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.

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

Objective The relationship between serum uric acid(UA)levels and gait kinematics characteristics in patients with cerebral small vessel disease(CSVD)was investigated.Methods Retrospective analysis was conducted on patients with CSVD from outparient clinics of the Neurology and Rehabilitation Department of Zhongshan Hospital affiliated with Guangzhou University of Chinese Medicine from January 2023 to December 2023.The general information of patients were collected and the gait of patients was analyzed using three-dimensional gait analysis.Patients were then divided into mild gait disorder group(0-1 points),moderate gait disorder group(2-3 points),and severe gait dysfunction group(4-5 points)based on gait results.The total burden of CSVD imaging and serum results such as UA were collected.The relationship between UA level and CSVD gait disorders was analyzed.Results This study recruited 105 CSVD patients.Patients were divided into different groups based on the severity of their gait disorder including 40 in the mild group,49 in the moderate group,and 16 in the severe group.The blood uric acid level in the moderate group(358.43±13.44)μmol/L was higher than that in the mild group(336.00±12.48)μmol/L,and the blood uric acid level in the severe group(289.94±11.88)μmol/L was lower than that in the mild and moderate groups(P<0.05).The MoCA score in the severe gait disorder group(21.38±0.13)was lower than that in the mild and moderate groups(28.05±0.09 vs.25.22±0.10)(P<0.05).The step width of the CSVD severe load group was(13.26±2.80)cm compared to the light and moderate load groups[(11.22±1.70)cm vs.(11.65±2.70)cm]increased(P<0.05),and the left swing phase in the severe group(35.90%)decreased compared to the mild and moderate groups(38.50%vs.37.20%)(P<0.05).Spearman correlation analysis showed a negative correlation between UA levels and CMB(r=-0.20,P=0.04).Hyperuricemia was negatively correlated with brain atrophy(r=-0.20,P=0.04).In patients with mild to moderate gait disorders,there was a positive correlation between hyperuricemia and the total burden of gait disorders(r=0.25,P=0.02),and hyperuricemia and right gait speed(r=-0.22,P=0.04),Right stride(r=-0.29,P<0.01),Left step speed(r=-0.32,P<0.01),Left step frequency(r=-0.29,P<0.01),The left stride was negatively correlated(r=-0.26,P=0.01).Conclusion In CSVD patients with mild to moderate gait disorders,the levels of uric acid and hyperuricemia are positively correlated with the total burden of gait disorders.The gait disorders are mainly characterized by reduced bilateral pace,bilateral stride,and left step frequency.

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.

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.

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.