risk.Results A total of 542 effective questionnaires were collected.The detection rate of stroke high-risk groups was 48.71％.Logistic regression analysis showed that after adjusting confounding factors,the objective risk of stroke in the general population and in different gender stratification decreased first and then increased with the increase of the level of disease risk perception.The results of the restricted cubic spline showed that the disease risk perception and objective risk of stroke in the total population and gender stratification showed a nonlinear dose-response relationship(P nonlinear＜0.05),and the curve showed a"U"type change.The disease risk perception score of 90.0～110.0 was the protective factor of objective risk of stroke.Conclusion The objective risk of stroke decreases first and then increases with the increase of the perceived risk.Medical staff should maintain individual disease risk perception within the appropriate level,and pay attention to the difference in disease risk perception of different gender groups to implement targeted risk communication strategies,so as to promote the development of healthy behaviors and reduce the incidence of stroke.

Objective To review the application scope of risk communication based on eHealth technology in the primary prevention of cardiovascular diseases,so as to provide references for future research and application.Methods A systematic search was conducted in PubMed,Cochrane Library,Embase,Web of Science,CINAHL,PsycINFO,CNKI,Wanfang database,and SinoMed.The search time limit was from the establishment of databases to March 1,2024.The included literature was summarized and analyzed.Results A total of 24 articles were included.The forms of eHealth technology applied in risk communication for primary prevention of cardiovascular include online websites,computerized decision support systems,electronic health records,mobile applications,email,telephone,and text messages.The functions include risk assessment,risk notification,personalized advice,risk tracking and reminders.The outcome indicators include risk perception,physiological indicators,lifestyle and behavior,psychological indicators,feasibility evaluation,decision correlation,doctor-patient communication,intention,risk score,and physician drug prescription.Conclusion The use of eHealth technology in risk communication of cardiovascular diseases has potential value in improving patients'risk perception and promoting healthy behaviors.It is necessary to continuously improve the functions of eHealth technology and enhance its precision and intelligence,so as to better meet the needs of medical staff and patients and promote the efficient implementation of primary prevention of cardiovascular diseases.

Objective To explore the potential categories of dyadic psychological capital and related factors among stroke patients and their spouses,in order to inform the improvement of their mental health.Methods Stroke patients and their spouses were recruited in the Neurology Department of 3 tertiary general hospitals from March,2023 to August,2023 by convenience sampling method.The general information questionnaire,Modified Rankin Scale(mRs),Psychological Capital Questionnaire,The Kessler Psychological Distress Scale(K10)and Perceived Social Support Scale(PSSS)were used for investigation.Latent profile analysis was adopted to explore the categories of dyadic psychological capital,and multiple Logistic regression analysis was used to assess the effect of various factors on different categories.Results A total of 232 returned questionnaires(out of the 235)were valid,resulting an effective response rate of 98.7％.Stroke patients and their spouses were divided into 3 categories,including dyadic low psychological capital-low self-efficacy group(6.0％),dyadic high psychological capital-low resilience group(15.9％)and dyadic medium psychological capital group(78.1％).The stroke patients'education level,mRs scores,occupational status after the illness,level of perceived social support,and spouses'working condition were the influencing factors on potential profiles of dyadic psychological capital(P＜0.05).Conclusion There are heterogeneity and interdependence in the level of dyadic psychological capital of stroke patients and their spouses.Healthcare professionals should promote dyadic mental health by developing targeted interventions for stroke patients and their spouses with different psychological capital characteristics from a dyadic holistic perspective.

Aim Based on coronary CT-fractional flow reserve(CT-FFR)combined with machine learning methods,a nomogram prediction model for coronary in-stent restenosis(ISR)was developed to assess the risk of ISR.Methods Retrospective analysis was performed on patients who underwent re-examination after PCI at our hospital from January 2022 to January 2025.According to the exclusion criteria,a total of 210 patients were enrolled,including 100 cases of ISR and 110 cases of non-ISR.The dataset was randomly divided into training and test sets at a 7∶3 ratio.Af-ter univariate analysis to screen potential predictors,LASSO regression was applied to identify feature variables with non-ze-ro coefficients.Subsequently,three machine learning(ML)algorithms including random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB)were used to rank the importance of the significant factors.The intersection of the top 10 variables from each algorithm was used as input for bidirectional stepwise multivariate Logistic re-gression.An ISR risk score was then constructed and visualized using a nomogram.Results A total of 14 predictive factors were identified through LASSO regression,including diastolic blood pressure,C-reactive protein,triglycerides(TG),N-terminal pro-brain natriuretic peptide(NT-proBNP),low density lipoprotein cholesterol(LDLC),minimum stent diameter＜3 mm,systolic blood pressure,△CT-FFR,CT-FFR,interleukin-6(IL-6),body mass index,glycosylated hemoglobin(HbA1c),history of hypertension,and high density lipoprotein cholesterol(HDLC).Following stepwise screening using three ML algorithms and Logistic regression,six independent risk factors for ISR were identified:elevated△CT-FFR,IL-6,NT-proBNP,TG and CT-FFR values,and minimum stent diameter＜3 mm.The area under the curve for the training set and test set were 0.995(95％CI:0.989～1.000)and 0.965(95％CI:0.927～1.000),respectively.Decision curve analysis demonstrated high net benefit across threshold probabilities of 0～1.00 in the training set and 0～0.92 in the test set.The nomogram integrating these six predictors exhibited high accuracy and clinical utility.Con-clusion The ISR nomogram prediction model based on LASSO-ML combined with CT-FFR technology has high accuracy and clinical utility for ISR.

Objective To explore the potential categories of dyadic psychological capital and related factors among stroke patients and their spouses,in order to inform the improvement of their mental health.Methods Stroke patients and their spouses were recruited in the Neurology Department of 3 tertiary general hospitals from March,2023 to August,2023 by convenience sampling method.The general information questionnaire,Modified Rankin Scale(mRs),Psychological Capital Questionnaire,The Kessler Psychological Distress Scale(K10)and Perceived Social Support Scale(PSSS)were used for investigation.Latent profile analysis was adopted to explore the categories of dyadic psychological capital,and multiple Logistic regression analysis was used to assess the effect of various factors on different categories.Results A total of 232 returned questionnaires(out of the 235)were valid,resulting an effective response rate of 98.7％.Stroke patients and their spouses were divided into 3 categories,including dyadic low psychological capital-low self-efficacy group(6.0％),dyadic high psychological capital-low resilience group(15.9％)and dyadic medium psychological capital group(78.1％).The stroke patients'education level,mRs scores,occupational status after the illness,level of perceived social support,and spouses'working condition were the influencing factors on potential profiles of dyadic psychological capital(P＜0.05).Conclusion There are heterogeneity and interdependence in the level of dyadic psychological capital of stroke patients and their spouses.Healthcare professionals should promote dyadic mental health by developing targeted interventions for stroke patients and their spouses with different psychological capital characteristics from a dyadic holistic perspective.

Aim Based on coronary CT-fractional flow reserve(CT-FFR)combined with machine learning methods,a nomogram prediction model for coronary in-stent restenosis(ISR)was developed to assess the risk of ISR.Methods Retrospective analysis was performed on patients who underwent re-examination after PCI at our hospital from January 2022 to January 2025.According to the exclusion criteria,a total of 210 patients were enrolled,including 100 cases of ISR and 110 cases of non-ISR.The dataset was randomly divided into training and test sets at a 7∶3 ratio.Af-ter univariate analysis to screen potential predictors,LASSO regression was applied to identify feature variables with non-ze-ro coefficients.Subsequently,three machine learning(ML)algorithms including random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB)were used to rank the importance of the significant factors.The intersection of the top 10 variables from each algorithm was used as input for bidirectional stepwise multivariate Logistic re-gression.An ISR risk score was then constructed and visualized using a nomogram.Results A total of 14 predictive factors were identified through LASSO regression,including diastolic blood pressure,C-reactive protein,triglycerides(TG),N-terminal pro-brain natriuretic peptide(NT-proBNP),low density lipoprotein cholesterol(LDLC),minimum stent diameter＜3 mm,systolic blood pressure,△CT-FFR,CT-FFR,interleukin-6(IL-6),body mass index,glycosylated hemoglobin(HbA1c),history of hypertension,and high density lipoprotein cholesterol(HDLC).Following stepwise screening using three ML algorithms and Logistic regression,six independent risk factors for ISR were identified:elevated△CT-FFR,IL-6,NT-proBNP,TG and CT-FFR values,and minimum stent diameter＜3 mm.The area under the curve for the training set and test set were 0.995(95％CI:0.989～1.000)and 0.965(95％CI:0.927～1.000),respectively.Decision curve analysis demonstrated high net benefit across threshold probabilities of 0～1.00 in the training set and 0～0.92 in the test set.The nomogram integrating these six predictors exhibited high accuracy and clinical utility.Con-clusion The ISR nomogram prediction model based on LASSO-ML combined with CT-FFR technology has high accuracy and clinical utility for ISR.

Home-based self-management can effectively improve functional status and slow disease progression in patients with stroke. Modern information technology has brought new opportunities for enhancing stroke self-management at home. This review summarizes the current application of modern information technologies-including artificial intelligence, mobile devices, virtual reality, and the Internet of Things-in home-based self-management for stroke patients. Based on this, the review further discusses the challenges and opportunities associated with applying these technologies, aiming to provide a reference for promoting their broader implementation in stroke patient self-management.