ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

The research and development of innovative drug have progressed remarkably, but the long development circle and high failure rate have become the bottleneck. Drug repurposing, discovering new indications of approved drugs, is a strategy to overcome these obstacles. By exploring new indications for approved drugs, rapid progress has been made in basic research and clinical translation in recent years. Rich resources of drugs, proven security, efficient development workflow and reduced cost are core advantages of this strategy, making the strategy a crucial direction of optimizing the pipeline of drug research and development. This review systematically summarizes drug repurposing cases that have received clinical approval over the past five years, and proposes core strategies for drug repurposing, including approaches based on targets, pathways, drug similarity, post-treatment phenotypes, and clinical side effects, aiming to provide some strategic guidance for drug repurposing efforts.

Objective To explore the impact of wearable devices in assisting disease monitoring on heart-focused anxiety in patients with tachyarrhythmias. Methods A cross-sectional study design was conducted. 305 patients with tachyarrhythmias who attended the department of cardiology of Zhongshan Hospital, Fudan University in Shanghai from July 2025 to December 2025 were enrolled as the research subjects, and clinical data of patients were collected. Univariate analysis and multiple stepwise regression analysis were used to analyze the impact of wearable devices in assisting disease monitoring on patients’ heart-focused anxiety. Results The mean score of heart-focused anxiety in the 305 patients with tachyarrhythmias was 33.16±9.70. Among them, 104(34.10%) patients used wearable devices in assisting disease monitoring. The results of univariate analysis and multiple stepwise regression showed that the use of wearable devices in assisting disease monitoring was an influencing factor for heart-focused anxiety (P＜0.05), and the patients who used wearable devices in assisting disease monitoring had a significantly lower level of heart-focused anxiety (P＜0.05). Conclusions The use of wearable devices in assisting disease monitoring is associated with a lower level of heart-focused anxiety in patients with tachyarrhythmias. Medical staff can guide patients to rationally use wearable devices in assisting disease monitoring on the basis of evaluating their needs and device operation capabilities.

OBJECTIVES: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes. METHODS: Using the open-access critical care database MIMIC-IV (Medical Information Mart for Intensive Care IV), we identified intensive care unit (ICU) patients diagnosed with AMI complicated by VA. Patients were grouped according to in-hospital survival. The predictive performance of the Charlson comorbidity index and other clinical variables for in-hospital mortality was analyzed. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable Logistic regression. A nomogram model was constructed based on the regression results. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots. RESULTS: A total of 1 492 patients with AMI and VA were included, of whom 340 died and 1 152 survived during hospitalization. Significant differences were observed between survivors and non-survivors in sex distribution, vital signs, comorbidity burden, organ function, and laboratory parameters (all P<0.05). The area under the curve (AUC) of the Charlson comorbidity index for predicting in-hospital mortality was 0.712 (95% CI 0.681 to 0.742), significantly higher than albumin, international normalized ratio (INR), hemoglobin, body temperature, and platelet count (all P<0.001), but comparable to Sequential Organ Failure Assessment (SOFA) score (P>0.05). LASSO regression identified seven key predictors: the Charlson comorbidity index (quartile groups: T1, <6; T2, ≥6-<7; T3, ≥7-<9; T4, ≥9), ventricular fibrillation, age, systolic blood pressure, respiratory rate, body temperature, and SOFA score. Multivariate Logistic regression showed that compared with T1, mortality risk increased significantly in T2 (OR=1.996, 95% CI 1.135 to 3.486, P=0.016), T3 (OR=3.386, 95% CI 2.192 to 5.302, P<0.001), and T4 (OR=5.679, 95% CI 3.711 to 8.842, P<0.001). Age (OR=1.056, P<0.001), respiratory rate (OR=1.069, P<0.001), SOFA score (OR=1.223, P<0.001), and ventricular fibrillation (OR=2.174, P<0.001) were independent risk factors, while systolic blood pressure (OR=0.984, P<0.001) and body temperature (OR=0.648, P<0.001) were protective factors. The nomogram incorporating these predictors achieved an AUC of 0.849 (95% CI 0.826 to 0.871) with high discrimination and good calibration (mean absolute error=0.014). CONCLUSIONS The Charlson comorbidity index is an independent predictor of in-hospital mortality in AMI patients complicated by VA, with performance comparable to the SOFA score. The nomogram model based on the Charlson comorbidity index and additional clinical variables effectively estimates mortality risk and provides a valuable reference for clinical decision-making.
Humans ; Nomograms ; Hospital Mortality ; Myocardial Infarction/complications* ; Male ; Female ; Comorbidity ; Middle Aged ; Aged ; Arrhythmias, Cardiac/complications* ; ROC Curve ; Intensive Care Units

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Pain assessment in children is vital in clinical practice. Accurate evaluation of pain intensity in children is the prerequisite for implementing effective analgesic interventions, it is necessary to chose age-specific assessment tools tailored to developmental stages of children. The degrees of patin reported by children themselves are the gold standard for evaluation, and self-assessment should be prioritized when feasible. In addition, behavioral and physiological assessments also show good reliability and validity. This review summarizes current domestic and international researches on pediatric pain assessment tools and their clinical applications, aiming to provide reference for optimizing pain evaluation in pediatric practice.

BackgroundNon-suicidal self-injury （NSSI） behavior among adolescents has become a global public health concern. Anxiety and depression are considered key factors influencing NSSI behavior， while social support may play a protective role in alleviating emotional and behavioral issues. However， existing research has primarily focused on the direct impact of individual factors on NSSI behavior， with insufficient exploration of the combined effects of anxiety， depression and social support. ObjectiveTo investigate the direct effect of anxiety on NSSI， the mediating role of depression and the moderating role of social support in relationship between anxiety and NSSI behavior， thus to provide references for the prevention and intervention of NSSI behavior among adolescents. MethodsIn February 2022， a total of 40 820 students in grades 7 to 12 across 10 middle schools in a district of Chengdu were selected as participants， and they were assessed using Generalized Anxiety Disorder Scale-7 item （GAD-7）， Patient's Health Questionnaire Depression Scale-9 item （PHQ-9）， Social Support Scale for Urban Students （SSSUS） and Adolescent Self-Harm Scale （ASHS）. Pearson correlation analysis was conducted to examine the correlations between scale scores among adolescents with NSSI behaviors. Mediation and moderation analyses were performed using Process 3.5 in SPSS， and the significance was tested with bootstrapping. The interaction was visualized by using simple slope analysis. ResultsAmong 34 534 （84.60%） valid respondents， 542 adolescents （1.57%） reported engaging in NSSI behavior. Significant differences in gender， GAD-7 scores， PHQ-9 scores， and SSSUS scores were observed between NSSI behavior group and non-NSSI group （χ²/t=62.889， 71.120， 94.365， -41.464， P<0.01）.Adolesents with NSSI showed positive correlations between GAD-7 scores and both ASHS and PHQ-9 scores （r=0.158， 0.166， P<0.01）. PHQ-9 scores were positively correlated with ASHS scores （r=0.364， P<0.01）， but negatively correlated with SSSUS scores （r=-0.290， P<0.01）. SSSUS scores were negatively correlated with ASHS scores （r=-0.247， P<0.01）. Depression partially mediated the relationship between anxiety and NSSI behavior， with an effect size of 0.544 （95% CI： 0.162~0.944）， accounting for 35.79% of the total effect. Social support moderated the relationship between depression and NSSI bahavior， with an effect value of -0.082 （95% CI： -0.135~-0.029）. ConclusionAnxiety not only directly influences NSSI bahavior among adolescents， also indirectly exacerbates it through depression， while social support mitigates the impact of depression on NSSI behavior. ［Funded by Youth Project of National Natural Science Foundation of China （number， 82401812）； Project of Health Commission of Sichuan Province （number， 24LCYJPT18）］

Ferroptosis of chondrocytes is a significant contributor to osteoarthritis (OA), for which there is still a lack of safe and effective therapeutic drugs targeting ferroptosis. Here, we screen for anti-ferroptotic drugs in Food and Drug Administration (FDA)-approved drug library via a high-throughput manner in chondrocytes. We identified a group of FDA-approved anti-ferroptotic drugs, among which vitamin K showed the most powerful protective effect. Further study demonstrated that vitamin K effectively inhibited ferroptosis and alleviated the extracellular matrix (ECM) degradation in chondrocytes. Intra-articular injection of vitamin K inhibited ferroptosis and alleviated OA phenotype in destabilization of the medial meniscus (DMM) mouse model. Mechanistically, transcriptome sequencing and knockdown experiments revealed that the anti-ferroptotic effects of vitamin K depended on growth arrest-specific 6 (Gas6). Furthermore, exogenous expression of Gas6 was found to inhibit ferroptosis through the AXL receptor tyrosine kinase (AXL)/phosphatidylinositol 3-kinase (PI3K)/AKT serine/threonine kinase (AKT) axis. Together, we demonstrate that vitamin K inhibits ferroptosis and alleviates OA progression via enhancing Gas6 expression and its downstream pathway of AXL/PI3K/AKT axis, indicating vitamin K as well as Gas6 to serve as a potential therapeutic target for OA and other ferroptosis-related diseases.

Liraglutide (Lira), a glucagon-like peptide-1 (GLP-1) receptor agonist approved for diabetes and obesity, has shown significant potential in treating metabolic dysfunction-associated steatotic liver disease (MASLD). However, its systematic molecular regulation and mechanisms remain underexplored. In this study, a mouse model of MASLD was developed using a high-fat diet (HFD), followed by Lira administration. Proteomics and glycoproteomics were analyzed using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS), while potential molecular target analysis was conducted via quantitative real-time polymerase chain reaction (qPCR) and Western blotting. Our results revealed that Lira treatment significantly reduced liver weight and serum markers, including alanine aminotransferase (ALT) and others, with glycosylation changes playing a more significant role than overall protein expression. The glycoproteome identified 255 independent glycosylation sites, emphasizing the impact of Lira on amino acid, carbohydrate metabolism, and ferroptosis. Simultaneously, proteomic analysis highlighted its effects on lipid metabolism and fibrosis pathways. 21 signature molecules, including 7 proteins and 14 N-glycosylation sites (N-glycosites), were identified as potential targets. A Lira hydrogel formulation (Lira@fibrin (Fib) Gel) was developed to extend drug dosing intervals, offering enhanced therapeutic efficacy in managing chronic metabolic diseases. Our study demonstrated the importance of glycosylation regulation in the therapeutic effects of Lira on MASLD, identifying potential molecular targets and advancing its clinical application for MASLD treatment.

BackgroundThe distressingly high prevalence of depressive symptoms among adolescents exerts profound impacts on their physical and psychological development， urgently necessitating effective preventive interventions. Existing studies， however， have predominantly focused on isolated risk factors， neglecting to construct an integrated model that systematically disentangles the intricate relationships linking parenting styles， learning burnout， and childhood trauma to adolescent depressive symptoms. Moreover， the potential protective roles of social support and psychological resilience in this context remain insufficiently elucidated. ObjectiveTo construct a structural equation model encompassing multiple pathways to unravel the comprehensive mechanisms through which negative parenting styles， childhood trauma， learning burnout， psychological resilience， and social support collectively influence adolescent depressive symptoms， thereby providing evidence-based intervention strategies. MethodsA stratified sampling technique was utilized to recruit 5 865 students from 12 middle schools in Chengdu City， Sichuan Province from March to May 2022. Participants were assessed using the following validated instruments： the Short-form Egna Minnen av Barndoms Uppfostran （s-EMBU）， the Childhood Trauma Questionnaire-Short Form （CTQ-SF）， the Adolescent Student Burnout Inventory， the Patients' Health Questionnaire Depression Scale-9 item （PHQ-9）， the Social Support Rating Scale （SSRS），and the Connor-Davidson Resilience Scale （CD-RISC）. A partial least squares structural equation modeling （PLS-SEM） approach was employed to construct a predictive framework examining the complex network of pathways through which negative parenting styles， childhood trauma， learning burnout， psychological resilience，and social support collectively influence depressive symptoms in adolescents. ResultsThe PHQ-9 scores demonstrated significant positive correlations with the scores on s-EMBU overprotection subscale （r=0.272， P<0.01）， s-EMBU rejection subscale （r=0.368， P<0.01）， CTQ-SF （r=0.288， P<0.01） and Adolescent Student Burnout Inventory （r=0.587， P<0.01）. Conversely， significant negative correlations were observed between PHQ-9 scores and both SSRS （r=-0.532， P<0.01） and CD-RISC scores （r=-0.418， P<0.01）. Negative parenting styles （β=0.113， 95% CI： 0.087-0.138） and learning burnout （β=0.339， 95% CI： 0.315-0.364） emerged as significant positive predictors of depressive symptoms， with childhood trauma mediating the relationship between negative parenting styles and depressive symptoms （effect size=0.018， 95% CI： 0.013-0.024）. Social support servesed as a mediating pathway between negative parenting styles and depressive symptoms （β=0.080， 95% CI： 0.069-0.092）， as well as between negative parenting styles and childhood trauma （β=0.041， 95% CI： 0.032-0.050）. It also functioned as an intermediary pathway linking learning burnout to depressive symptoms （β=0.092， 95% CI： 0.081-0.104） and connecting learning burnout with childhood trauma （β=0.048， 95% CI： 0.037-0.058）. Additionally， psychological resilience serveed as a mediating pathway between negative parenting styles and depressive symptoms （β=0.004， 95% CI： 0.002-0.007）， between learning burnout and depressive symptoms （β=0.037， 95% CI： 0.023-0.052）， and between childhood trauma and depressive symptoms （β=0.003， 95% CI： 0.001-0.006）. ConclusionLearning burnout exerts a direct effect on adolescent depressive symptoms. Negative parenting styles influence depressive symptoms both directly and indirectly through childhood trauma. Furthermore， social support and psychological resilience serve as mediator linking negative parenting styles and learning burnout to depressive symptoms in adolescents. ［Funded by Science and Technology Project of the Health Commission of Sichuan Province （number， 24LCYJPT18）］