Objective: To systematically evaluate the therapeutic efficacy and maintenance effects of digital psychological therapies on depressive symptoms among adolescents, so as to provide a reference for clinical practice. Methods: Randomized controlled trial(RCT) investigating digital psychological therapies to improve depressive symptoms among adolescents were searched across databases, including PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang database, VIP database, and SinoMed, from database inception to November 20, 2025. Following literature screening, quality assessment, and data extraction, a Meta analysis was performed using Stata 18.0 software. Results: A total of 20 studies involving 2 042 adolescents aged 11-19 were included. The Meta analysis revealed that digital psychological therapies significantly alleviated depressive symptoms in adolescents ( SMD =-0.59, 95% CI =-0.85 to -0.32, P <0.01). The therapeutic effect was sustained at long term follow up ( SMD =-0.21, 95% CI =-0.34 to -0.09, P <0.01). Furthermore, depression scores in the intervention group showed a continued decrease from post intervention to long term follow up ( SMD =-0.28, 95% CI =-0.41 to -0.14, P <0.01). Egger s linear regression test indicated possible publication bias (Kendall s tall=0.28, P <0.01). Conclusions Digital psychological therapies can effectively improve depressive symptoms among adolescents, with stable long term efficacy. However, current evidence remains limited and exhibits substantial heterogeneity. Therefore, further large sample, high quality RCTs are warranted to validate the effectiveness of this intervention.

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.

With the rapid development of social economy and the continuous improvement of human living standard, the incidence, fatality and recurrence rates of cardiovascular disease (CVD) are increasing year by year, which seriously affects people's life and health. Conventional therapeutic drugs have limited improvement on the disability rate, so the search for new therapeutic drugs and action targets has become one of the hotspots of current research. In recent years, the therapeutic role of the natural compound rosmarinic acid (RA) in CVD has attracted much attention, which is capable of preventing CVD by modulating multiple signalling pathways and exerting physiological activities such as antioxidant, anti-apoptotic, anti-inflammatory, anti-platelet aggregation, as well as anti-coagulation and endothelial function protection. In this paper, the role of RA in the prevention of CVD is systematically sorted out, and its mechanism of action is summarised and analysed, with a view to providing a scientific basis and important support for the in-depth exploration of the prevention value of RA in CVD and its further development as a prevention drug.

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.

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.

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.

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.

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.

Hospitals presently confront multiple challenges, including rapid technological advancements, escalating healthcare demands, and continuously rising healthcare costs, thereby necessitating the introduction of new quality productive forces to drive high-quality hospital development. Adopting the perspective of new quality productive forces, the authors examined the transformation trend from conventional fragmented medical services towards integrated digital healthcare systems. Through the establishment of digital infrastructure characterized by big data, cloud computing, the Internet of Things, and artificial intelligence, hospitals could realize deep integration between inpatient and outpatient care, as well as seamless online and offline service delivery. Concurrently, oriented by the healthcare needs of residents, a precise and efficient " prevention-diagnosis-treatment-rehabilitation" service mode was proposed. This mode could facilitate the extension of innovation and industrial chains originating from healthcare services, expedite the translation of research outcomes into practice, and promote scientifically meticulous hospital management. Collectively, these measures could enable hospitals to develop patient-centered, comprehensive life-cycle healthcare service systems, thereby promoting the high-quality development of hospitals.

The epidemiological and spatiotemporal clustering characteristics of dengue fever in Fujian Province were ana-lyzed,to provide a scientific basis for dengue fever prevention and control.Descriptive epidemiology,spatial autocorrelation a-nalysis,and spatiotemporal scanning were used to analyze dengue fever cases in Fujian Province from 2011 to 2023.In this peri-od,a total of 3 586 cases of dengue fever were reported in Fujian Province,including 2 360 local cases,1 134 imported cases from abroad,and 92 imported cases from China.Cases were reported in ten prefectures and cities of the province,and 81 out of 88 counties reported cases.Imported cases were reported throughout the year in Fujian Province,but the occurrence of local ca-ses showed clear seasonality.Local cases and domestic imports were concentrated in August to October,whereas overseas im-ports occurred primarily from June to October.The imported cases were mainly from Southeast Asian countries,but a trend of spreading from Southeast Asian countries to South Asia,Africa,the Americas,and other regions,was observed.Spatio-tem-poral clustering of dengue fever was found in Fujian Province(Moran's I value 0.14-0.66,P＜0.05),and the high-high ag-gregation areas were distributed primarily in Fuzhou,Quanzhou,and Putian.Spatio-temporal scanning detected three aggrega-tion areas:one main and two secondary.The aggregation time was from the end of July to October,and the distribution was primarily in Fuzhou,Quanzhou,Putian,Zhangzhou,and Xiamen.The distribution of dengue fever in Fujian Province showed clear spatial and temporal clustering from the end of July to October,and the distribution was primarily in Fuzhou,Quanzhou,Putian,Zhangzhou,and Xiamen.For high concentration areas,national health campaigns,mosquito prevention and control,epidemic surveillance,medical personnel training,and other relevant measures could be carried out in advance before local cases appear every year.Reduce local transmission of dengue fever due to importation.