Objective To predict and preliminarily validate potential shared key genes involved in cardiac complications caused by influenza A virus(IAV)and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infections.Methods Differentially expressed genes(DEGs)associated with cardiac complications were obtained from the Gene Expression Omnibus(GEO)database.A hierarchical intersection strategy was applied.First,cardiac complication related DEGs were overlapped with 2 independent virus related gene sets:3 454 human genes linked to IAV infection in GeneCards and 333 human protein-coding genes interacting with SARS-CoV-2 in the Human Protein Atlas.The 2 overlap results were then intersected to yield 22 hub genes.Lasso regression,random forest(RF)and support vector machine algorithms(SVM)were employed to refine this list.Predicted genes were validated in vitro in H1N1-infected human cardiomyocyte AC16 cells and in vivo in IFITM3 knockout mice challenged with H1N1,assessing transcriptional changes.Results A total of 22 hub genes were identified through integrative bioinformatics analysis.Application of the 3 machine learning algorithms resulted in 5 common key genes:ACE2,TBK1,NUP210,PUSL1,and MEPCE.In vitro infection of AC16 cells with H1N1 revealed dynamic transcriptional changes in all 5 genes post-infection(P<0.05).In vivo experiments using H1N1-infected IFITM3 knockout mice confirmed the dynamic mRNA expression changes of these 5 genes,consistent with the in vitro results(P<0.05).Conclusion By combining multilayered bioinformatics analysis with 3 machine learning approaches,5 common key genes are identified:ACE2,TBK1,NUP210,PUSL1 and MEPCE.Validation in H1N1 infection models confirms their relevance to IAV-induced cardiac complications.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with few effective treatments currently available. The multifactorial nature of AD, shaped by genetic, environmental, and biological factors, complicates both research and clinical management. Recent advances in artificial intelligence (AI) and multi-omics technologies provide new opportunities to elucidate the molecular mechanisms of AD and identify early biomarkers for diagnosis and prognosis. AI-driven approaches such as machine learning, deep learning, and network-based models have enabled the integration of large-scale genomic, transcriptomic, proteomic, metabolomic, and microbiomic datasets. These efforts have facilitated the discovery of novel molecular signatures and therapeutic targets. Methods including deep belief networks and joint deep semi-non-negative matrix factorization have contributed to improvements in disease classification and patient stratification. However, ongoing challenges remain. These include data heterogeneity, limited interpretability of complex models, a lack of large and diverse datasets, and insufficient clinical validation. The absence of standardized multi-omics data processing methods further restricts progress. This review systematically summarizes recent advances in AI-driven multi-omics research in AD, highlighting achievements in early diagnosis and biomarker discovery while discussing limitations and future directions needed to advance these approaches toward clinical application.

Objective:Our aim is to investigate the relationship of VEGF-C,VEGF-D and lymph node metastasis in pancreatic cancer,and to elucidate the role and significance of the cancerous peripheral lymphatic tissue.Methods: The expression of VEGF-C and VEGF-D was assayed by means of immunohistochemistry in 30 pancreatic carcinomas.Results:The positive rates of VEGF-C、VEGF-D were 73% (22/30)、57% (17/30) respectively in pancreatic cancer.The expression of VEGF-C、 VEGF-D in cancerous invasive edge was significantly higher than that in the center of cancerous tissues.There was no correlation between the expression of VEGF-C、VEGF-Dand the site,differentiation,histology types.Ⅲ~Ⅳ stages of pancreatic cancer showed strong expressions of VEGF-C、VEGF-D than that Ⅰ~Ⅱ stages of pancreatic cancer.The positive lymph node in positive VEGF-C and VEGF-D group were higher than that in the negative group.Conclusion:VEGF-C and VEGF-D induced lymphangiogenisis in pancreatic cancer,promoted the tumor cell lymph metastasis.

Objective To improve the traditional percutaneous cholecystolithotripsy in order to elevate the success rate and to discuss its indications. Methods With selfmade apparatus, we modified the open procedure which is otherwise impossible in removing the stones. We have followed 120 patients for 3 to 36 months to look into the recurrence of cholelithiasis. Results We have succeeded in 287 operations in a total of 291 (98 6%) cases. Among which 99 cases (34 5%) received modified open procedures, and the postoperative course was all uneventful. 120 patients were followed up postoperatively from 3 to 36 months and the stone recurrence rate was 13 3% (16 cases). Statistical analysis showed that the stone recurrence rate in patients with 3 stones and up, or poor gallbladder function was significantly higher than those with less than 3 stones and or good gallbladder function.Conclusions The percutaneous cholecystolithotripsy with improved procedures can effectively increase the success rate of operation. According to our result the indications should be restricted to those with less than 3 stones and or with good gallbladder functions.