Search Results (4,780)

Background/Objectives: Medication-related harm is a leading global patient safety challenge, yet patients’ lived experiences of medication safety remain underexplored in Central and Eastern European healthcare systems, where structural constraints significantly shape everyday medication use. Methods: This study provides an in-depth qualitative analysis of Hungarian patients’ online narratives, building on a prior netnographic mixed-methods study. Using grounded theory-informed principles and a patient-centred medication safety framework, we inductively analysed 5174 publicly accessible Hungarian-language comments posted on health forums and social media platforms between August 2020 and August 2023. The COM-B model was applied as a secondary lens to map findings onto modifiable behavioural determinants. Results: Access to services and communication emerged as the dominant medication safety concerns. Patients reported long waiting times, limited rural emergency services, and brief consultations leading to delayed or inadequate treatment. Communication gaps included insufficient information on medication duration, side effects, and follow-up, as well as conflicting advice from multiple sources, all of which eroded trust and prompted treatment discontinuation or reliance on informal online communities. Community pharmacists were largely absent from patients’ mental models of care, representing a significant missed opportunity given their accessibility. Less frequently mentioned were medication shortages, healthcare professional workload, and systemic safety culture. Conclusions: Clear, respectful communication and timely access to care are central to medication safety from the patient perspective. Netnography combined with a grounded theory-informed methodology offers a valuable approach for capturing authentic patient perspectives in structurally burdened healthcare systems, with findings relevant beyond the Hungarian context. Full article

Objective: To evaluate the impact of urban–rural health insurance integration on the health outcomes and financial burden of rural older adults. Methods: Utilizing panel data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2013 to 2018, we employed a staggered difference-in-differences model coupled with propensity score matching (PSM-DID) for rigorous causal identification. Results: The policy significantly reduced out-of-pocket medical expenditures for rural households by approximately 5.6% (p = 0.034). Concurrently, significant improvements were observed in both physical health (a 0.092-point reduction in ADL impairment scores) and mental health (a 0.725-point reduction in CES-D depression scores). Mechanism analyses revealed that the integration did not significantly increase the probability of outpatient or inpatient visits—thereby ruling out supplier-induced demand and moral hazard—while effectively reducing the incidence of catastrophic health expenditure by 1.9% (p = 0.004). Heterogeneity analyses indicated that while the financial relief was universally distributed across varying educational levels, the policy dividends were predominantly captured by the younger-old demographic. Notably, the reduction in financial burden was not statistically significant for the oldest-old cohort (aged 75 and older). Conclusions: The urban–rural health insurance integration has achieved a dual dividend of financial protection and health enhancement without triggering the overutilization of medical services. Nevertheless, the unmet care expenses for older adults with severe disabilities underscore the urgent necessity for a secondary safety net, such as long-term care insurance. Full article

Background/Objectives: Peripheral pulsed radiofrequency (PRF) is a minimally invasive option for trigeminal neuralgia (TN) with a favorable safety profile compared with neurorestorative techniques, but its durability and recurrence patterns remain uncertain. This study evaluated the early effectiveness, durability, recurrence-free survival, and safety of peripheral PRF in refractory classical or idiopathic TN. Methods: This retrospective single-center cohort study assessed procedure-level outcomes of peripheral PRF targeting the ophthalmic, maxillary, and mandibular branches. Pain intensity and clinical status were evaluated using the Numeric Rating Scale (NRS) and Barrow Neurological Institute (BNI) pain score. Early effectiveness was defined as clinically meaningful pain relief sustained for at least 1 month, and sustained effectiveness as NRS ≤ 3 at 6 months. Recurrence-free survival was analyzed using Kaplan–Meier methods. Results: A total of 68 procedures in 57 patients were analyzed. Early effectiveness at 1 month was achieved in 85.3% of procedures. Median NRS decreased from 9 (IQR 8–9) at baseline to 2 (0–4) at 1 month and 0 (0–2) at 3 and 6 months (p < 0.001). In a worst-case analysis, 6-month sustained effectiveness was 72.1%. Recurrence occurred in 61.8% of procedures, with a median recurrence-free survival of 11 months. Among procedures with recurrence, repeat peripheral PRF was performed in 45.2%. Medication requirements decreased in 66.2% of procedures, and no major complications occurred. Conclusions: Peripheral PRF provides rapid and meaningful early pain relief in TN, but durability is limited. These findings support peripheral PRF as a safe, repeatable neuromodulatory intervention within a staged treatment strategy rather than a definitive therapy. Full article

Background/Objectives: Extramedullary disease (EMD) is an aggressive and treatment-resistant manifestation of multiple myeloma with limited therapeutic options, particularly in heavily pretreated patients. Methods: We conducted a retrospective study to evaluate the efficacy and safety of concurrent immune checkpoint inhibitors (ICIs) and radiation therapy (RT) in patients with EMD treated at Hackensack University Medical Center and John Theurer Cancer Center between January 2016 and May 2025. Patients were included if they had confirmed EMD and received nivolumab or pembrolizumab with concurrent RT. Results: A total of 21 patients were included, representing a high-risk cohort with a median of 6 prior lines of therapy (range 2–13), including 47.6% triple-class refractory and 19.0% penta-refractory disease. The overall response rate (ORR) was 47.6%, with a clinical benefit rate of 57.1%. Despite these responses, median progression-free survival (PFS) and overall survival (OS) were 4 and 12 months, respectively. Notably, two patients achieved complete responses with nivolumab and RT early in their treatment course following cellular therapy and remain disease-free at last follow-up. The combination of ICIs and RT was generally well-tolerated, with manageable immune-related adverse events and no treatment-related deaths. Conclusions: These findings suggest that concurrent ICI and RT may provide a signal of treatment responses in a subset of patients with advanced EMD, although durability remains limited. Further prospective studies are warranted to further define the role of this combination and identify patients most likely to benefit. Full article

Background: Immune checkpoint inhibitors have significantly improved survival in metastatic melanoma. Combination nivolumab plus ipilimumab demonstrated superior efficacy in randomized trials, including CheckMate 067, but data beyond the first-line setting remain limited. This study evaluated real-world outcomes and predictors of response across different lines of therapy, with an exploratory comparison between first- and second-line use. Methods: This retrospective single-center study included patients with metastatic melanoma treated with nivolumab plus ipilimumab as first- or second-line therapy at Moscow City Oncology Hospital No. 62 between September 2015 and October 2023. Eligible patients had histologically confirmed melanoma and received at least one cycle of dual immune checkpoint blockade. Clinical and demographic data were extracted from electronic medical records. The primary endpoints were progression-free survival (PFS) and overall survival (OS); secondary endpoints included objective response rate (ORR) and safety. Survival outcomes were estimated using the Kaplan–Meier method and compared using the log-rank test. Multivariable Cox proportional hazards models adjusted for clinically relevant covariates were applied to evaluate the association between treatment line and survival outcomes. Additional prognostic analyses were performed using backward stepwise multivariable Cox regression. Results: Median follow-up was 18.2 months (IQR, 6.7–30.4). Median PFS in the overall cohort was 7.9 months (95% CI, 4.2–11.5), and median OS was not reached (NR); 5-year OS: 50%. The ORR was 45.8%, including 15.1% complete responses. Median PFS was 9.0 months (95% CI, 5.0–12.9) in first-line and 6.1 months (95% CI, 3.4–8.8) in second-line patients. Median OS was NR in the first-line cohort and was 30.5 months (95% CI: NR) in the second-line cohort. In exploratory analyses, OS did not differ significantly between patients treated in the first-line (n = 141) versus second-line setting (n = 63) (p = 0.848). After adjustment for clinical and demographic characteristics, line of therapy was not associated with OS (HR 0.93; 95% CI, 0.58–1.50; p = 0.762). Immune-related adverse events were associated with longer PFS (HR 0.66; 95% CI, 0.46–0.93), although this may reflect time-dependent bias. Conclusions: Nivolumab plus ipilimumab demonstrated clinically meaningful activity in both first- and second-line settings. Outcomes were numerically lower than in clinical trials, consistent with broader real-world populations. In exploratory analyses, OS did not differ significantly between treatment lines after adjustment for clinical and demographic characteristics. These findings should be interpreted with caution given the retrospective design and potential sources of bias. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Background: As glucagon-like peptide-1 (GLP-1) receptor agonist use expands globally, reports of psychiatric adverse events (AEs) have increased in spontaneous reporting databases. However, which case-level characteristics are associated with these reporting patterns remains insufficiently characterized. This study aimed to characterize case-level features associated with psychiatric AE reporting in GLP-1 receptor agonist users through pharmacovigilance and explainable machine learning methods. Methods: The FDA Adverse Event Reporting System (FAERS) data (2021 Q2–2025 Q3) were analyzed using a comparator-based approach comprising other antidiabetic and anti-obesity agents. Disproportionality analyses (PRR, ROR, and IC) were performed to detect consolidated safety signals at the Preferred Term (PT) level, with additional drug-specific analyses for individual GLP-1 receptor agonists. Three classification models (logistic regression, XGBoost, and LightGBM) were developed, and SHAP values were used to identify case-level features associated with psychiatric AE reporting patterns. Results: A total of 211,195 unique cases were included (111,105 for GLP-1 receptor agonists; 100,090 for comparators). Sixteen PTs met consolidated signal criteria, with suicidal ideation being the most frequently reported (ROR 2.95). Drug-specific analyses indicated that signal magnitudes were consistently higher for semaglutide than tirzepatide. The XGBoost model achieved an AUROC of 0.816. SHAP analysis showed that age ≥65 years had the highest mean |SHAP| value (0.57) with a negative direction, corresponding to a lower predicted probability of psychiatric AE reporting in older adults. Semaglutide use ranked second (0.35) and showed a positive direction. Absence of concomitant medications (0.20) and diabetes indication (0.10) showed negative directions, while younger age (19–44 years, 0.08) showed a positive direction. These patterns remained consistent in sensitivity analysis excluding concomitant psychotropic medication users (AUROC 0.797). Conclusions: Older age status was associated with decreased predicted probability of psychiatric AE reporting, while semaglutide use and younger age showed positive contributions. These case-level patterns, identified through pharmacovigilance analysis using a comparator-based approach and explainable machine learning, suggest that reporting patterns may differ across subgroups and that the observed reporting heterogeneity among younger adults and semaglutide users merits further investigation in prospective studies. Full article

Agentic artificial intelligence (AI) offers new opportunities for intelligent clinical decision support, but deployment in emergency and crisis settings remains challenging because time-critical recommendations must often be generated under incomplete patient information and system constraints. Conventional clinical decision support systems rely on rule-based workflows that degrade when structured data are absent, while standalone language models lack coordination mechanisms to enforce mandatory safety checks. We present TriAgent, a multi-agent framework that unifies adaptive orchestration, iterative retrieval, embedded safety verification, and end-to-end auditability within a single crisis clinical decision support workflow. An Orchestrator Agent dynamically selects specialist modules for clinical assessment, retrieval, treatment planning, safety verification, and system coordination, with routing determined by model reasoning rather than fixed execution paths. A retrieval sub-agent performs iterative query refinement and relevance grading over 49,000 MIMIC-IV discharge notes, while medication-conflict screening and allergy-risk assessment are invoked in parallel only when clinically indicated. A Critique Agent reviews the full reasoning trace before recommendation finalization. In a retrospective evaluation on 1000 real emergency presentations under synthesized incomplete-information inputs, TriAgent achieved 85.0% critical-case recall and 65.7% overall triage accuracy, versus at most 14.7% and 43.4% for matched single-model and retrieval-only baselines, with safety checks executed on every continuation pathway and adaptive routing invoking only the modules each case required. These results support multi-agent orchestration as a promising design pattern for transparent and auditable AI in healthcare. These gains are internal system properties; clinical-safety benefit remains to be established through prospective, clinician-involved validation. Full article

Background: Pressure injuries (PIs) remain a major and escalating global patient safety concern, affecting 12.8% of hospitalized patients worldwide and contributing to rising prevalence and mortality rates from 5.63 to 8.18 and 0.31 to 0.47 per 100,000 population, respectively. The economic burden of PIs is substantial, amounting to $26.8 billion annually in the United States and $9608 per patient in the Kingdom of Saudi Arabia (KSA). However, adherence toward pressure injury prevention (PIP) guidelines among registered nurses (RNs) remains critically inconsistent. Objectives: The aim of this study was to examine whether attitude and practice function as parallel mediators in the relationship between knowledge and adherence to PIP guidelines among RNs, adjusting for age and years of experience. This aim was addressed through three-fold objectives: to assess RNs’ knowledge, attitude, practice (KAP) and adherence to PIP guidelines, evaluate the direct knowledge–adherence relationship, and quantify the two mediated pathways. Methods: A cross-sectional mediation study recruited 166 RNs from 52 clinical units at Prince Sultan Military Medical City in Riyadh, KSA, using convenience sampling with data collected from 5 to 15 March 2026. Validated instruments assessed KAP and adherence to PIP guidelines. Pearson correlations, multiple linear regression, and parallel mediation (Hayes PROCESS macro, model 4; 5000 bootstrap resamples) were performed with age and years of experience as covariates. Results: Mean scores indicated low knowledge (7.31/25; 29.2%), negative attitude (28.20/52; 54.2%), poor practice (36.10/110; 32.8%), and low adherence (28.40/90; 31.6%). Regression explained 36.8% of the variance in adherence to PIP guidelines (Adjusted R² = 0.35), with knowledge (β = 0.22; p < 0.003), attitude (β = 0.30; p < 0.001), practice (β = 0.20; p = 0.006), and years of experience (β = 0.12; p = 0.04) emerging as significant predictors. Both attitude (unstandardized indirect effect = 0.40; 95% Boot CI [0.20, 0.64]) and practice (indirect effect = 0.30; [0.10, 0.60]) significantly mediated the knowledge–adherence relationship, while knowledge retained a significant direct effect (B = 0.70, p = 0.003), indicating partial mediation. Conclusions: This study was the first to employ a parallel mediation analysis to examine KAP as concurrent predictors of adherence toward PIP guidelines within a tertiary military healthcare setting in the KSA. The mediating roles of attitude and practice, together with the direct effect of knowledge, indicated that adherence to PIP guidelines is shaped by interconnected cognitive and behavioral mechanisms. Persistently low KAP levels and low adherence, along with the predictive influence of all KAP domains and RNs’ years of experience, underscored the urgent need for integrated interventions that strengthen KAP to improve adherence and prevent PIs. Full article

Multiple factors such as global climate warming and environmental degradation have increased natural disaster frequencies, threatening the safety of citizens’ lives and properties seriously. Existing literature primarily focuses on livelihood diversification, resilience, and vulnerability in flood-prone areas, with limited research systematically examining farmers’ livelihood risks and management strategies across multiple dimensions. To address this gap, this study advances the understanding of multidimensional livelihood risks by systematically identifying the key risk perceptions and management strategy choices of farmers, thereby providing empirical evidence essential for designing targeted interventions and sustainable adaptation policies in flood-prone regions. Specifically, this study employs an unordered multinomial logistic model to examine farmers’ risk management strategy choices, drawing on a field survey of 540 farmers from floodplain areas in Sichuan Province, China. The analysis systematically covers four livelihood risk dimensions (health, environmental, financial, social) and five management strategies (expansion, adjustment-oriented, contraction, aid-oriented, dependency-based). The results indicate that: (1) The most significant livelihood risk is environmental, and the most commonly selected risk management strategy is adjustment-oriented management; (2) When farmers face health risks, they tend to adopt dependency-based management strategy; in dealing with financial and social risks, farmers perceive no significant difference in the selection of the five management strategies. Accordingly, targeted strategies are proposed: insurance and information for environmental risks, medical security for health, employment training for social, and income diversification for financial risks. Full article

Background: Idiopathic slow-transit constipation (STC) is a clinically significant event of chronic constipation. Total colectomy with ileorectal anastomosis is considered the standard surgical option for medically refractory STC but is associated with relevant morbidity and long-term functional impairment. This study aimed to evaluate safety and functional outcomes of partial colectomy (segmental or subtotal resection) as a potential alternative in patients with limited colonic involvement. Methods: A retrospective observational single-center study was conducted on patients with STC refractory to medical and rehabilitative treatment (1998–2021). Five-year follow-up data were collected. Results: On a cohort of 76 patients, 10 (13.2%) underwent total colectomy, 63 (82.9%) segmental, and 3 (3.9%) subtotal resections (left hemicolectomy). No perioperative mortality occurred. Overall, 30-day morbidity was 25.0%, with major complications observed in 12.1% after partial colectomy. Median hospital stay was three days longer after total colectomy. Constipation recurred in 20.3% of patients, exclusively after segmental resection, at a median follow-up of 7.7 years. Constipation severity significantly decreased postoperatively (p < 0.001), with the mean Wexner score improving from 21.5 to 6.1 (p < 0.001). Rates of diarrhea and fecal incontinence were comparable between segmental and total colectomy. Quality-of-life significantly improved in more than 75% of cases across all SF-36 domains. Conclusions: Segmental colectomy may be a safe and effective alternative to total colectomy in patients with limited STC, potentially offering durable symptom relief and favorable quality-of-life outcomes. Full article

Despite recent advances, medical question answering systems still struggle with domain-specific reasoning and data efficiency. This paper presents Med-LLaMA3, a family of medical large language models developed by parameter-efficient fine-tuning of the LLaMA-3.1 (8 billion) and LLaMA-3.2 (1 and 3 billion) architectures using quantized low-rank adaptation (QLoRA) and low-rank adaptation (LoRA) with 4-bit quantization. Beyond model training, this work contributes the following: (1) a formalized dataset curation taxonomy (source type × clinical granularity × task format) with a source-category ablation confirming that the multi-source combination drives benchmark gains beyond any single category; (2) a systematic characterization of low-rank-adaptation rank-scaling behavior for the LLaMA-3 family in the medical domain (monotonic improvement up to rank 128, with no observed plateau); and (3) statistically validated comparisons using McNemar’s test and 95% bootstrap confidence intervals. We curated a medical instruction dataset of over 1.5 million samples spanning medical examinations, clinical dialogues, and biomedical literature. Our approach trains only ∼4% of the base model’s parameters and, consistent with prior studies of parameter-efficient methods in the medical domain, achieves performance comparable to full fine-tuning at a fraction of the memory footprint. Evaluated with five in-context examples per prompt, the 8-billion-parameter model attains a mean accuracy of 75.71% across the eight medical-domain subsets of the Massive Multitask Language Understanding benchmark; improvements over the unmodified LLaMA-3.1-8B-Instruct baseline are statistically significant on the medical multiple-choice benchmark MedMCQA and, after Bonferroni correction across the eight subsets, on three subsets (Clinical Knowledge, Medical Genetics, and Nutrition), with two further subsets being significant only before correction. A structured named-entity-recognition evaluation on 100 hospital discharge summaries (macro-averaged F1 0.94; dual-annotator agreement $\kappa =0.87$) provides complementary evidence of clinical-text utility. A safety mitigation pilot shows that context-disambiguation preprocessing reduces the highest-severity abbreviation-ambiguity error rate from 30% to 10% on a 30-case held-out set. These results show that parameter-efficient fine-tuning can deliver high-performance medical large language models while training only ∼4% of the model’s parameters and reducing memory use by roughly 75%, enabling development on low-cost consumer-grade hardware. Full article

Withania somnifera L. (Dunal), commonly known as Ashwagandha, is widely used in traditional medical systems, particularly Ayurveda, and is increasingly included in nutraceuticals and dietary supplements. This narrative review summarizes and critically discusses the literature published from 2015 to 2026 on WS, with a focus on CNS-related outcomes, proposed mechanisms, extract standardization, dosage, and safety considerations. Evidence from preclinical research and human studies suggests that WS preparations may influence stress-related and sleep-related outcomes and support neuroprotective pathways suggesting a significant role in nutraceuticals; however, the overall strength of evidence varies across indications and products, and heterogeneity in extract composition and study design limits firm conclusions. Further well-designed, adequately powered clinical trials using standardized preparations are needed to clarify efficacy, mechanisms of action, and long-term safety. Full article

Generative Artificial Intelligence (GenAI) in cardiac surgery refers to the integration of advanced computational models, such as Large Language Models (LLMs), to automate and enhance clinical decision-making, preoperative risk assessment, and surgical education. In the context of surgical training, it functions as a personalized pedagogical tool that supports various learning activities, ranging from information acquisition and clinical inquiry to procedural practice, while requiring rigorous human oversight to ensure patient safety and clinical accuracy. (1) Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into health professions education, offering new opportunities for learning; however, its specific application and pedagogical mapping in high-stakes fields such as cardiac surgery remain underexplored. This systematic review investigates how GenAI is utilized in cardiac surgery and surgical education, aligning these uses with Laurillard’s six learning types. (2) Methods: Following the PRISMA 2020 guidelines, we searched the Web of Science Core Collection for studies on GenAI in cardiac surgery, resulting in 42 studies that met the inclusion criteria. Study quality was appraised using the Medical Education Research Study Quality Instrument (MERSQI). (3) Results: GenAI applications most frequently supported clinical inquiry (93.8%) and practice (68.8%), demonstrating expanding efficiency across commercial and open-source models (including ChatGPT-4o, Gemini AI, and emerging reasoning architectures such as DeepSeek) for knowledge acquisition and medical production. While it significantly improves individualized learning and preoperative assessment workflows, its practical role in Discussion and Collaboration remains heavily underutilized, highlighting a distinct shift toward individualized solo professional workflows. (4) Conclusions: GenAI provides a transformative and scalable approach to cardiac surgical training by offering personalized and accessible knowledge retrieval. However, clinical educators and governance bodies must deliberately balance these immediate productivity benefits with long-term concerns regarding structural “hallucinations,” data verifiability, and the preservation of collaborative competencies within modern multidisciplinary Heart Teams. Full article

The gut microbiota is increasingly examined as a therapeutic target because it contributes to epithelial barrier integrity, microbial metabolite production, bile acid transformation, immune regulation, and communication between the gut and distant organs. This structured narrative review synthesizes evidence on microbiota involvement in metabolic, gastrointestinal, hepatic, cancer, and neuroimmune conditions, including MASLD/MASH, inflammatory bowel disease, irritable bowel syndrome, obesity, type 2 diabetes, hypertension, colorectal cancer, Parkinson’s disease, and autism spectrum disorder. Across these conditions, microbiome findings are biologically plausible but heterogeneous. Many associations are shaped by diet, geography, medication exposure, host genetics, disease stage, sampling methods, and analytical pipelines. Microbial alterations should therefore be interpreted as context-dependent signals and candidate modifiers rather than universal causal markers. Conventional microbiota targeted strategies include diet, physical activity, prebiotics, probiotics, synbiotics, postbiotics, and fecal microbiota transplantation. These approaches are clinically familiar, but their effects are often broad, host specific, strain dependent, and difficult to assign to one mechanism. Fecal microbiota transplantation has the clearest clinical role in recurrent Clostridioides difficile infection, while evidence for most other indications remains inconsistent. Engineered microbial therapeutics offer greater experimental precision through signal sensing, payload delivery, metabolic modulation, and genetic circuit design. However, most evidence remains preclinical or early translational. Progress requires stronger human trials, standardized methods, mechanistic validation, safety monitoring, ecological containment, transparent reporting, and proportionate regulation. Full article