AIM:To investigate the protective effects of Dendrobium officinale polysaccharide(DOP)on high glucose-induced apoptosis in retinal capillary pericytes and its potential mechanism involving mitochondrial function.METHODS:Retinal capillary pericytes were allocated into five groups: normal control(NC), high glucose(HG), and three DOP treatment groups(low, DOP-L; medium, DOP-M; high, DOP-H). Pericyte ultrastructure was analyzed using transmission electron microscopy(TEM). Apoptotic rate was quantified via Annexin V-FITC staining. Mitochondrial transmembrane potential was assessed using the JC-1 probe. Quantitative real-time polymerase chain reaction(qRT-PCR)and Western blot were employed to measure expression levels of cytochrome C(Cyt C), B-cell lymphoma 2(Bcl-2), Bcl-2-associated X protein(Bax), Caspase-9, and Caspase-3, respectively.RESULTS:Compared to the NC group, pericytes exposed to HG exhibited significant mitochondrial damage, elevated apoptotic rate, increased mRNA and protein expression of Cyt C, Bax, Caspase-9, and Caspase-3(all P<0.01), alongside a marked reduction in mitochondrial transmembrane potential and expression of Bcl-2 mRNA and protein(all P<0.01). In contrast, DOP treatment groups(DOP-M,DOP-H)dose-dependently ameliorated mitochondrial damage, reduced apoptotic rate, downregulated Cyt C, Bax, Caspase-9, and Caspase-3 expression, enhanced mitochondrial transmembrane potential, and upregulated Bcl-2 expression relative to the HG group(all P<0.05).CONCLUSION:DOP attenuates high glucose-induced apoptosis and mitochondrial injury in retinal capillary pericytes. The underlying mechanism may involve the restoration of mitochondrial transmembrane potential.

Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

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.

ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

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:Accurately grasping the disability levels and characteristics of the elderly population in China is of significant importance for responding effectively to the care service needs of this demographic.Methods:Based on multi-wave data(2011-2023)from three nationally representative aging-related surveys CLHLS,CHARLS and CALSS,this study assessed,compared,and predicted the disability levels and characteristics of China's elderly population.Results:Direct estimates from existing survey data showed low reliability.Integrated multi-source projections indicate that the disability rate among China's population aged 65 and older will follow a phased upward trend,ranging approximately between 12.27%and 15.05%from 2025 to 2050,with the disabled elderly population growing continuously from 25.9 million to 58.64 million.Bathing,toileting,and dressing were the three activities with the highest disability rates.Discussion and Suggestions:Future efforts should focus on improving the construction of an elderly capacity assessment system and addressing the shortage of high-quality disability data.Research should emphasize the evaluation process of item-specific disability among older adults.It is essential to grasp the phased characteristics of changes in disability levels and identify critical windows of opportunity for response.Based on the item-specific disability characteristics of older adults,the focus of elderly care services,facilities,and product supply should be accurately targeted.

Objective To construct a prediction model for in-hospital mortality in the elderly(≥65 years)patients undergoing unsynchronous cardioversion in ICU and to evaluate its effectiveness.Methods A retrospective study was conducted on 276 elderly eligible patients in the ICU of the Ninth and the First Medical Centers of Chinese PLA General Hospital between June 2022 and August 2024.According to their clinical outcomes,they were divided into a non-in-hospital dead group(111 cases)and an in-hospital dead group(165 cases).Clinical data were collected,and pre-dictive factors for in-hospital mortality were screened.And then a nomogram prediction model was developed based on the obtained predictive factors,which was evaluated with ROC curve and deci-sion curve analyses.Results When compared to the non-in-hospital dead group,the in-hospital dead group had significantly higher heart rate,ratio of hemodialysis,and levels of alanine amin-otransferase,aspartate aminotransferase,lactate dehydrogenase,alkaline phosphatase,serum cre-atinine,blood glucose,lactate,low base excess,sequential organ failure assessment(SOFA)score,model for end-stage liver disease score,and larger proportions of ventricular fibrillation/flutter and structural heart disease induced by pulseless ventricular tachycardia,and had significantly lower Glasgow Coma Scale(GCS)(P＜0.05).Multivariate logistic regression analysis identified body temperature＞37℃(OR=0.426,95％CI:0.198-0.915,P=0.029),chronic obstructive pulmonary disease(OR=2.333,95％CI:1.217-4.473,P=0.011),GCS score(OR=0.622,95％CI:0.410-0.944,P=0.026),hemoglobin(OR=0.817,95％CI:0.715-0.934,P=0.003),lactate(OR=1.365,95％CI:1.174-1.587,P=0.000),heart rate＞100 bpm(OR=2.757,95％CI:1.397-5.441,P=0.003),and SOFA score(OR=1.112,95％CI:1.032-1.198,P=0.005)as pre-dictors of in-hospital mortality.ROC curve analysis showed an AUC value of above indicators combined together in the prediction was 0.797,with a sensitivity of 76.97％and a specificity of 65.77％.Calibration curve analysis demonstrated good consistency between predicted and observed outcomes.Decision curve analysis indicated favorable clinical utility of the model.Conclusion This study identifies independent risk factors for in-hospital mortality among elderly patients in the ICU who underwent asynchronous cardioversion.Based on these factors,a nomo-gram model is established,demonstrating good discrimination,calibration,and model fit,with high clinical applicability.

Objective:To construct a Logistic regression model based on the energy spectral computed tomography(CT)parameters and clinical parameters characteristics,and to evaluate its diagnostic efficacy in the benign and malignant pulmonary ground glass nodules(GGN).Methods:162 GGN patients who were admitted to our hospital from April 2022 to March 2024 were selected,they were divided into benign group and malignant group according to pathological results.The difference of CT parameters[water content in normal scan period,water content in arterial period,slope of energy spectral curve in normal scan period and k value in arterial period]and clinical data between the two groups was compared.Multivariate Logistic regression model was used to analyze the independent influencing factors of malignant GGN.Receiver operating characteristic(ROC)curve was used to evaluate the diagnostic efficacy of the model for malignant GGN.Results:Water content in normal scan period in malignant group was significantly higher than that in benign group(P＜0.05).k value in normal scan period in the malignant group was significantly higher than that in the benign group(P＜0.05).Water content in arterial period in malignant group was higher than that in benign group(P＜0.05).k value in arterial period in the malignant group was higher than in benign group(P＜0.05).There were significant differences in smoking history,family history of tumor and chronic obstructive pulmonary disease(COPD)between benign group and malignant group(P＜0.05).Multivariate Logistic regression model analysis showed that,water content in normal scan period,k value in normal scan period,k value in arterial period,water content in arterial period,smoking history and family history of tumor were independent influencing factors of malignant nodule in GGN patients(P＜0.05).Based on the results of multi-factor Logistic regression analysis,the Logistic regression prediction model was constructed:logit(P)=ln(P/l-P)=0.015× water content in arterial period+1.214× smoking history+1.506× family history of tumor+0.013× water content in normal scan period 0.553× k value in normal scan period+0.202 × k value in arterial period.ROC curve results showed that the area under the curve(AUC)of the combined prediction model was 0.852,which was significantly higher than 0.654,0.607,0.628,0.759,0.707,0.682 of water content in normal scan period,k value in normal scan period,k value in arterial period,water content in arterial period,smoking history and family history of tumor.Conclusion:The Logistic regression model constructed based on the characteristics of energy spectral CT parameters and clinical parameters characteristics has good diagnostic efficacy for benign and malignant GGN.