Purpose: Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system characterized by progressive disability. Emerging evidence has implicated gut microbiome dysbiosis, characterized by decreased short‐chain fatty acids (SCFAs)‐producing taxa and increased pro‐inflammatory species, in disturbed immune signaling, T‐helper17/T‐regulatory cells imbalance, disturbed tryptophan metabolism, and disrupted integrity of the blood–brain barrier. In this review, we summarize the mechanistic and therapeutic insights from studies that have explored the gut microbiome in MS. Method: We performed a literature search in PubMed, Scopus, Web of Science, and ClinicalTrials.gov from database inception to January 2025; only English‐language articles were included, comprising human MS cohorts and preclinical experimental autoimmune encephalomyelitis models. Of these, approximately 95 human and preclinical studies fulfilled the inclusion criteria. Evidence synthesis was narrative, without meta‐analysis. Finding: There has been a consistent depletion of beneficial genera such as Faecalibacterium and Roseburia, expansion of Akkermansia muciniphila, and reduction in microbial metabolites such as butyrate, propionate, and neuroactive indole derivatives in MS patients across studies. These changes promote intestinal permeability, exaggerated pro‐inflammatory cytokine responses, and microglial activation. The therapeutic approach of restoring microbial balance includes therapies such as probiotics, prebiotics, synbiotics, fecal microbiota transplantation, and dietary interventions. Early trials have shown modest improvements in relapse rates, fatigue, immune profiles, and microbiome composition. Results across randomized studies are heterogeneous, with no significant clinical benefit in several. Pilot trials report modest reductions in relapse rate (RR ≈ 0.85) and fatigue (Cohen's d ≈ 0.3), but several double‑blind RCTs showed no significant benefit ( p > 0.05) in up to 40% of participants, highlighting variable effect sizes. Conclusion: Interventions aimed at the microbiome are promising as adjunct approaches to the treatment of MS, acting principally through the restoration of SCFAs, immune modulation, and strengthening of the gut‐brain axis. Larger, longer‐term randomized trials are required to confirm clinical efficacy, define responder phenotypes, and inform personalized microbiome‐based therapies.

Background: Postoperative cognitive dysfunction (POCD) is a common postoperative condition after neurosurgery, and in patients of advancing age, with far-reaching implications for recovery and quality of life. Current evidence points to the gut–brain axis as the main mechanism for the regulation of perioperative neuroinflammation and cognition. Objective: The aim of this review is to consolidate the existing evidence for perioperative gut microbiome dysbiosis in POCD, specifically in neurosurgical patients. Methods: A review of preclinical and clinical evidence on the gut microbiome, surgical stress, and cognitive recovery was conducted. Both mechanistic and therapeutic evidence were examined. Results: Surgery and anesthesia enhance gut microbial diversity, intestinal permeability, and systemic inflammation, thereby compromising neuroplasticity and the integrity of blood–brain barriers. Preclinical models show that interventions to reestablish microbial homeostasis with probiotics, prebiotics, or fecal microbiota transplantation decrease postoperative cognition. Clinical studies offer evidence supporting the associations between decreased short-chain fatty acid-producing bacteria and POCD risk. Randomized controlled trials have demonstrated that perioperative probiotics lower the incidence and markers of POCD. Multi-omic approaches to integrating microbiome, metabolome, and neuroimaging signatures are being engineered to discern recovery phenotypes prior to surgery. Conclusions: Perioperative gut microbiota are a modifiable target for the optimization of cognitive recovery from neurosurgery. The inclusion of microbiome treatments and diagnostics into standard surgical care pathways is one potential pathway to POCD minimization, but large randomized trials will be necessary to establish this.

Endometriosis is a gynecologic inflammatory condition that affects up to 10% of reproductive-aged women worldwide. The disease exhibits heterogeneous presentations and is associated with a prolonged diagnostic delay, often exceeding seven years, because existing diagnostic modalities such as transvaginal ultrasound, magnetic resonance imaging, and the biomarker cancer antigen 125 (CA-125) are suboptimal. This review examines how machine learning (ML) is playing an increasingly significant role in early, non-surgical endometriosis diagnosis through two main approaches: symptom clustering and imaging integration. Unsupervised ML algorithms such as k-means, partitioning around medoids, and Bayesian networks have demonstrated success in identifying clinically informative endometriosis phenotypes from patient-reported symptoms and electronic health records. Concurrently, ML models such as convolutional neural networks and radiomics approaches have achieved high accuracy in lesion detection from imaging data, in some cases surpassing human interpretation. Despite these advances, significant challenges remain, including limited access to large, annotated multimodal datasets, the absence of widely accepted evaluation standards, and concerns regarding interpretability and generalizability. Multicenter, integrative studies and the incorporation of explainability techniques are recommended as potential strategies to address these gaps. Finally, multimodal ML approaches that combine symptomatology and imaging data hold substantial promise for reducing diagnostic delays, facilitating early intervention, and improving clinical outcomes in the management of endometriosis.

Laparoscopic cholecystectomy, introduced in 1985 by Prof. Dr. Erich Mühe, has become the gold standard for treating chronic symptomatic calculous cholecystopathy and acute cholecystitis, with an estimated 750,000 procedures performed annually in the United States of America. The risk of iatrogenic bile duct injury persists, ranging from 0.2 to 1.3%. Risk factors include male gender, obesity, acute cholecystitis, previous hepatobiliary surgery, and anatomical variations in Calot’s triangle. Strategies to mitigate bile duct injury include the Critical View of Safety and fundus-first dissection, along with intraoperative cholangiography and alternative approaches like subtotal cholecystectomy.

Introduction: Chronic kidney disease (CKD) is a progressive illness with high morbidity and mortality that warrants early and accurate risk stratification for optimal management. The traditional biomarkers, serum creatinine and estimated glomerular filtration rate (eGFR), are insufficient for detecting early CKD and long-term prognosis. Novel biomarkers have emerged as effective tools to complement CKD diagnosis, prognosis, and therapeutic monitoring. Aim: The aim of this research was to determine the potential of novel biomarkers in CKD risk stratification and their clinical significance for improving early detection, monitoring disease progression, and developing individualized treatment strategies. Methods: A literature review was conducted by searching the PubMed, Scopus, and Embase databases to identify studies on novel CKD biomarkers, including cystatin C, neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and specific microRNAs. Results: Emerging evidence suggests that novel biomarkers provide superior predictive abilities compared to traditional markers. Cystatin C is more accurate in kidney function estimation, whereas NGAL and KIM-1 are markers of early kidney injury. MicroRNAs show potential in distinguishing between CKD subtypes and predicting disease progression. Clinical application of these biomarkers may enhance CKD risk stratification, allowing more targeted intervention strategies. Conclusion: New biomarkers in CKD risk stratification represent a watershed moment in nephrology, offering improved early detection and prognostic accuracy. While promising, additional large-scale research and clinical validation are required before they can be used routinely.