The aim of this study was to develop and internally validate an interpretable machine learning (ML) model for predicting the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) infection.

The role of immune cell profiles at birth in determining the risk of celiac disease (CD) development is currently unestablished. This study aimed to determine the associations between T- and B-cell profiles at birth and pediatric CD.

To analyze the expression patterns of circRNAs in metabolic associated fatty liver disease (MAFLD) and the regulation of m6A methylation on those circRNAs.

Upper gastrointestinal bleeding (UGIB) in pediatric patients is a significant clinical concern requiring prompt diagnosis and management. This study aims to provide a descriptive analysis of the common causes of UGIB in pediatric patients in Kerman, Iran.

Single nucleotide polymorphisms (SNPs) in patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2) and membrane bound O-acyltransferase domain containing 7 (MBOAT7) genes were reported to be strongly associated with non-alcoholic fatty liver disease (NAFLD) pathogenicity among different populations. We investigated whether these SNPs are associated with prognostic factors and genetic biomarkers of non-alcoholic steatohepatitis (NASH)-related hepatocellular carcinoma (HCC) in the Sri Lankan context.

Muscle cramps are among the common debilitating complications of liver cirrhosis. Since this complication lacks effective treatments, we aimed to evaluate the effectiveness of L-carnitine supplementation in reducing the frequency, duration, and severity of muscle cramps in patients with liver cirrhosis.

Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligence (AI) algorithms have been employed for imaging diagnoses. In this study, we examined the sensitivity of neoplastic lesions in CT colonography images.

With the rising incidence of MASLD, extensive drug research has been conducted in clinical trials. The study examined the design principles and research objectives of MASLD therapeutics, in order to offer guidance to clinical trial participants and decision makers.

Colonoscopy, a crucial procedure for detecting and removing colorectal polyps, has seen transformative advancements through the integration of artificial intelligence, specifically in computer-aided detection (CADe) and diagnosis (CADx). These tools enhance real-time detection and characterization of lesions, potentially reducing human error, and standardizing the quality of colonoscopy across endoscopists. CADe has proven effective in increasing adenoma detection rate, potentially reducing long-term colorectal cancer incidence. However, CADe's benefits are accompanied by challenges, such as potentially longer procedure times, increased non-neoplastic polyp resections, and a higher surveillance burden. CADx, although promising in differentiating neoplastic and non-neoplastic diminutive polyps, encounters limitations in accuracy, particularly in the proximal colon. Real-world data also revealed gaps between trial efficacy and practical outcomes, emphasizing the need for further research in uncontrolled settings. Moreover, CADx limited specificity and binary output underscore the necessity for explainable artificial intelligence to gain endoscopists' trust. This review aimed to explore the benefits, harms, and limitations of artificial intelligence for colon cancer screening, surveillance, and treatment focusing on CADe and CADx systems for lesion detection and characterization, respectively, while addressing challenges in integrating these technologies into clinical practice.