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.

BACKGROUND:In recent years,deep learning technologies have been increasingly applied in the field of oral medicine,enhancing the efficiency and accuracy of oral imaging analysis and promoting the rapid development of intelligent oral medicine. OBJECTIVE:To elaborate the current research status,advantages,and limitations of deep learning based on oral imaging in the diagnosis and treatment decision-making of oral diseases,as well as future prospects,exploring new directions for the transformation of oral medicine under the backdrop of deep learning technology. METHODS:PubMed was searched for literature related to deep learning in oral medical imaging published from January 2017 to January 2024 with the search terms"deep learning,artificial intelligence,stomatology,oral medical imaging."According to the inclusion criteria,80 papers were finally included for review. RESULTS AND CONCLUSION:(1)Classic deep learning models include artificial neural networks,convolutional neural networks,recurrent neural networks,and generative adversarial networks.Scholars have used these models in competitive or cooperative forms to achieve more efficient interpretation of oral medical images.(2)In the field of oral medicine,the diagnosis of diseases and the formulation of treatment plans largely depend on the interpretation of medical imaging data.Deep learning technology,with its strong image processing capabilities,aids in the diagnosis of diseases such as dental caries,periapical periodontitis,vertical root fractures,periodontal disease,and jaw cysts,as well as preoperative assessments for procedures such as third molar extraction and cervical lymph node dissection,helping clinicians improve the accuracy and efficiency of decision-making.(3)Although deep learning is promising as an important auxiliary tool for the diagnosis and treatment of oral diseases,it still has certain limitations in model technology,safety ethics,and legal regulation.Future research should focus on demonstrating the scalability,robustness,and clinical practicality of deep learning,and finding the best way to integrate automated deep learning decision support systems into routine clinical workflows.

In the context of the rapid development of 5G technology， the development of artificial intelligence （AI） in traditional Chinese medicine （TCM） faces new opportunities and challenges. Focusing on how to uphold tradition while innovating in the development of AI in TCM， starting from the current development status of AI in Chinese medicine， including the integration of four diagnostic methods， syndrome differentiation and treatment， auxiliary diagnosis and treatment， research and development of Chinese herbal medicine， prevention and treatment of diseases， knowledge inheritance， and other aspects， this paper discussed the support of policies and technical advancements， as well as development opportunities such as increased demand for health. Regarding machine ethics， data ethics， regulatory review， and other aspects， it also proposed some suggestions that the training algorithm should be improved to assist medical work； data ownership should be clarified to ensure data security； and an AI ethics committee should be set up to improve the review system， aiming to maximize the advantages of smart healthcare and accelerate the modernization of TCM for the benefit of patients and the service of human health.

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）］

Objective:To compare the consistency of microarray chemiluminescent protein chip and immunoblotting in the autoantibody spectrum of patients and the diagnostic efficacy of systemic lupus erythematosus(SLE), and to explore the correlation between the detection results of protein microarray and clinical indicators and lymphocyte subsets.Methods:Serum autoantibodies in 649 samples collected between December 2023 and December 2024 in Nanjing Drum Tower Hospital were analyzed using the microarray chemiluminescent protein chip method, with 401 samples simultaneously tested by immunoblotting. Kappa coefficient assessed inter-method consistency. Diagnostic performance was compared via ROC curves. Spearman correlation analysis evaluated relationships between autoantibody levels and SLEDAI-2000 scores, clinical parameters, and lymphocyte subsets.Results:The two methods demonstrated good consistency across 14 antinuclear antibodies, with optimal agreement for anti-SSA/Ro ( Kappa=0.845, P<0.001), anti-SSB ( Kappa=0.825, P<0.001), and anti-CENP B ( Kappa=0.851, P<0.001). The protein chip method significantly improved SLE diagnostic efficacy, particularly for anti-dsDNA (AUC difference=0.291, P<0.001) and anti-Sm antibodies (AUC difference=0.295, P<0.001). Combined detection of anti-SSA/Ro and anti-nRNP/Sm antibodies achieved superior diagnostic performance (AUC=0.927). Anti-dsDNA, anti-histone, and anti-nucleosome antibodies positively correlated with SLEDAI-2000 ( r=0.408, 0410, 0.384, all P<0.001), complement ( P<0.001), and 24-hour urinary protein ( r=0.374, 0.387, 0.301, all P<0.001). Immunological analysis showed that the proportion of NK cells was generally negatively correlated with antinuclear antibodies such as anti-dsDNA ( r=-0.352, P<0.001) and anti-Sm ( r=-0.328, P<0.001) antibodies. Meanwhile, the proportion of CD8 + T cells was positively correlated with anti-nRNP/Sm ( r=0.229, P=0.002) and anti-Sm antibodies ( r=0.211, P=0.005). The proportion of CD4 + T cells was negatively correlated with anti-SSA/Ro ( r=-0.239, P<0.001), while the proportion of B cells was positively correlated with anti-dSDNA antibody ( r=0.300, P<0.001). Conclusion:The protein chip method showed strong consistency with immunoblotting for detecting 14 autoantibodies but demonstrated superior SLE diagnostic efficacy. The combined use of multiple detection methods can enhance the reliability of clinical diagnosis.

AIM: To design and construct recombinant epitope nucleotides vaccine of glycoprotein B(gB)and glycoprotein D(gD)of herpes simplex virus type 1(HSV-1), and to investigate its immunoprotective effects and tissue expression in animal models.METHODS: The HSV-1 gB and gD epitope genes were selected and tandem assembled to construct the recombinant protein-coding gene X, which was transducted into the prokaryotic expression vector pET28(a). The recombinant protein was synthesized and utilized to generate monoclonal antibodies, which were subsequently used to immunize New Zealand white rabbits. The immunogenicity of the purified protein and the presence of polyclonal antibodies in the serum were tested through separating serum from cardiac blood, and the serum antibody titers were determined. The pcDNA3.1-X was successfully constructed as a eukaryotic expression vector and immunized the female BALB/c mice aged 4 to 6 wk via intramuscular injection. Serum antibodies and immune-related cytokines were quantified using enzyme-linked immunosorbent assay(ELISA). The expression of the X protein in the ocular, trigeminal ganglion, and brain tissues of the mice was assessed.RESULTS: The target polyclonal antibody was identified with a serum antibody titer of 1:3200 in the rabbit serum after immunized by recombinant protein X. Upon immunizing mice with the eukaryotic recombinant plasmid pcDNA3.1-X, the concentration of HSV-1 serum IgM antibodies of the experimental group was 12.13±0.85 ng/L, which was significantly higher than that of the vector control group(0.49±0.44 ng/L; t=21.07, P<0.001). The concentrations of cytokines interleukin IL-2, IL-4, IL-10, and IFN-γ in the experimental group were 11.63±0.60, 22.65±1.47, 85.75±14.12, and 114.90±6.39 ng/L, respectively, all of which were significantly higher than those in the vector control group and the blank control group(all P<0.05). Immunohistochemical staining revealed the presence of target protein X in the eyeball, trigeminal ganglion, and brain tissue.CONCLUSION: The HSV-1 gB and gD tandem epitope nucleotides vaccine pcDNA3.1-X was successfully constructed, which activates a remarkable immune response and is stably expressed in the eyeball, trigeminal ganglion, and brain tissue. This study provides a foundation for further research of an HSV-1 recombinant antigen epitope tandem vaccine.

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.