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.

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）］

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）］

Natural polysaccharides have important physiological effects on immune regulation,and their effects on natural and ac-quired immunity are worthy of further discussion.Natural polysaccharides can regulate the innate immune system by regulating macro-phages,natural killer cells,inflammatory response,physiological barrier,complement system,and oxidation of the body,and they can also regulate the adaptive immune system by regulating humoral immunity and cellular immunity.Therefore,this article reviews the re-search advances in the role of natural polysaccharides in innate immunity and adaptive immunity,in order to provide new ideas for fur-ther research.

Tumor microenvironment(TME)is the cellular environment for tumor development,growth,and metastasis.Adenosine(ADO)is an immunosuppressive metabolic product that is continuously upregulated in TME,with various types and wide distribution of receptors.The complex and dynamic interactions between ADO and tumor cells constantly influence tumor progression.ADO can di-rectly or indirectly promote tumor development and progression by promoting tumor generation and metastasis,mediating the immune escape of tumor,and modulating tumor-infiltrating immune cells.Based on the characteristics of ADOs in TME,this article reviews the latest advances in the dynamic alterations of ADO in TME,in order to provide insights into tumor treatment targeting the ADO pathway.

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

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.

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.