Search Results (1,697)

Background: Manganese (Mn) exposure is common in welding and metal-processing occupations and has been implicated in both thyroid disruption and endothelial dysfunction through oxidative and nitric-oxide–related pathways. However, endocrine and vascular biomarkers have rarely been examined together in occupational settings. Methods: In this cross-sectional study, 95 Mn-exposed workers and 95 non-exposed controls were evaluated. Whole-blood Mn, triiodothyronine (T3), thyroxine (T4), thyroid-stimulating hormone (TSH), asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), arginine and citrulline were measured using validated Inductively Coupled Plasma—Mass Spectrometer and chemiluminescent immunoassays. Group differences were assessed using independent samples t-tests, and exposure–biomarker associations were evaluated using Pearson correlations (p < 0.05). Results: Mn-exposed workers had significantly higher blood Mn levels than controls (19.82 ± 4.54 vs. 10.22 ± 3.07 µg/L; p < 0.001). Thyroid hormones (T3, T4, and TSH) were significantly lower among Mn workers, representing a non-classical hormonal pattern, including T3 (2.47 ± 0.31 vs. 3.14 ± 0.42 ng/L; p < 0.001), T4 (1.02 ± 0.13 vs. 1.21 ± 0.18 ng/L; p < 0.001), and TSH (1.75 ± 0.53 vs. 2.88 ± 0.37 mIU/L; p < 0.001). Endothelial biomarkers also differed: ADMA (0.26 ± 0.14 vs. 0.19 ± 0.08 µmol/L; p < 0.001) and SDMA (0.24 ± 0.06 vs. 0.20 ± 0.03 µmol/L; p < 0.001) were higher, while citrulline was lower (18.77 ± 10.23 vs. 22.82 ± 6.70 µmol/L; p = 0.002). In Mn workers, blood Mn showed negative correlations with T3 (r = –0.535, p < 0.01), T4 (r = –0.331, p < 0.01), and TSH (r = –0.652, p < 0.01), and positive correlations with ADMA (r = 0.205, p < 0.05) and SDMA (r = 0.193, p < 0.05). Conclusions: These findings indicate measurable differences in thyroid hormones and dimethylarginine-related endothelial markers among Mn-exposed workers. While the cross-sectional design precludes causal inference, the combined pattern suggests a possible unusual biological response involving both endocrine regulation and nitric-oxide–related pathways. Further longitudinal studies incorporating oxidative stress markers, co-exposure assessment, and functional endothelial testing are needed to clarify the biological relevance of these associations. Full article

Background/objectives: Psoriatic disease (PsD) encompasses psoriasis (PsO) and psoriatic arthritis (PsA) and is associated with heterogeneous cardiometabolic risk. Integrating immunogenetic markers such as HLA-Cw6 into data-driven analyses may refine phenotyping and uncover clinically meaningful endotypes. We aimed to identify cardiometabolic phenotypes across PsD, integrating HLA-Cw6 and exploring disease-specific heterogeneity and predictors of high-risk profiles. Methods: In a cross-sectional study of 572 PsD patients (401 PsO, 171 PsA), eight demographic and clinical variables, including HLA-Cw6, were entered into k-means clustering (k = 4). Cardiometabolic risk factors were profiled post hoc. Cluster validity was assessed by Gaussian Mixture Models and principal component analysis (PCA). Stratified analyses (k = 3) were conducted separately for PsO and PsA. Predictors of the high-risk phenotype were examined using bootstrap-resampled logistic regression. Results: Four cardiometabolic phenotypes were identified, ranging from younger patients with active PsO and low cardiometabolic burden to a small, high-risk subgroup (~6%) combining older age, universal cardiovascular disease, and a clustering of hypertension, diabetes, and dyslipidemia. Disease-stratified analyses showed that high-risk phenotypes were present in both PsO and PsA. In stratified analyses, HLA-Cw6 showed opposite associations—enriched in high-risk PsO (OR 2.0, 95% CI 1.3–3.1) but depleted in high-risk PsA (OR 0.24, 95% CI 0.11–0.52). Conclusions: Incorporating HLA-Cw6 into clustering identified reproducible cardiometabolic phenotypes with distinct genetic signatures. The inverse HLA-Cw6 risk patterns in PsO and PsA suggest disease-specific patterns that may have differing cardiometabolic implications, which should be tested in longitudinal studies. Full article

Background/Objectives: Reconstructive urology is critically underrepresented in global surgery initiatives, despite its essential role in managing congenital and acquired urogenital conditions. In response, a multinational Global Surgery Collaborative was launched in 2022 by a faculty from the University of Toronto, aiming to enhance reconstructive urology capacity in the Philippines, among other low- to low-middle-income countries through longitudinal mentorship and skills transfer. This report presents early experience from 2022 to 2024. Methods: This collaboration delivered annual in-person surgical missions from 2022 to 2024 at two major Philippine healthcare institutions. Training focused on pediatric and adult reconstructive urologic procedures. Local mentees participated in structured preoperative planning, intraoperative teaching, and postoperative debriefing. We conducted a prospective service evaluation comprising a prospective registry of consecutive cases and paired pre/post trainee surveys. Data were collected on patient demographics and surgical metrics. Primary clinical endpoints included operative time, length of stay, and complications (Clavien–Dindo), with standardized follow-up windows. Mentee educational outcomes were assessed through pre- and post-training trainee-reported (Likert) measures, evaluating comfort and technical understanding. Statistical analysis used the Wilcoxon signed-rank test to assess changes. Results: Over three years, 33 surgical cases were performed with 45 surgical resident mentees (Post-graduate year (PGY)4–PGY6) engaged. The median patient age was 23 (inter-quartile range [IQR] 12.5–41.5) years, with 33.3% pediatric and 84.8% of cases classified as major. The complication rate was 15.1%, with only one major event (3%). Across 45 mentees, comfort increased from a median 4.0 (IQR 2.5–5.0) to 7.0 (5.5–8.0) and technique understanding from 5.0 (4.0–6.5) to 9.0 (8.0–10.0), with large Wilcoxon effects (r = 0.877 and r = 0.875; both p < 0.001). Year-by-year analyses showed the same pattern with large effects. Conclusions: In this early three-year experience (33 cases, 84.8% major), this multi-institutional collaboration longitudinal mentorship model was feasible and safe, and was associated with significant trainee-reported improvements in comfort and technical understanding. This demonstrates a replicable model for global surgery in reconstructive urology, successfully enhancing surgical skills and fostering sustainable capacity in low- and middle-income countries (LMIC) settings. Full article

Background: Multiple sclerosis (MS) is a chronic, inflammatory, and demyelinating disease of the central nervous system that predominantly affects young adults, particularly women, and is associated with progressive disability and a wide range of symptoms that impair functionality and quality of life. Recent research suggests that diet, especially its inflammatory potential, may influence the clinical course of the disease. We hypothesize that patients following a proinflammatory dietary pattern will exhibit poorer functional outcomes than those following an anti-inflammatory diet. Methods: An observational preliminary study was conducted, including 19 patients. Dietary inflammatory potential was assessed using the Dietary Inflammatory Index (DII), while functional status was measured with the Functional Assessment of MS (FAMS) scale. Results: Participants were divided into two groups according to their DII score: a group following a more pro-inflammatory diet (n = 10; 80% female; mean age 49.60 ± 10.63 years) and a group following a more anti-inflammatory diet (n = 9; 44.4% female; mean age 49.00 ± 10.79 years). The results show that patients with a proinflammatory dietary profile reported a higher symptom burden (FAMS symptoms score 20.70 ± 5.48 vs. 14.44 ± 7.05, p = 0.044), including greater fatigue as well as musculoskeletal and gastrointestinal complaints. In contrast, patients with an anti-inflammatory dietary profile reported fewer symptoms, greater energy and vitality, and higher intake of anti-inflammatory nutrients such as fiber, magnesium, and vitamin B6. No other significant between-group differences were observed. Conclusions: These findings suggest that dietary interventions aimed at reducing inflammation may improve functionality and quality of life in persons with MS. However, given the limited sample size, larger multicenter longitudinal studies are required to confirm these results. The findings of this study may provide preliminary evidence to inform future research. Full article

Background and Objectives: To evaluate whether regional climatic factors, particularly relative humidity (RH) and temperature, are associated with geographic variation in dry eye disease (DED) prevalence in Argentina. Materials and Methods: A secondary analysis of a nationwide voluntary online cross-sectional survey of DED in Argentina (April 2022; non-probability sample; n = 10,812) was performed. Participants were assigned to one of five standard geopolitical regions. DED prevalence (%) was estimated at regional levels. Monthly mean, maximum, and minimum temperature and RH for April 2022 were obtained from the National Meteorological Service; for each province/region, data corresponded to the principal synoptic station in the provincial capital. Because the survey and climate data were restricted to April, seasonal effects could not be evaluated. Region-level analyses of climate–DED patterns were prespecified as exploratory and descriptive given the small number of regional units (n = 5). Weighted descriptive models were used to compare RH–DED patterns while accounting for regional age and sex distributions. Results: Regional DED prevalence ranged from 37.7% (Northwest) to 49.8% (Cuyo). RH showed a clear inverse pattern with regional DED prevalence, whereas temperature showed no consistent relationship. Weighted descriptive models showed a similar qualitative RH–DED pattern, although residual confounding cannot be excluded. Conclusions: DED prevalence in Argentina shows geographic variability and a descriptive inverse pattern with ambient RH. These ecological findings are hypothesis-generating and must be interpreted cautiously due to sampling bias. Future seasonal, longitudinal, or individual-level studies are needed to confirm and quantify these preliminary signals. Full article

This paper addresses the issue of stress redistribution in surrounding soil during the construction of shallow-buried, large-section loess tunnels. Using the Luochuan Tunnel as a case study, we employ the FLAC 3D numerical simulation method to investigate the effects of advanced pipe roof support on the stability of the surrounding soil. The results demonstrate that advanced pipe umbrella reduces the stress release amplitude at the vault by 50% compared to the unsupported condition, due to a “pre-support-load bearing mechanism”, while promoting orderly stress recovery. The “longitudinal beam effect” and “transverse arch effect” of soils effectively suppress the plastic zone area of the surrounding soil from 413.3 m² (unsupported) to 95.0 m², achieving a reduction exceeding 77%. Furthermore, the pipe umbrella support facilitates the formation of a more efficient “active soil arch”, which exhibits distinct symmetrical characteristics. The arch’s stress distribution and spatial structure both follow symmetrical patterns, significantly enhancing the self-stabilizing capacity of the surrounding soil. As a result, the height of the stress release zone at the tunnel excavation face and the surrounding soil stability areas is reduced by 45.9% and 63.3%, respectively, compared to the unsupported condition. This study also establishes a Pasternak elastic foundation beam model that accounts for the spatiotemporal effects of support, elucidating the mechanism of pipe umbrella support and providing a theoretical foundation for the design and construction risk control of shallow large-section loess tunnels. Full article

Background/Objectives: Endocrine autoimmune diseases, including autoimmune thyroid, pituitary, parathyroid, adrenal, and gonadal diseases, result from complex interactions between genetic susceptibility and environmental triggers. Advances in genomics and epigenomics have provided novel insights into the molecular pathways leading to immune dysregulation and endocrine tissue destruction. This review summarizes recent progress in understanding the genetic and epigenetic bases, emphasizing shared and disease-specific mechanisms that contribute to autoimmunity and endocrine dysfunction. Methods: A comprehensive literature search was performed in PubMed, Scopus, and Web of Science up to August 2025, focusing on genome-wide association studies (GWAS), next-generation sequencing, and epigenetic profiling (DNA methylation, histone modification, and non-coding RNA regulation). Results: More than 60 susceptibility loci have been identified across endocrine autoimmune diseases (EADs), including key genes in immune tolerance (HLA, CTLA4, PTPN22) and endocrine-specific pathways. Epigenetic studies reveal that altered DNA methylation and histone acetylation patterns in immune and endocrine cells modulate gene expression without changing the DNA sequence, linking environmental exposures to disease onset. Dysregulated microRNAs further influence immune signaling and cytokine networks. Conclusions: Genetic and epigenetic discoveries highlight the multifactorial nature of EADs and reveal potential biomarkers for early detection and targets for precision immunotherapy. Future research integrating multi-omics and longitudinal analyses will be crucial to unravel causal mechanisms and develop personalized preventive strategies. Full article

This study examines the economic and humanistic burden associated with multimorbidity among adults in the United States. Using data from the 2019–2022 Medical Expenditure Panel Survey (MEPS), we identified individuals with two or more chronic conditions and assessed trends in healthcare expenditures, out-of-pocket costs, inpatient stays, and health-related quality of life (HRQL). Weighted analyses were conducted to estimate national patterns and annual changes across survey years. Outcomes were analyzed using generalized estimating equation (GEE) models with AR(1) working correlation to compare adjusted mean total and out-of-pocket expenditures, inpatient utilization, and mental and physical HRQL across multimorbidity profiles while controlling for sociodemographic and health factors. Findings showed that multimorbidity was associated with substantial economic burden, reflected in higher healthcare costs and out-of-pocket spending over time. HRQL consistently declined throughout the study years, highlighting the growing humanistic toll of chronic disease clustering. These findings provide longitudinal evidence of persistent disparities associated with multimorbidity and may inform future research and health system planning strategies. The results provide timely evidence for health policymakers and practitioners seeking to improve health system efficiency and equity in managing multimorbidity. Full article

Memory constitutes a fundamental cognitive domain, and converging evidence suggests that its dysfunction represents a prominent, though not exclusive, transdiagnostic dimension across major psychiatric disorders. This review aimed to integrate neurobiological, cognitive, and clinical evidence on domain-specific memory impairments in mood, anxiety, obsessive–compulsive, post-traumatic stress, and psychotic disorders. A comprehensive search was conducted on PubMed, Scopus, and Web of Science up to November 2025 for peer-reviewed studies examining short-term, working, long-term, episodic, semantic, and prospective memory, prioritizing both landmark and recent contributions. Two recurrent transdiagnostic patterns emerged: (i) consistent impairments in working-memory control, and (ii) reduced episodic/autobiographical specificity, while procedural memory appeared relatively preserved. Disorder-specific profiles include overgeneral autobiographical memory in major depression, enduring working and episodic deficits in bipolar disorder, variable impairments in anxiety disorders, functional rather than structural memory inefficiencies in obsessive–compulsive disorder, broad mnemonic disorganization in post-traumatic stress disorder, and pervasive working and episodic deficits in schizophrenia and related psychoses. Across conditions, converging neurobiological data implicate fronto-hippocampal dysconnectivity, altered plasticity, and impaired consolidation processes. Unlike previous reviews, this work syntetisizes evidence across multiple memory systems and across major psychiatric categories, linking neurobiological mechanisms with cognitive and clinical manifestations to support a dimensional, transdiagnostic interpretation of memory dysfunction. These findings could suggest that memory dysfunction represents a recurrent and clinically relevant dimension across psychiatric conditions, warranting further mechanistic and longitudinal investigation. Full article

Post-Traumatic Stress Disorder (PTSD) is consistently linked to multidimensional working memory (WM) impairments, encompassing deficits in sustained attention, verbal and visuospatial processing, and executive control, with inhibitory dysfunction emerging as a key feature. This scoping review synthesizes evidence from 39 studies examining neurobiological mechanisms, trauma-related factors, genetic and hormonal influences, gender differences, and task-specific variability. Findings indicated that PTSD is associated with altered activation and connectivity in the prefrontal cortex, hippocampus, and related neural networks, often resulting in compensatory but inefficient recruitment patterns. Emotional distraction and comorbidities such as depression, alcohol use, and traumatic brain injury can exacerbate cognitive deficits. Performance impairments are evident across both emotional and neutral WM tasks, with visuospatial and updating processes being particularly vulnerable. Risk factors include chronic trauma exposure, older age, APOE ε4 allele, and the BDNF Val66Met (rs6265) polymorphism, while modulators such as oxytocin, cortisol, and physical activity show potential cognitive benefits under specific conditions. Methodological heterogeneity and limited longitudinal data restrict generalizability. These findings underscore the importance of early screening, targeted cognitive interventions, and inclusion of underrepresented populations to refine prevention and treatment strategies for PTSD-related WM deficits. Full article

Purpose: This study aimed to investigate the associations between serum per- and polyfluoroalkyl substances (PFAS) and reproductive hormones, including follicle-stimulating hormone (FSH), anti-Müllerian hormone (AMH), estradiol, and progesterone, in U.S. women. Approach and Results: We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2017–2018. The study included 612 women aged ≥18 years with available PFAS and sex hormone measurements. Serum concentrations of four major PFASs (linear perfluorooctanoic acid [n-PFOA], perfluorooctane sulfonic acid [PFOS], perfluorononanoic acid [PFNA], and perfluorohexane sulfonic acid [PFHxS]) were analyzed, along with serum levels of FSH, AMH, estradiol, and progesterone measured by isotope dilution liquid chromatography–tandem mass spectrometry. Higher serum PFAS concentrations were associated with increased FSH and decreased AMH, estradiol, and progesterone. For example, each interquartile range (IQR) increase in ln-PFNA was associated with a 42.0% increase in ln-FSH (p = 0.01) and 32.2% lower ln-AMH (p < 0.001), 33.0% lower ln-estradiol (p = 0.004), and 40.9% lower ln-progesterone (p = 0.02). A PFAS exposure index was related to higher FSH and lower AMH, estradiol, and progesterone, with stronger effects in premenopausal women. Conclusions: PFAS exposure was linked to broad endocrine disruption in women, with consistent alterations across gonadotropins and sex steroids. These findings suggest that PFAS exposure was associated with hormonal patterns consistent with diminished ovarian reserve and potential changes in reproductive function, underscoring the need for longitudinal studies and regulatory actions to mitigate exposure. Full article

The gut microbiota plays an essential role in human health through its contributions to immune regulation, metabolism, pathogen defence and disease susceptibility. Despite this significance, most gut microbiome research remains disproportionately focused on high-income countries, resulting in a limited and underrepresented view of global microbial diversity. This bias is evident in Africa, where populations, including those in South Africa, show unique combinations of genetic variation, dietary patterns and environmental exposures that are insufficiently captured in current datasets but offer opportunities to uncover novel insights into microbial evolution and its influences on health across diverse settings. In response to this gap, this review synthesises global patterns in gut microbiota composition and diversity while situating South African findings within this broader context. We examine evidence across microbial domains, including bacteria, fungi, viruses, archaea, protozoa and helminths, and highlight the impact of dietary transitions and environmental exposures on microbial community structure. Although still emerging, research on the gut microbiome of South African populations consistently reports contrasts between rural and urban populations, with rural groups enriched in fibre-fermenting and anti-inflammatory taxa, whereas urban communities often exhibit reduced diversity and features of dysbiosis linked to Westernisation. However, limited sample sizes, heterogeneous methodologies and absence of multi-omic approaches constrain robust interpretation. These lacunae in current knowledge emphasise the urgent need for large-scale, longitudinal studies that reflect South Africa’s demographic and geographic diversity. Strengthening this evidence will not only help identify microbial signatures linked to modifiable lifestyle factors but will also guide nutrition, prevention and screening programmes to improve health in African populations. Full article

Background: Ovarian cancer is characterized by high mortality rates, primarily due to diagnosis at late stages. Current biomarkers, such as CA125, have demonstrated limited efficacy for early detection. While high-dimensional proteomics offers a more comprehensive view of systemic biology, the analysis of such data, where the number of features far exceeds the number of samples, presents a significant computational challenge. Methods: This study utilized a nested case–control cohort of longitudinal pre-diagnostic serum samples from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) profiled for eight candidate ovarian cancer biomarkers (CA125, HE4, PEBP4, CHI3L1, FSTL1, AGR2, SLPI, DNAH17) and 92 additional cancer-associated proteins from the Olink Oncology II panel. We employed a Synolitic Graph Neural Network framework that transforms high-dimensional multi-protein data into sample-specific, interconnected graphs using a synolitic network approach. These graphs, which encode the relational patterns between proteins, were then used to train Graph Neural Network (GNN) models for classification. Performance of the network approach was evaluated together with conventional machine learning approaches via 5-fold cross-validation on samples collected within one year of diagnosis and a separate holdout set of samples collected one to two years prior to diagnosis. Results: In samples collected within one year of ovarian cancer diagnosis, conventional machine learning models—including XGBoost, random forests, and logistic regression—achieved the highest discriminative performance, with XGBoost reaching an ROC-AUC of 92%. Graph Convolutional Networks (GCNs) achieved moderate performance in this interval (ROC-AUC ~71%), with balanced sensitivity and specificity comparable to mid-performing conventional models. In the 1–2 year early-detection window, conventional model performance declined sharply (XGBoost ROC-AUC 46%), whereas the GCN maintained robust discriminative ability (ROC-AUC ~74%) with relatively balanced sensitivity and specificity. These findings indicate that while conventional approaches excel at detecting late pre-diagnostic signals, GNNs are more stable and effective at capturing subtle early molecular changes. Conclusions: The synolitic GNN framework demonstrates robust performance in early pre-diagnostic detection of ovarian cancer, maintaining accuracy where conventional methods decline. These results highlight the potential of network-informed machine learning to identify subtle proteomic patterns and pathway-level dysregulation prior to clinical diagnosis. This proof-of-concept study supports further development of GNN approaches for early ovarian cancer detection and warrants validation in larger, independent cohorts. Full article

This study investigates the Serial Discontinuity Concept (SDC) by analyzing the size of phytoplankton structures across 13 cascade dams in the fragmented East River, China. The results showed that dam flow-regulation minimized seasonal differences in total chlorophyll-a (Chl-a). Spatially, midstream Chl-a was higher during the dry period, whereas increased wet periods were linked to reservoir effects and nutrient conditions. Nano-phytoplankton dominated during both periods, while micro-phytoplankton declined during wet periods due to higher pH and transparency. Micro-Chl-a increased downstream in dry periods as a result of dissolved oxygen levels and silicate. Self-organizing maps revealed greater size–class variability during dry periods, with pH and conductivity identified as key regulators. Aulacoseira granulata (micro-phytoplankton) and Anabaena oscillarioides (also micro-phytoplankton) were co-dominant. This pattern suggests that the flow regulation and water impoundment by cascade dams during the wet period created localized lentic conditions with enhanced water stability, which favored the proliferation of these species, despite the increased seasonal discharge at the basin scale. These findings support the SDC in that (1) longitudinal Chl-a variations empirically validated SDC, especially during dry periods, and (2) a spatially periodic Chl-a pattern was identified, termed the Cascade Cycle of SDC (CC-SDC). Full article

The purpose of this study was to develop a model capable of predicting stress levels and interpreting the underlying physiological patterns using large-scale, real-life biosignal data. To achieve this, we utilized approximately 137,000 longitudinal measurements voluntarily collected from residents of Sejong Special Self-Governing City over a two-year period (February 2023–December 2024). Based on these data, we constructed a stress prediction framework that integrates both static machine-learning models—such as Random Forest and LightGBM—and time-series deep learning models, including LSTM and Transformer architectures. Model interpretability was further enhanced through SHapley Additive exPlanations (SHAP), which quantified the contribution of key biomarkers, and through visualization of Transformer attention weights to reveal temporal interactions within the biosignal sequences. The central objective of this study was to evaluate how accurately a deep learning model can learn and reproduce stress indices generated by existing heart rate variability (HRV)-based algorithms embedded in K-FDA-approved wearable devices. Accordingly, the ground truth used in this work reflects algorithmic outputs rather than clinically validated assessments such as salivary cortisol or psychological scales. Thus, rather than identifying independent clinical stress markers, the present work focuses on determining whether a Transformer-based model can effectively approximate device-derived physiological stress levels over time, thereby providing a methodological foundation for future applications using clinically validated stress labels. Experimental results demonstrated that the Transformer model achieved approximately 98% classification accuracy across this large dataset, indicating that it successfully captures short-term biosignal fluctuations as well as long-term temporal structure. These findings collectively demonstrate the engineering feasibility of developing a large-scale, wearable-based stress monitoring system. Full article