Search Results (11)

Background: Severe obesity is associated with chronic low-grade inflammation, dysglycemia, and higher periodontitis risk. Sleeve gastrectomy (SG) is now a dominant bariatric procedure and reliably improves weight and metabolic status yet reported oral and periodontal trajectories after surgery remain heterogeneous. Objective: To synthesize SG-centered evidence on periodontal outcomes, oral and gut microbiome remodeling, and mechanistic pathways that may link postoperative physiology to the gut–oral axis. Methods: We conducted a structured narrative review guided by SANRA principles using targeted searches of PubMed/MEDLINE, Web of Science, Scopus, and Embase, complemented by citation chaining of key reviews and mechanistic anchor papers; evidence was organized into clinical, oral microbiome, gut microbiome, and mechanistic gut–oral axis streams and interpreted with a pragmatic evidence hierarchy. Results: Small prospective SG cohorts suggest bleeding on probing (BOP), gingival indices, and sometimes probing depth (PD) may improve in some patients, particularly alongside weight loss, improved glycemic control, and lower systemic inflammatory burden, whereas clinical attachment level (CAL) and longer-term structural trajectories remain mixed; mixed-procedure syntheses also report early deterioration in some settings. Oral microbiome findings after bariatric surgery are site- and time-dependent, and salivary signals do not necessarily mirror subgingival plaque, whereas gut microbiome remodeling and bile acid signaling changes are more consistently reported and provide plausible but indirect mediator candidates. At the same time, reflux, vomiting, salivary changes, diet patterning, medications, and periodontal care can modify or counteract potential periodontal benefits and may increase competing risks such as caries or erosive tooth wear. Conclusions: The SG–gut–oral axis-periodontal pathway is a biologically plausible working hypothesis rather than a proven causal pathway in humans. The present evidence for any periodontal benefit relies mainly on small observational cohorts and is most credibly demonstrated for inflammatory, not structural, endpoints. Full article

Background/Objectives: Out-of-hospital cardiac arrest (OHCA) outcomes remain poor and vary widely across communities with socioeconomic deprivation. This study examines whether delays in emergency medical services (EMS) activation, the earliest link in the Chain of Survival, mediate the association between regional deprivation and OHCA outcomes, and whether this effect is modified by bystander cardiopulmonary resuscitation (CPR) status. Methods: We analyzed adult patients (aged 18–80 years) with witnessed, EMS-treated OHCA of presumed cardiac etiology from the Korean nationwide OHCA registry (2015–2022). Regional deprivation was defined by the Regional Deprivation Index and dichotomized into deprived (top 20%) vs. non-deprived areas. Timely EMS activation, defined as collapse to EMS activation, was measured as an awareness time interval (ATI) < 5 min. Outcomes were good neurological recovery (CPC 1–2) and survival to discharge. Causal mediation analysis within the counterfactual framework quantified the proportion of the association mediated by timely EMS activation, with stratification by bystander CPR status. Results: Among 43,032 patients, 6.1% resided in deprived areas. Deprived areas had lower bystander CPR (22.6% vs. 36.3%) and timely EMS activation (67.8% vs. 75.6%) (p < 0.05 for all). Regional deprivation was associated with poorer outcomes (good neurological prognosis: aOR 0.46, 95% CI 0.39–0.55; survival: aOR 0.65, 95% CI 0.57–0.73). Mediation analysis showed that ATI < 5 min accounted for 3.7% of the total deprivation effect on good neurological outcome and 7.9% on survival, with stronger mediation among patients receiving bystander CPR (7.9% and 14.7%, respectively). Conclusions: Regional deprivation is significantly associated with poorer OHCA outcomes, partly mediated by delays in EMS activation, particularly among patients who received bystander CPR. Interventions to enhance early recognition, rapid EMS activation, and bystander CPR in deprived communities are critical to improving survival equity after OHCA. Full article

Variants in ELAC2, a gene encoding the mitochondrial RNase Z enzyme essential for mitochondrial tRNA processing, have been associated with severe pediatric-onset mitochondrial dysfunction, primarily presenting with developmental delay, hypertrophic cardiomyopathy (HCM), and lactic-acidosis. We hereby report the case of a 25-year-old young woman presenting with dilated cardiomyopathy (DCM) and peripheral sensorimotor polyneuropathy, harboring a homozygous variant in ELAC2. The same variant has been reported only once so far in a case of severe infantile-onset form of HCM and mitochondrial respiratory chain dysfunction, with in vitro data showing a moderate reduction in the RNase Z activity and supporting the current classification as C4 according to the American College of Medical Genetics (ACMG) criteria (PS3, PM2, PM3, PP4). Our extensive clinical, imaging, histological, and genetic investigations support a causal link between the identified variant and the patient’s phenotype, despite the fact that the latter might be considered atypical according to the current state of knowledge. A detailed review of the existing literature on ELAC2-related disease is also provided, highlighting the molecular mechanisms underlying tRNA maturation, mitochondrial dysfunction, and the variable phenotypic expression. Our case further expands the clinical spectrum of ELAC2-related cardiomyopathies to include a relatively late onset in young adulthood and underscores the importance of comprehensive genetic testing in unexplained cardiomyopathies with multisystem involvement. Full article

Background/Objectives: Studies have reported an increased risk of type 2 diabetes among people with higher protein intake. Moreover, branched-chain amino acids (BCAA) are reported to be positively associated with insulin resistance (IR). However, it is not understood whether elevated levels of BCAA are causal to IR development, or if higher BCAA are a marker of IR. The objective of this study was to examine the effects of long-term protein and carbohydrate supplementation on plasma BCAA levels, and the relationship between plasma BCAA and IR in postmenopausal women. Methods: Stored samples and data from 84 postmenopausal women who participated in a protein supplementation trial (SPOON) were included. Exclusion criteria consisted of protein intakes less than 0.6 g/kg or greater than 1.0 g/kg, a body mass index (BMI) greater than 32 kg/m² or less than 19 kg/m² diseases, and conditions and medications known to impact musculoskeletal health. Subjects were randomized to a whey protein (PRO: n = 38) or maltodextrin supplement (CHO: n = 46) for 18 months. Plasma BCAA, homeostatic model assessment of insulin resistance (HOMA-IR) and body composition were analyzed at baseline and 18 months. Results: At baseline, there were no significant associations between plasma BCAA and IR. There were also no significant changes in plasma BCAA or IR by study arm. However, there was a significant positive association between plasma BCAA and IR in both groups at 18 months (CHO: r = 0.35, p = 0.02; PRO: r = 0.35, p = 0.03). Conclusions: Findings from this study warrant future research to examine other diet and lifestyle factors that may mediate the relationship between circulating BCAA and IR in postmenopausal women. Full article

The proliferation of electronic health records (EHRs) and advances in deep learning have enabled personalized drug combination recommendations. However, traditional deep learning models often lack the contextual understanding and medical knowledge integration necessary for accurate predictions. While large language model (LLM)-based approaches address some of these challenges, they still fall short in incorporating critical medical knowledge, addressing comprehensive safety constraints such as multi-disease drug contraindications (MDCs), and providing sufficient interpretability of the causal mechanisms behind their outputs. To overcome these limitations, we propose KELLM, a knowledge-enhanced LLM framework for drug recommendations. By linking medical entities in EHRs to an external medical knowledge graph, inputs are enriched with causal chains, enhancing both prediction accuracy and interpretability. Additionally, we introduce a fine-tuned label-wise LLaMA model designed for multi-label classification, which incorporates safety considerations such as drug-drug interactions (DDIs) and MDCs to ensure clinically accurate and safe recommendations. Experimental results show that KELLM achieves state-of-the-art performance in effectiveness and safety metrics, while also providing evidence-based insights through causal chains that clarify its reasoning process. This establishes a new benchmark for trustworthy, interpretable drug combination recommendations. Full article

Torque teno virus (TTV) is emerging as a potential marker for monitoring immune status. In transplant recipients who are immunosuppressed, higher TTV DNA loads are observed than in healthy individuals. TTV load measurement may aid in optimizing immunosuppressive medication dosing in solid organ transplant recipients. Additionally, there is a growing interest in the role of HDL particles in immune function; therefore, assessment of both HDL concentrations and TTV load may be of interest in transplant recipients. The objective of this study was to analyze TTV loads and HDL parameters in serum samples collected at least one year post-transplantation from 656 stable outpatient kidney transplant recipients (KTRs), enrolled in the TransplantLines Food and Nutrition Cohort (Groningen, the Netherlands). Plasma HDL particles and subfractions were measured using nuclear magnetic resonance spectroscopy. Serum TTV load was measured using a quantitative real-time polymerase chain reaction. Associations between HDL parameters and TTV load were examined using univariable and multivariable linear regression. The median age was 54.6 [IQR: 44.6 to 63.1] years, 43.3% were female, the mean eGFR was 52.5 (±20.6) mL/min/1.73 m² and the median allograft vintage was 5.4 [IQR: 2.0 to 12.0] years. A total of 539 participants (82.2%) had a detectable TTV load with a mean TTV load of 3.04 (±1.53) log10 copies/mL, the mean total HDL particle concentration was 19.7 (±3.4) μmol/L, and the mean HDL size was 9.1 (±0.5) nm. The univariable linear regression revealed a negative association between total HDL particle concentration and TTV load (st.β = −0.17, 95% CI st.β: −0.26 to −0.09, p < 0.001). An effect modification of smoking behavior influencing the association between HDL particle concentration and TTV load was observed (P_interaction = 0.024). After adjustment for age, sex, alcohol intake, hemoglobin, eGFR, donor age, allograft vintage and the use of calcineurin inhibitors, the negative association between HDL particle concentration and TTV load remained statistically significant in the non-smoking population (st.β = −0.14, 95% CI st.β: −0.23 to −0.04, p = 0.006). Furthermore, an association between small HDL particle concentration and TTV load was found (st.β = −0.12, 95% CI st.β: −0.22 to −0.02, p = 0.017). Higher HDL particle concentrations were associated with a lower TTV load in kidney transplant recipients, potentially indicative of a higher immune function. Interventional studies are needed to provide causal evidence on the effects of HDL on the immune system. Full article

The causes of sore throat are complex. It can be caused by diseases of the pharynx, adjacent organs of the pharynx, or even systemic diseases. Therefore, a lack of medical knowledge and experience may cause misdiagnoses or missed diagnoses in sore throat diagnoses, especially for general practitioners in primary hospitals. This study aims to develop a computer-aided diagnostic system to assist clinicians in the differential diagnoses of sore throat. The computer-aided system is developed based on the Dynamic Uncertain Causality Graph (DUCG) theory. We cooperated with medical specialists to establish a sore throat DUCG model as the diagnostic knowledge base. The construction of the model integrates epidemiological data, knowledge, and clinical experience of medical specialists. The chain reasoning algorithm of the DUCG is used for the differential diagnoses of sore throat. The system can diagnose 27 sore throat-related diseases. The model builder initially tests it with 81 cases, and all cases are correctly diagnosed. Then the system is verified by the third-party hospital, and the diagnostic accuracy is 98%. Now, the system has been applied in hundreds of primary hospitals in Jiaozhou City, China, and the degree of recognition for doctors to the diagnostic results of the system is more than 99.9%. It is feasible to use DUCG for the differential diagnoses of sore throat, which can assist primary doctors in clinical diagnoses and the diagnostic results are acceptable to clinicians. Full article

Marine microplastic pollution (MMP) is becoming one of the most pressing environmental problems facing humanity today. The novel coronavirus epidemic has raised the issue of environmental contamination caused by large-scale improper disposal of medical waste such as disposable masks (DMs). To assess the impact of MMP caused by DMs and to seek solutions for the prevention and control of MMP, this study uses the Driving force-Pressure-State-Impact-Response (DPSIR) framework to establish a causal chain of MMP caused by DMs. The conclusion shows that the novel coronavirus epidemic has led to a surge in the use of DMs, which has brought pressure on resource constraints and environmental pollution at the same time. Improperly DMs enter the environment and eventually transform into MMP, which not only endangers the marine ecological system but also poses potential human health risks as well as economic and social hazards. In addition, further research on environmentally friendly masks (cloth masks and biodegradable masks) is essential to mitigate the environmental damage caused by the large-scale global use of DMs. This study provides a scientific and theoretical basis for the assessment of MMP from discarded DMs, and the findings of this study will provide a reference for the formulation of relevant policies. Full article

Numerous studies show a modification of the gut microbiota in patients with obesity or diabetes. Animal studies have also shown a causal role of gut microbiota in liver metabolic disorders including steatosis whereas the human situation is less clear. Patients with metabolic dysfunction associated fatty liver disease (MAFLD) also have a modification in their gut microbiota composition but the changes are not fully characterized. The absence of consensus on a precise signature is probably due to disease heterogeneity, possible concomitant medications and different selection or evaluation criteria. The most consistent changes were increased relative abundance of Proteobacteria, Enterobacteriaceae and Escherichia species and decreased abundance of Coprococcus and Eubacterium. Possible mechanisms linking the microbiota and MAFLD are increased intestinal permeability with translocation of microbial products into the portal circulation, but also changes in the bile acids and production of microbial metabolites such as ethanol, short chain fatty acids and amino acid derivatives able to modulate liver metabolism and inflammation. Several interventional studies exist that attempt to modulate liver disease by administering antibiotics, probiotics, prebiotics, synbiotics, postbiotics or fecal transplantation. In conclusion, there are both gaps and hopes concerning the interest of gut microbiome evaluation for diagnosis purposes of MAFLD and for new therapeutic developments that are often tested on small size cohorts. Full article

The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D. Full article

Hepatitis B is a widespread epidemic in the world, but so far no single drug has been shown to kill or eliminate the Hepatitis B virus and heal people with chronic Hepatitis B virus infection. Based on comprehensive investigations to relevant characteristics of Hepatitis B, a diagnostic modelling and reasoning methodology using Dynamic Uncertain Causality Graph is proposed. The symptoms, physical signs, examinations results, medical histories, etiology, pathogenesis and other factors were included in the diagnosis model. In order to reduce the difficulty of building the model, a modular modeling scheme is proposed, which provides multi-perspectives and arbitrary granularity for the expression of disease causality. The chain reasoning algorithm and weighted logic operation mechanism are introduced to ensure the correctness and effectiveness of diagnostic reasoning under incomplete and uncertain information. In addition, the causal view of the potential interactions between diseases and symptoms visually shows the reasoning process in a graphical way. In the relevant model, the model of the diagnostic process and the model of the therapeutic process are symmetrical. The results show that, even with incomplete observations, the proposed methodology achieves encouraging diagnostic accuracy and effectiveness, providing a promising assistance tool for physicians in the diagnosis of Hepatitis B. Full article