Myosteatosis is associated with poor outcomes in various liver diseases. However, standardized methods for assessing, defining, and diagnosing myosteatosis in the context of liver diseases remain unclear. Furthermore, the underlying mechanisms by which myosteatosis leads to pathophysiological progression and adverse health outcomes remain elusive. Therefore, in this review, we elaborate on the currently available measures, definitions, and diagnostic criteria of myosteatosis in the existing literature. We thoroughly clarify the recent evidence and data regarding the possible involvement of myosteatosis in the progression and deterioration of various liver diseases and resulting complications, including liver cirrhosis, chronic viral hepatitis, non-alcoholic/metabolic-associated fatty liver disease, primary sclerosing cholangitis, liver transplantation, and hepatocellular carcinoma. Additionally, it synthesizes insights from basic research on the pathogenesis of myosteatosis, which involves multifactorial mechanisms, including insulin resistance, mitochondrial dysfunction, and chronic inflammation. Finally, from an operational and pragmatic perspective, several regimens, including physical, nutritional, and pharmacological therapies, have been discussed as potential treatments for myosteatosis.

Microsatellite-stable colorectal cancer, which accounts for roughly 80–85% of cases, remains largely refractory to immune checkpoint inhibitors compared with microsatellite instability-high tumors. This review synthesizes current evidence on tumor-intrinsic and microenvironmental mechanisms underlying immune checkpoint inhibitor resistance in microsatellite-stable colorectal cancer—including low neoantigen burden and impaired antigen presentation, activation of Wnt/β-catenin and MAPK signaling that exclude T cells, an immunosuppressive cellular milieu (regulatory T cells, myeloid-derived suppressor cells, M2-like tumor-associated macrophages, cancer-associated fibroblasts), metabolic reprogramming, and gut microbiome dysbiosis—and evaluates translational strategies aimed at overcoming these barriers. Preclinical and early-phase clinical data indicate that rational, mechanism-guided combinations (vascular normalization, myeloid reprogramming, metabolic inhibitors, antigen-priming approaches, and microbiome modulation) can enhance immune infiltration and produce benefits in biomarker-defined subgroups. Moving the field forward will require biomarker-driven, adaptive clinical trials with embedded translational endpoints to optimize patient selection and manage toxicity.

Chronic diabetes mellitus is marked by hyperglycemia and metabolic dysfunction, increasing the risk of complications such as nephropathy. This study aimed to evaluate key biochemical parameters among participants with diabetic nephropathy (DNp), diabetes control (DC), nephropathy control (NC), and healthy control groups.

A prospective case-control study was conducted with 200 participants categorized into four groups: DNp, NC, DC, and healthy controls. Biochemical parameters, including glucose, glycated hemoglobin, waste metabolites, proteins, enzymes, electrolytes, and lipids, were analyzed using an Advia 1800 chemical system analyzer (Siemens, Germany) with standard kits.

Among the four investigated groups, the DNp group exhibited augmented fasting glucose (178.75 ± 61 mg/dL), glycated hemoglobin (8.13 ± 1.7%), creatinine (5.67 ± 1.8 mg/dL), and blood urea nitrogen (72.02 ± 22.8 mg/dL), indicating poor glycemic control and impaired kidney function. In contrast, the DC group showed elevated random glucose levels (280 ± 3.1 mg/dL). Elevated inflammatory markers (C-reactive protein, 6.35 ± 6.3 mg/L; lactate dehydrogenase, 1,216.43 ± 634 U/L) were observed in the NC group. Compared to the other groups, the DC group demonstrated augmented lipid profiles, including elevated triglycerides (230.67 ± 59 mg/dL), very low-density lipoprotein (48.5 ± 16.5 mg/dL), low-density lipoprotein (107.41 ± 16 mg/dL), and cholesterol (169 ± 19 mg/dL). Statistical analysis was performed using one-way analysis of variance followed by a t-test to investigate differences among groups at P < 0.05.

Altered biochemical variations were noted among groups. The DNp group showed renal dysfunction and poor glycemic control, the DC group had dyslipidemia and hyperglycemia, and the NC group showed elevated inflammatory markers. Early testing is indispensable for the timely diagnosis and management of diabetic complications.

Delirium, commonly observed in critically ill patients following intracerebral hemorrhage (ICH), is an acute neuropsychiatric disorder characterized by disturbances in attention, consciousness, and cognition. The underlying brain network mechanisms remain poorly understood. This study aimed to explore the functional connectivity (FC) of the ascending reticular activating system (ARAS) in delirium patients with basal ganglia ICH and to identify potential biomarkers for predicting delirium onset.

In this cross-sectional study, brain networkomics techniques were used to examine the FC within the ARAS in ICH patients with and without delirium. A two-sample t-test compared differences in ARAS connectivity between delirium and non-delirium groups, identifying abnormal brain regions and their corresponding FC values. Receiver operating characteristic curve analysis was then performed to evaluate the predictive value of FC for delirium onset.

A significant disruption in FC between the brainstem ARAS nuclei and the left parahippocampal gyrus was observed in ICH patients with delirium. The FC strength between these regions was a reliable predictor of delirium occurrence, with an area under the curve of 0.893, indicating high predictive accuracy.

The disruption of FC between the brainstem ARAS nuclei and the left parahippocampal gyrus may represent a key mechanism underlying delirium pathogenesis. The strength of this connectivity could serve as a potential biomarker for predicting delirium onset. Future research should focus on strategies to restore this connectivity as a potential treatment for early reversal of delirium.

Portal vein thrombosis (PVT) frequently occurs in patients with porto-sinusoidal vascular disease (PSVD), but its clinical characteristics and outcomes remain poorly understood. This study aimed to investigate the clinical features and outcomes of PVT in PSVD.

A total of 169 patients with PSVD confirmed by hepatic histology were included. PVT was diagnosed using contrast-enhanced magnetic resonance imaging or computed tomography. Demographic, clinical, and laboratory data, portal hypertension-related complications, comorbidities, and mortality were collected and compared between patients with and without PVT. The primary outcomes were baseline clinical characteristics and liver-transplantation-free mortality; the secondary outcome was the dynamic changes of PVT during follow-up.

At baseline, 45 (26.6%) PSVD patients had PVT. Compared to those without PVT, patients with PVT had significantly higher rates of esophageal variceal bleeding (62.2% vs. 29.0%), ascites (73.3% vs. 35.5%), antithrombin III deficiency (78.1% vs. 38.4%) (all p < 0.001), and a history of hematological disorders (11.1% vs. 0.8%, p = 0.005). After a median follow-up of 40.1 (23.4–62.3) months, liver-transplantation-free mortality rates were 7.9% (3/38) and 1.8% (2/112) in patients with and without PVT, respectively (log-rank p = 0.110). Among 41 patients followed for a median of 17.1 (7.4–39.3) months, PVT resolved in 9.1% (1/11) of those with baseline PVT and developed in 13.3% (4/30) of those without PVT at baseline. The one- and two-year cumulative incidence rates of PVT were 3.3% and 6.7%, respectively.

PSVD patients with PVT experience more portal hypertension-related complications, complex coagulation profiles, hematological disorders, and a higher risk of death compared to those without PVT.

Despite advancements in diagnostic and therapeutic strategies, hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality. Antioxidant-1 (ATOX1) has been implicated in oncogenic processes across various cancer types; however, its specific role in HCC remains unclear. This study aimed to investigate the function of ATOX1 and its underlying molecular mechanisms in HCC.

Immunohistochemical analysis was conducted to assess ATOX1 expression in HCC tissues. Cell Counting Kit-8, colony formation, Transwell migration, flow cytometry, and reactive oxygen species (ROS) assays were employed to evaluate the malignant behaviors of tumor cells. A xenograft mouse model was employed to assess the effects of ATOX1 knockdown on tumor growth in vivo. DCAC50 treatment was performed to inhibit the copper transport function of ATOX1. RNA sequencing was conducted to explore the potential molecular mechanisms of ATOX1 in HCC.

ATOX1 expression was significantly elevated in HCC tumor tissues. ATOX1 promoted cell proliferation, colony formation, and migration. Knockdown of ATOX1 suppressed tumor growth in vivo. Mechanistically, ATOX1 activated c-Myb, and thus enhanced the malignant phenotype of HCC cells via activation of the PI3K/AKT signaling pathway. Additionally, ATOX1 reduced intracellular copper accumulation and inhibited ROS production and apoptosis. Inhibition of ATOX1 by DCAC50 decreased cell proliferation while increasing ROS levels and apoptosis in HCC cells. Notably, acetylcysteine reversed the reduction in c-Myb expression induced by ATOX1 knockdown.

ATOX1 may promote HCC carcinogenesis through the activation of the c-Myb/PI3K/AKT pathway and the inhibition of copper accumulation and oxidative stress.

Despite rapid advances in computational biology and regulatory reforms encouraging the reduction of animal use, a clear synthesis of how artificial intelligence (AI)-driven polypharmacology can function as a scientific and ethical bridge between traditional in vivo pharmacology and human-relevant drug development remains lacking. The shift from cage-based experimentation to code-based predictive modeling presents both opportunities and unresolved challenges in biological interpretation, regulatory acceptance, and pharmacology education. Therefore, this review aims to critically examine the transition toward AI-enabled, human-centric drug discovery within the framework of the 3R principles (Replacement, Reduction, and Refinement). Specifically, it explores (i) the global regulatory and ethical drivers accelerating non-animal methodologies, (ii) the scientific and educational gaps emerging from reduced dependence on animal models, and (iii) the role of AI and deep learning in reconstructing biological complexity through multi-omics integration and predictive toxicity modeling. By analyzing emerging AI platforms and computational strategies, this review highlights how AI-driven polypharmacology may offer a scalable, ethical, and precision-oriented framework for future pharmacological research.

Terminal ileum intubation is considered the completion step of colonoscopy and is usually performed to assess the ileum. The histological examination of the ileal mucosa, which is acquired during terminal ileum intubation, may allow an accurate diagnosis. However, there is no absolute consensus on when ileoscopy and biopsy should be attempted. As a result, we aimed to evaluate whether terminal ileum intubation and biopsy should be performed routinely.

Systematic searches were performed in the PubMed, EMBASE, and Cochrane Library databases, as well as the Science Citation Index via the Web of Science platform. Reference lists from the identified papers were manually searched. Systematic searches were performed from January 1, 1971, to October 1, 2025. Studies reporting on terminal ileum intubation and biopsy during colonoscopy were included. Case reports, letters, reviews, and animal studies were excluded. The primary outcomes were the diagnostic yield of terminal ileum intubation and the rate of necessitating a change in management. Data were extracted independently by three reviewers.

Thirty-six studies were included. The subtotal diagnostic yield and the rate of necessary change among the selected patients were much greater than those among the unselected patients (5.1% versus 2.5% and 1.5% versus 0.4%, respectively). In addition, the diagnostic yield was found more frequently for inflammatory bowel disease, anemia, abdominal pain, and chronic diarrhea than for the other indications (26.7%, 16.1%, 14.9%, 12.4%, and 3.2%, respectively). The yield of ileal histopathology with a normal endoscopic appearance was low in both unselected and selected patients (3.5% and 2.4%, respectively).

Terminal ileum intubation is recommended as gold standard for completing colonoscopy. Biopsy should be considered in patients with abnormal endoscopic findings or specific high-risk symptoms.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now considered to be among the most prevalent chronic liver diseases worldwide. Its comprehensive management encompasses multiple stages, including risk assessment, early detection, stratified intervention, and long-term follow-up. Among these, improving diagnostic accuracy and optimizing individualized therapeutic strategies remain key challenges in both research and clinical practice. In recent years, artificial intelligence and smart devices have developed rapidly and have gradually been applied in the medical field, offering novel tools and pathways for MASLD risk stratification, non-invasive diagnosis, therapeutic evaluation, and patient self-management. This review summarizes the current applications of artificial intelligence and smart devices in MASLD care, highlights their benefits and limitations, and discusses future directions to support precision diagnosis and treatment strategies.