Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading cause of hepatic fibrosis, yet its prevalence in asymptomatic populations remains unclear. This study aimed to assess the prevalence of steatosis and significant fibrosis in asymptomatic individuals without known liver disease in the Greater Vancouver Area.

Interested individuals voluntarily registered online via the Canadian Liver Foundation website or by telephone. Inclusion criteria included age ≥ 19 years, no known liver disease, and low alcohol intake (<30 g/day for men, <20 g/day for women). Demographic and clinical data were collected, and all participants underwent transient elastography after a 3-h fast. The study aimed to collect 4,500 analyzable scans while reflecting the region’s ethnic diversity.

A total of 4,193 participants were analyzed. The median age was 62 years, the median body mass index was 25.4, and 45% were male. Asian individuals comprised 42% of the cohort. Steatosis was present in 59.6% of participants, and 45.7% met diagnostic criteria for MASLD. Significant fibrosis (F2–F4) was found in 8.6%. Age, male sex, ethnicity, cardiac disease, diabetes, hypertension, and obesity were significantly associated with fibrosis. Logistic regression analysis confirmed age, weight, diabetes, dyslipidemia, hypertension, and obesity as independent predictors.

A substantial proportion of asymptomatic individuals in Greater Vancouver have undetected MASLD and significant fibrosis. Early identification of high-risk groups may support broader implementation of transient elastography screening. This study provides one of the first North American population-based estimates of MASLD and fibrosis stratified by ethnicity, offering new insights into liver disease distribution among Caucasian, Chinese, and South Asian populations.

Metabolic dysfunction-associated fatty liver disease, representing a spectrum of liver disorders from simple steatosis to metabolic dysfunction-associated steatohepatitis, fibrosis, and cirrhosis, has emerged as one of the most prevalent chronic liver conditions globally, affecting an estimated approximately 30% of the world's population. Its pathogenesis is highly complex, involving intricate interactions between genetic predisposition, metabolic dysregulation, inflammation, and cellular stress responses. Within this complex landscape, orphan nuclear receptors (ONRs) have gained significant attention. Defined by the lack of identified endogenous ligands, ONRs function as master transcriptional regulators controlling diverse biological processes. Crucially, they play pivotal roles in the development and progression of numerous diseases, including metabolic disorders.This review specifically focuses on elucidating the critical contributions of various ONRs to the pathogenesis of metabolic dysfunction-associated fatty liver disease. We examined how these receptors modulate key pathological drivers: lipid metabolism, inflammation,and autophagy.

Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is essential for non-invasively investigating brain function. However, conventional fMRI methods are limited by low spatial and temporal resolution. This narrative review evaluates recent advancements in deep learning techniques for high-resolution BOLD-fMRI reconstruction, focusing on super-resolution, segmentation, and image registration. A comprehensive literature search was conducted across PubMed, IEEE, Scopus, and Web of Science databases for the period 2000–2023. Studies employing deep learning methods, including convolutional neural networks, transformer-based models, and generative adversarial networks for super-resolution, segmentation, and registration of BOLD-fMRI, were included. Deep learning approaches demonstrated significant improvements in spatial resolution, segmentation accuracy, and registration robustness. Convolutional neural network-based models, particularly generative adversarial networks, notably improved image reconstruction quality and detail preservation. Preliminary studies targeting specific brain regions such as the cerebellum and hippocampus showed promise; however, systematic evaluations across broader brain areas and large-scale clinical validations remain limited. While deep learning techniques have led to substantial advancements in high-resolution BOLD-fMRI reconstruction, future research should focus on standardized protocols, multi-center validation, and improving computational efficiency and model generalization to enhance clinical utility.

Emerging evidence implicates immune dysregulation and neuroinflammation in the pathogenesis of epilepsy, yet the causal mechanisms remain unclear. This study aimed to investigate the causal effects of immune cells and inflammatory proteins on epilepsy and evaluate the mediating role of inflammatory proteins.

This study utilized the largest available genome-wide association study data on immune cell phenotypes and inflammatory proteins as exposures, and epilepsy genome-wide association study data from the FinnGen dataset as outcomes. Five Mendelian randomization (MR) methods were applied within a two-sample MR framework to assess causal effects. Furthermore, a two-step MR analysis was conducted to quantify the proportion of epilepsy and its subtypes influenced by immune cells through inflammatory proteins.

The two-sample MR analysis identified 32 immune cell phenotypes associated with epilepsy risk (19 risk-increasing, e.g., CD19+ B cells; 13 protective, e.g., regulatory T cell subsets). Subtype analyses revealed 30 immune phenotypes associated with generalized epilepsy and 26 with focal epilepsy. Eight inflammatory proteins showed suggestive causal effects on epilepsy: C-C chemokine ligand 23, C-X-C motif chemokine ligand 6, C-X-C motif chemokine ligand 11, and vascular endothelial growth factor A increased epilepsy risk, while interleukin-13 (IL-13), leukemia inhibitory factor receptor, tumor necrosis factor, and osteoprotegerin conferred protection. Mediation analysis indicated that inflammatory proteins mediated 6.3–13.5% of the immune effects on epilepsy. Specifically, CD14+CD16+ monocytes increased epilepsy risk through elevated C-C chemokine ligand 23 levels (8.5% mediation), while effector memory double-negative (CD4−CD8−) T cells reduced epilepsy risk via upregulation of IL-13 (6.3%). Sensitivity analyses confirmed the robustness of these findings (P heterogeneity/pleiotropy > 0.05). Although no associations reached Bonferroni-corrected significance, the findings implicate B cells, monocytes, regulatory T cells, and cytokines (e.g., IL-13, leukemia inhibitory factor receptor) in the pathogenesis of epilepsy, with inflammatory proteins acting as partial mediators.

These results enhance our understanding of immune-inflammatory pathways in epilepsy and highlight potential therapeutic targets. Future studies should validate these findings across diverse populations and further elucidate the molecular mechanisms underlying the identified associations.