Insulinoma is a neuroendocrine tumor originating in the pancreas that secretes excess amounts of insulin, leading to severe hypoglycemia. The clinical presentation of hypoglycemia is classically described by Whipple’s Triad. Due to the rarity of this diagnosis, it can often be mistaken for other etiologies with similar presentations. In this paper, we present the case of a woman in her 70s with metastatic insulinoma involving the liver, who was initially diagnosed with an insulin-like growth factor 2-secreting hepatocellular carcinoma. Biochemical and immunohistochemical analyses were instrumental in distinguishing between these two etiologies.

Chronic liver cirrhosis (LC) and acute-on-chronic liver failure (ACLF) are interconnected hepatic disorders associated with substantial morbidity and mortality. Despite their distinct clinical characteristics, both conditions share common pathogenic pathways that remain inadequately understood. This study aimed to identify shared gene signatures and elucidate underlying molecular mechanisms.

In this study, we employed Weighted Gene Co-Expression Network Analysis to explore transcriptomic data from the Gene Expression Omnibus for LC and ACLF.

Key co-expression modules enriched with genes involved in glycolysis and gluconeogenesis pathways were identified, implicating metabolic dysfunction as a central feature in both conditions. Furthermore, microRNA analysis revealed that hsa-miR-122 and hsa-miR-194 play pivotal roles in regulating these metabolic pathways, potentially contributing to immune dysregulation.

Our findings indicate that these shared molecular mechanisms are critical in the progression from LC to ACLF, providing novel insights into potential therapeutic targets for mitigating disease severity and improving clinical outcomes.

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.

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.

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.