(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient’s liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.

Background: The preventative effect of melatonin on the development of obesity and the progression of fatty liver under a high-fat diet (HFD) has been well elucidated through previous studies. We investigated the mechanism behind this effect regarding cholesterol biosynthesis and regulation of cholesterol levels.Methods: Mice were divided into three groups: normal chow diet (NCD); HFD; and HFD and melatonin administration group (HFD+M). We assessed the serum lipid profile, mRNA expression levels of proteins involved in cholesterol synthesis and reabsorption in the liver and nutrient transporters in the intestines, and cytokine levels. Additionally, an in vitro experiment using HepG2 cells was performed.Results: Expression of hepatic sterol regulatory element-binding protein 2 (SREBP-2), 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), and low-density lipoprotein receptor (LDLR) demonstrated that melatonin administration significantly reduces hepatic cholesterol synthesis in mice fed an HFD. Expression of intestinal sodium-glucose transporter 1 (SGLT1), glucose transporter 2 (GLUT2), GLUT5, and Niemann-pick C1-like 1 (NPC1L1) demonstrated that melatonin administration significantly reduces intestinal carbohydrate and lipid absorption in mice fed an HFD. There were no differences in local and circulatory inflammatory cytokine levels among the NCD, HFD, and HFD+M group. HepG2 cells stimulated with palmitate showed reduced levels of SREBP, LDLR, and HMGCR indicating these results are due to the direct mechanistic effect of melatonin on hepatocytes.Conclusion: Collectively, these data indicate the mechanism behind the protective effects of melatonin from weight gain and liver steatosis under HFD is through a reduction in intestinal caloric absorption and hepatic cholesterol synthesis highlighting its potential in the treatment of obesity and fatty liver disease.

Recent advances in the integration of artificial intelligence (AI) and microbiome analysis have expanded our understanding of gastrointestinal diseases, particularly in inflammatory bowel disease (IBD), colitis-associated colorectal cancer (CAC), and sporadic colorectal cancer (CRC). While IBD and CAC are mechanistically linked, recent evidence also implicates dysbiosis in sporadic CRC. The progression from IBD to CAC is mechanistically linked through chronic inflammation and microbial dysbiosis, whereas distinct dysbiotic patterns are also observed in sporadic CRC. In this review, we examined how machine learning (ML) and AI were applied to the microbiome and multi-omics data, which enabled the discovery of non-invasive microbial biomarkers, refined risk stratification, and prediction of treatment response. We highlighted how emerging computational frameworks, including explainable AI (xAI), graph-based models, and integrative multi-omics, were advancing the field from descriptive profiling toward predictive and prescriptive analytics. While emphasizing these innovations, we also critically assessed current limitations, including data variability, the lack of methodological standardization, and challenges in clinical translation. Collectively, these developments enabled AI-powered microbiome research as a driving force for precision medicine in IBD, CAC, and sporadic CRC.

Background: We evaluated the protective effects of melatonin against high-fat diet (HFD)-induced deterioration of bone microarchitecture using high-resolution peripheral quantitative computed tomography (HR-pQCT).Methods: Four-week-old male C57BL/6 mice were divided into control (chow diet group), HFD, and HFD + melatonin-administered groups. Mice were sacrificed after 14 weeks, and the right femur was extracted. The microskeletal structure of the femur was analyzed using SkyScan1173 (version 1.6). A 3-dimensional image was reconstructed using the Nrecon (version 1.7.0.4) program.Results: Bone volume (BV) was significantly increased in the HFD group compared with that in the normal diet group, and that of the melatonin group also increased significantly compared with BV of the normal diet group (p<0.05). Percent BV/total volume [TV] and bone surface/BV were significantly higher in both the HFD and melatonin groups than in the normal diet group (p<0.05), and the melatonin group had the highest BV/total volume (TV). BMD was lower in the HFD than in the normal diet group and was the highest in the melatonin group.Conclusions: This study shows that melatonin inhibited the deterioration of microarchitecture induced by a HFD. A better understanding of the protective effect of melatonin on bone microarchitecture and mechanisms could provide fracture prevention for people who are obese.

Background: Post-hepatectomy liver failure (PHLF) is a serious complication following hepatic resection for Klatskin tumors, significantly affecting patient prognosis. Identifying reliable preoperative and early postoperative predictors of PHLF can help optimize patient outcomes and guide surgical planning. Method: We conducted a retrospective review of 34 patients who underwent hemi-hepatectomy for extrahepatic cholangiocarcinoma at Kosin University Gospel Hospital between April 2019 and April 2024, and at Chonnam National University Hwasun Hospital between September 2017 and April 2024. Demographics, laboratory data, and volumetric measurements including spleen volume, were analyzed to assess their roles in predicting PHLF. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the predictive value of these factors. Results: Elevated preoperative glucose levels and reduced future liver remnant to spleen ratio (FLR/SV) were significantly associated with an increased risk of PHLF. Additionally, elevated postoperative day 1 total bilirubin (POD 1 TB) was identified as a key postoperative predictor of PHLF. Multivariable analysis confirmed the significance of these factors, with FLR/SV, preoperative glucose, and POD 1 TB demonstrating good discriminative ability in ROC analysis, with AUC values of 0.779, 0.782, and 0.786 respectively. Conclusion: Preoperative glucose control, evaluation of FLR/SV, and early postoperative monitoring of TB are useful for improving outcomes in patients undergoing major hepatectomy for Klatskin tumors.

Background: Postoperative liver regeneration is essential for maintaining hepatic function. This study evaluated the rate, determinants, and volumetric patterns of early liver regeneration after hemihepatectomy.Methods: A retrospective review was conducted of 50 patients who underwent right or left hemihepatectomy between April 2019 and March 2025. Liver and spleen volumes (SV) were assessed preoperatively, at postoperative day (POD) 1 week, and at POD 3 months. Early liver regeneration rate (LRR) was defined as the percentage increase in remnant liver volume at POD 1 week relative to the preoperative future liver remnant (FLR), and patients were categorized into low (<50%) and high (≥50%) LRR groups. Clinical, biochemical, and volumetric variables were compared, and predictors of regeneration were identified using multivariable analyses. Regeneration patterns were also examined according to whether the FLR/standard liver volume (SLV) ratio was <50% or ≥50%.Results: FLR/SLV was the strongest independent predictor of rapid early liver regeneration (p<0.001). Remnants with FLR/SLV <50% exhibited rapid and sustained regeneration, whereas those with FLR/SLV ≥50% showed slower regrowth that plateaued after reaching approximately 90% of SLV. SV increased at POD 1 week in all patients; however, only the FLR/SLV ≥50% group showed a reduction by POD 3 months, whereas the <50% group maintained elevated volumes.Conclusions: FLR/SLV reliably predicts early postoperative liver regeneration. Smaller remnants regenerate more rapidly, whereas persistent splenic enlargement suggests a sustained portal hemodynamic burden. Combined evaluation of FLR/SLV and SV may enhance perioperative risk assessment and surgical planning.