Depression as a common complication of brain tumors. Is there a possible common pathogenesis for depression and glioma? The most serious major depressive disorder (MDD) and glioblastoma (GBM) in both diseases are studied, to explore the common pathogenesis between the two diseases. In this article, we first rely on transcriptome data to obtain reliable and useful differentially expressed genes (DEGs) by differential expression analysis. Then, we used the transcriptomics of DEGs to find out and analyze the common pathway of MDD and GBM from three directions. Finally, we determine the important biological pathways that are common to MDD and GBM by statistical knowledge. Our findings provide the first direct transcriptomic evidence that common pathway in two diseases for the common pathogenesis of the human MDD and GBM. Our results provide a new reference methods and values for the study of the pathogenesis of depression and glioblastoma.

Inflammatory bowel disease (IBD) is a chronic and recurring lifelong condition, the exact etiology of which remains obscure. However, an increasing corpus of research underscores the pivotal role of cellular signaling pathways in both the instigation and management of intestinal inflammation. Drosophila, owing to its prodigious offspring, abbreviated life cycle, and the conservation of signaling pathways with mammals, among other advantages, has become a model organism for IBD research. This review will expound on the feasibility of utilizing Drosophila as an IBD model, comparing its intestinal architecture with that of mammals, its inflammatory responses, and signaling pathways. Furthermore, it will deliberate on the role of natural products across various biological models of IBD pathways, elucidating the viability of fruit flies as IBD models and the modus operandi of cellular signaling pathways in the context of IBD.

Antibiotics are double-edged swords. Although antibiotics are used to inhibit pathogenic bacteria, they also run the risk of destroying some of the healthy bacteria in our bodies. We examined the effect of penicillin on the organism through a microarray dataset, after which 12 genes related to immuno-inflammatory pathways were selected by reading the literature and validated using neomycin and ampicillin. The expression of genes was measured using qRT-PCR. Several genes were significantly overexpressed in antibiotic-treated mice, including CD74 and SAA2 in intestinal tissues that remained extremely expressed after natural recovery. Moreover, transplantation of fecal microbiota from healthy mice to antibiotic-treated mice was made, where GZMB, CD3G, H2-AA, PSMB9, CD74, and SAA1 were greatly expressed; however, SAA2 was downregulated and normal expression was restored, and in liver tissue, SAA1, SAA2, SAA3 were extremely expressed. After the addition of vitamin C, which has positive effects in several aspects, to the fecal microbiota transplantation, in the intestinal tissues, the genes that were highly expressed after the fecal microbiota transplantation effectively reduced their expression, and the unaffected genes remained normally expressed, but the CD74 gene remained highly expressed. In liver tissues, normally expressed genes were not affected, but the expression of SAA1 was reduced and the expression of SAA3 was increased. In other words, fecal microbiota transplantation did not necessarily bring about a positive effect of gene expression restoration, but the addition of vitamin C effectively reduced the effects of fecal microbiota transplantation and regulated the balance of the immune system.

Background/Objectives: Liver cirrhosis is a major global health challenge and a key risk factor for hepatocellular carcinoma (HCC), a malignancy with high mortality due to late diagnosis. This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data, using single-cell data to identify macrophage-associated transcriptomic changes during the progression from cirrhosis to HCC, and using bulk data to validate these findings in independent cohorts, while developing predictive models for early risk assessment. Methods: We integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing datasets derived from liver tissues of cirrhosis and HCC patients. Single-cell data were used to identify macrophage subtypes and their dynamic transcriptional changes, while bulk data provided validation in independent cohorts. Gene expression and network analyses were performed, and candidate genes were used to construct diagnostic models with Lasso regression, Random Forest, and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using receiver operating characteristic curves. Results: We identified eleven macrophage-associated genes, among which KLK11, MARCO, CFP, KRT19, GAS1, SOD3, and CYP2C8 were downregulated in HCC, indicating loss of tumor-suppressive and pro-apoptotic functions, while TOP2A, CENPF, MKI67, and NUPR1 were upregulated, reflecting enhanced cell cycle progression, proliferation, and M2 polarization. These are all associated with the progression from liver cirrhosis to HCC. Based on these findings, we established predictive models using Lasso, Random Forest, and XGBoost, which stratified cirrhotic patients into high- and low-risk groups according to cutoff values using liver tissue transcriptomic data. All three models demonstrated high diagnostic performance. Conclusions: This study highlights the critical role of macrophage-associated transcriptomic remodeling in liver disease progression. The machine learning–based predictive models offer a promising approach for early diagnosis and clinical decision-making in patients with cirrhosis.

Gut microbiota dysbiosis has been associated with ischemic stroke (IS), myocardial infarction (MI), and aging, but whether these contexts share reproducible microbial features remains unclear. We conducted an exploratory and hypothesis-generating descriptive study of genus-level microbiota patterns across an internal IS cohort and publicly available external IS, MI, and age-stratified or longevity-associated datasets. Analyses were performed within predefined age strata and interpreted cautiously because of the small internal cohort, cross-cohort heterogeneity, and the absence of direct metabolite, intestinal barrier, inflammatory, or microbial activity measurements. No taxon in the internal cohort remained statistically significant after false-discovery-rate correction; therefore, all taxonomic observations were treated as descriptive. Candidate overlapping features included repeated detection of Escherichia-Shigella and Klebsiella and non-uniform patterns among genera previously associated with short-chain fatty acid metabolism, including Faecalibacterium, Blautia, and Roseburia. Lachnoclostridium and Bacteroides showed opposite abundance gradients in selected cross-dataset comparisons. These observations suggest possible ecological overlap across ischemic disease and age-associated microbiome contexts, but they do not establish causality, disease-specific biomarkers, or shared microbial function. The mechanistic models discussed in this manuscript are literature-informed hypotheses based on exploratory compositional data and require future validation in larger, harmonized longitudinal cohorts using metagenomic, metabolomic, clinical, and experimental measurements.