Recent advances have highlighted the gut microbiota as a significant contributor to the development and progression of atherosclerosis, which is an inflammatory cardiovascular disease (CVD) characterized by plaque buildup within arterial walls. The gut microbiota, consisting of a diverse collection of microorganisms, impacts the host’s metabolism, immune responses, and lipid processing, all of which contribute to atherosclerosis. This review explores the complex mechanisms through which gut dysbiosis promotes atherogenesis. We emphasize the potential of integrating microbiota modulation with traditional cardiovascular care, offering a holistic approach to managing atherosclerosis. Important pathways involve the translocation of inflammatory microbial components, modulation of lipid metabolism through metabolites such as trimethylamine-N-oxide (TMAO), and the production of short-chain fatty acids (SCFAs) that influence vascular health. Studies reveal distinct microbial profiles in atherosclerosis patients, with increased pathogenic bacteria (Megamonas, Veillonella, Streptococcus) and reduced anti-inflammatory genera (Bifidobacterium, Roseburia), highlighting the potential of these profiles as biomarkers and therapeutic targets. Probiotics are live microorganisms that have health benefits on the host. Prebiotics are non-digestible dietary fibers that stimulate the growth and activity of beneficial gut bacteria. Interventions targeting microbiota, such as probiotics, prebiotics, dietary modifications, and faecal microbiota transplantation (FMT), present effective approaches for restoring microbial equilibrium and justifying cardiovascular risk. Future research should focus on longitudinal, multi-omics studies to clarify causal links and refine therapeutic applications.

Recent research indicates that the microbiome has a significant impact on the progression of inflammatory bowel disease (IBD) and that creating therapies that change its composition could positively impact the outcomes of IBD treatment. This review summarizes the results of extensive studies that examined IBD patients undergoing several therapies, including anti-TNF medication, vedolizumab, ustekinumab, probiotics, and fecal microbiota transplantation (FMT), and the alterations in their gut microbiota’s composition and function. The objective was to investigate the variety and effectiveness of microbial species in order to discover new biomarkers or therapeutic targets that could improve the outcome of treatment for these patients. This research aimed to offer useful insights into personalized medicine techniques for managing IBD. Beneficial bacteria such as Faecalibacterium prausnitzii and Roseburia have been consistently linked to favorable clinical outcomes, whereas pathogenic bacteria such as Escherichia coli and Clostridioides difficile are associated with worsening disease conditions. Although many studies have examined the role of gut microbiota in IBD, there is still a need for more targeted research on the connection between specific microbial communities and treatment outcomes. This study sought to address this gap by exploring the intricate relationship between the gut microbiota composition and the effectiveness of IBD medications.

Background: Recent studies have shown that gut microbiota have important roles in different human diseases. There has been an ever-increasing application of high-throughput technologies for the characterization of microbial ecosystems. This led to an explosion of various molecular profiling data, and the analysis of such data has shown that machine-learning algorithms have been useful in identifying key molecular signatures. Results: In this review, we first analyze how dysbiosis of the intestinal microbiota relates to human disease and how possible modulation of the gut microbial ecosystem may be used for disease intervention. Further, we introduce categories and the workflows of different machine-learning approaches and how they perform integrative analysis of multi-omics data. Last, we review advances of machine learning in gut microbiome applications and discuss challenges it faces. Conclusions: We conclude that machine learning is indeed well suited for analyzing gut microbiome and that these approaches are beneficial for developing gut microbe-targeted therapies, helping in achieving personalized and precision medicine.

It is well known that rheumatoid arthritis (RA) patients are at an increased risk of developing non-infectious pulmonary complications, especially interstitial lung disease (ILD); however, the clinician must keep in mind that lung disease could not only be a manifestation of the underlying condition, but also a consequence of using disease-modifying therapies. New-onset ILD or ILD worsening has also been reported as a possible consequence of both conventional disease-modifying antirheumatic drugs (DMARDs) and biologic agents. This study is a narrative review of the current literature regarding the potential risk of developing interstitial lung disease along with the administration of specific drugs used in controlling rheumatoid arthritis. Its purpose is to fill knowledge gaps related to this challenging patient cohort by addressing various aspects of the disease, including prevalence, disease features, treatment strategies, and patient outcomes.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disease, and it leads to end-stage renal disease (ESRD). The clinical manifestations of ADPKD are variable, with extreme differences observable in its progression, even among members of the same family with the same genetic mutation. In an age of new therapeutic options, it is important to identify patients with rapidly progressive evolution and the risk factors involved in the disease’s poor prognosis. As the pathophysiological mechanisms of the formation and growth of renal cysts have been clarified, new treatment options have been proposed to slow the progression to end-stage renal disease. Furthermore, in addition to the conventional factors (PKD1 mutation, hypertension, proteinuria, total kidney volume), increasing numbers of studies have recently identified new serum and urinary biomarkers of the disease’s progression, which are cheaper and more easily to dosing from the early stages of the disease. The present review discusses the utility of new biomarkers in the monitoring of the progress of ADPKD and their roles in new therapeutic approaches.

Background: Chronic ethanol consumption is a major global health concern traditionally associated with liver disease. Ethanol disrupts gut microbial communities, compromises intestinal barrier function, and contributes to hepatic, metabolic, and neurocognitive disorders. Methods: We conducted a systematic PubMed search and meta-analysis of 11 human and 19 animal studies evaluating ethanol-induced gut microbiota alterations. Studies were assessed for microbial diversity, taxonomic shifts, barrier integrity, and systemic effects. Effect sizes were calculated where possible, and interventional outcomes were examined. Results: Across species, ethanol exposure was consistently associated with reduced microbial diversity and depletion of beneficial commensals such as Faecalibacterium, Lactobacillus, Akkermansia, and Bifidobacterium, alongside an expansion of proinflammatory taxa (Proteobacteria, Enterococcus, Veillonella). Our analysis uniquely highlights discrepancies between human and animal studies, including opposite trends in specific genera (e.g., Akkermansia and Bifidobacterium) and the impact of confounders such as antibiotic exposure in human cohorts. We also demonstrate that microbiota-targeted interventions can partially restore diversity and improve clinical or behavioral outcomes. Conclusions: This meta-analysis highlights reproducible patterns of ethanol-induced gut dysbiosis across both human and animal studies.