Gut microbiota is associated with a variety of diseases, but its relationship with severe pneumonia remains to be explored. This study primarily analyzed the intestinal microbiota of patients with severe pneumonia and examined its association with clinical data. We collected clinical data from 96 patients with severe pneumonia for differential analysis and identified prognostic factors using logistic regression. Fecal samples from patients with severe pneumonia and healthy controls were collected and analyzed using 16S rRNA sequencing. We applied three machine learning algorithms (LASSO, Random Forest, and SVM) to identify microbial markers associated with severe pneumonia. The patients were grouped by "discharge status". Significant differences were observed in age (p = 0.025), total length of hospital stay (p < 0.001), and C-reactive protein (CRP) (p < 0.001). Logistic regression analysis identified age (p = 0.024) and total hospital stay (p < 0.001) as factors influencing the likelihood of improvement and discharge. Diversity analysis of collected stool samples revealed differences between the two groups. LDA Effect Size (LEfSe) analysis highlighted significant microbial differences at various taxonomic levels between the two populations. Three machine learning algorithms identified 9 microbial markers for severe pneumonia. A diagnostic prediction model was constructed, with an area under the Receiver Operating Characteristic (ROC) curve of 0.969 (95% CI: 0.946-0.992). Patients with severe pneumonia exhibit unique intestinal microbiota characteristics, which may be regulated by age and total length of hospital stay, thereby influencing the disease's prognosis.
The gut microbiome is linked to body composition, yet most studies involve probiotic or dietary interventions. This study explored relationships between changes in body composition and the fecal microbiota under natural lifestyle conditions. A repeated-measures design involved 15 adults completing four body composition assessments at 3-month intervals. Fecal samples from each time point underwent 16S rRNA gene sequencing. Participants were stratified by body composition parameters, and microbial profiles from initial and final measurements were compared to assess longitudinal patterns. Overweight participants showed lower alpha diversity. Linear mixed models revealed fecal microbiota remained stable across all four time points, with no statistically significant continuous trends observed longitudinally. Exploratory baseline-to-endpoint comparisons across stratified groups and Spearman correlation analyses suggested potential microbiota shifts, though these associations remained statistically non-significant. Preliminary observations exhibited that the OTU identified as Parasutterella excrementihominis tended to associate with higher body fat, whereas the putative species Akkermansia muciniphila showed a potential inverse association. Representative taxa, such as Dialister invisus, appeared enriched in individuals with higher skeletal muscle percentages, whereas the OTU assigned to Bifidobacterium pseudocatenulatum showed the opposite trend. Several associations differed by sex, suggesting modulation by host factors. These preliminary findings suggest possible fecal microbiota patterns associated with body composition, even without targeted interventions. While lacking robust linear associations in this small pilot cohort, the observed directional consistency across statistical approaches highlights the potential of fecal microbes as candidate indicators of metabolic health. These exploratory results require further validation in larger, longitudinal studies with sufficient statistical power.
This study identified key intra-tumor microbial signatures distinguishing head and neck cancers from gastrointestinal cancers and explored their diagnostic and prognostic potential. Intra-tumor microbial data of five cancer types were obtained from the Cancer Microbiome Atlas, and corresponding clinical data were retrieved from the Cancer Genome Atlas. The Wilcoxon test was used to analyze differences in microbial populations. Univariate logistic regression, least absolute shrinkage and selection operator, and recursive feature elimination were sequentially applied to screen optimal microbial markers, and a support vector machine classification model was constructed. A nomogram model and Kaplan-Meier curves were used to validate the predictive and prognostic value of the optimal microbes, respectively. Overall, 463 tumor samples and 47 controls were included. Twenty-three microbes showed significant differences in distribution between head and neck and gastrointestinal tumors; among these, eight overlapping microbes were selected as optimal markers. The SVM model based on these eight microbes achieved AUCs of 0.937 and 0.856 in the training and validation datasets, respectively. The nomogram model constructed with these markers showed high predictive accuracy (C-index = 0.8944 in training, 0.8023 in validation). Kaplan-Meier analysis revealed that high abundance of Capnocytophaga, Lachnospiraceae, and Bacteroidales was significantly associated with longer overall survival in both head and neck tumors and gastrointestinal tumors (all P < 0.05). The eight intra-tumor microbial communities serve as a robust signature for distinguishing head and neck tumors from gastrointestinal tumors. Among these, Capnocytophaga, Lachnospiraceae, and Bacteroidales have potential as prognostic biomarkers to improve survival prediction in cancers.
The composition of the intestinal microbiome has been identified as a key factor influencing the efficacy of immune checkpoint inhibitors. This study aimed to systematically evaluate the potential associations among gut microbiota, metabolic profiles, and clinical outcomes in patients with MSI-H advanced colorectal cancer (CRC) treated with immunotherapy. Twenty advanced CRC patients receiving immunotherapy were enrolled and categorized into clinical benefit response (CBR) and non-benefit (NCB) groups based on treatment efficacy. Fecal samples were analyzed using metagenomic sequencing and untargeted metabolomics. The results revealed significant enrichments of s_Clostridium unclassified and metabolites such as guanosine, 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid, and quercetin 3-(6"-malonyl-glucoside) in the CBR group, suggesting their potential positive predictive value for immunotherapy response. Conversely, the NCB group showed significant enrichments of s_Roseburia hominis, s_Marseilla massiliensis, and metabolites including pyrophosphate, riboflavin, and PC(22:5(4Z,7Z,10Z,13Z,16Z)/14:0), indicating a possible association with treatment resistance. By integrating fecal metagenomics and metabolomics, this study reveals distinctive "flora-metabolite" interactions linked to therapeutic response in advanced CRC patients undergoing immunotherapy. Specific microbial and metabolic profiles were positively or negatively correlated with immunotherapy outcomes, highlighting their potential not only as predictive biomarkers but also as a theoretical foundation for developing individualized immunotherapy strategies based on microecological modulation.
The review article is a critical examination of the growing global problem of multidrug-resistant (MDR) Salmonella and Shigella, focusing on their epidemiology, molecular resistance mechanisms, and clinical effects. It compiles current information on increasing resistance to major antibiotics, such as fluoroquinolones, third-generation cephalosporins, and sulfonamides, that is caused by a variety of factors, including ESBL production and plasmid-mediated gene transfer, especially in Asia, Africa, and the Middle East. The minireview discusses the new therapeutic approaches such as bacteriophage therapy, antimicrobial peptides, antimicrobials based on nanotechnology, and a combination of antibiotic regimens and their mechanisms, efficacy, and some limitations that exist in these approaches. In addition, it discusses the issues of diagnostic challenges, infection control practices, and antibiotic stewardship as part and parcel of resistance management. It will be a focused, evidence-based review to inform research, clinical practices, and policy to turn the trend of resistance around and enhance patient outcomes. The presented multidisciplinary review shows that MDR Salmonella and Shigella infections have become a threatening problem in the world, which requires urgent measures, including multilateral cooperation, improved molecular tracking, fast diagnostics, and new therapeutic options.
