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.

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.

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 To establish an electrical field(EF)stimulation model for Schwann cells(SCs),and to provide a basis for exploring the mechanisms of EF stimulation in promoting proliferation,migration and epithelial-to-mesenchymal transition of SCs.Methods A YC-3 bipolar programmable electrical stimulator and an electrotaxis chamber were used to construct an EF stimulation system to stimulate SCs.In the study,SCs were divided into control group(Ctrl)receiving no EF stimulation and EF group stimulated by continuous constant-voltage EF(100 mV/mm,3 h).The effects of EF stimulation on the proliferation and migration of SCs were analyzed using CCK-8 assay,and wound healing assay+Transwell assay,separately;and its effect on SCs adhesion was observed by analyzing the expressions of E-cadherin and N-cadherin using Western Blot.Results The CCK-8 assay results suggested that the absorbance at 450 nm was significantly higher in EF group than in Ctrl group(P<0.05).The results of wound healing assay+Transwell assay revealed that EF group had higher cell migration efficiency than Ctrl group(P<0.05).Western Blot results showed decreased E-cadherin expression and increased N-cadherin expression in EF group as compared with Ctrl group(P<0.05).Conclusion The improved EF stimulation system for SCs is operable.EF stimulation can promote the proliferation and migration of SCs.The decreased E-cadherin expression and increased N-cadherin expression may be related to the occurrence of epithelial-to-mesenchymal transition in SCs after EF stimulation.

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.

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.