Introduction: Mucosal-associated invariant T (MAIT) cells are innate-like T cells that are involved in anti-bacterial immunity. MAIT cells are found in the intestines, but their role and distribution within the large intestine have not been fully elucidated. Therefore, we investigated the distribution of MAIT cells within the cecum and colon. Material and methods: Surgically resected tissues of the cecum and colon were obtained from 4 patients with cecal appendix cancer and 8 patients with colorectal cancer, respectively. Lymphocytes were isolated from the intestinal epithelium (intraepithelial lymphocytes – IELs) and the underlying lamina propria (lamina propria lymphocytes – LPLs), and then, MAIT cells were analyzed by flow cytometry. Results: Compared with the colon, the cecum showed a significantly increased frequency of MAIT cells among IELs (p < 0.01). CD69 expression on MAIT cells was significantly increased in the cecum and colon compared with that in the blood, and the frequency of natural killer group 2, member A+ cells among MAIT cells was significantly increased in the cecum. Conclusions: These results suggest that the distribution of MAIT cells was different between the cecum and colon and that MAIT cells were more likely to be activated, especially in the intestinal epithelium of the cecum than in the colon and blood.

Metabolic dysfunction-associated steatotic liver disease (MASLD) causes cellular senescence due to oxidative stress, endoplasmic reticulum stress, and ectopic fat deposition in the liver. Recently, dasatinib, an antitumor agent, and quercetin, a dietary supplement, were combined as a senolytic drug to eliminate senescent cells. Thus, this study aimed to examine the effects of dasatinib and quercetin administration on removing senescent cells and their therapeutic effects on MASLD in a medaka MASLD model. Dasatinib and quercetin were administered to a medaka MASLD model, which was fed a high-fat diet by dissolving them in aquarium water. The results revealed that senescent cells in the liver were increased in the HFD group but improved in the treatment group. Hematoxylin and eosin staining also showed that treatment improved fat deposition in hepatocytes. In addition, TGFβ1, a driver factor of fibrosis, was reduced in the treatment group. Dasatinib and quercetin eliminated senescent cells in MASLD, attenuated fat deposition, and suppressed fibrosis gene expression. The results indicate that dasatinib and quercetin as senolytic drugs are novel therapeutic agents that reduce MASLD.

This study investigated the efficacy and safety of radiotherapy as part of multidisciplinary therapy for advanced hepatocellular carcinoma (HCC). Clinical data of 49 HCC patients treated with radiotherapy were assessed retrospectively. The efficacy of radiotherapy was assessed by progression-free survival, disease control rate, and overall survival. Safety was assessed by symptoms and hematological assay, and changes in hepatic reserve function were determined by Child–Pugh score and albumin–bilirubin (ALBI) score. Forty patients underwent curative radiotherapy, and nine patients with portal vein tumor thrombus (PVTT) underwent palliative radiotherapy as part of multidisciplinary therapy. Local disease control for curative therapy was 80.0% and stereotactic body radiotherapy was 86.7% which was greater than that of conventional radiotherapy (60.0%). Patients with PVTT had a median observation period of 651 days and 75% three-year survival when treated with multitherapy, including radiotherapy for palliative intent, transcatheter arterial chemoembolization, and administration of molecular targeted agents. No adverse events higher than grade 3 and no changes in the Child–Pugh score and ALBI score were seen. Radiotherapy is safe and effective for HCC treatment and can be a part of multidisciplinary therapy.

Background: Osteoporosis is a common complication in patients with primary biliary cholangitis (PBC). This study aimed to compare the efficacy and safety of denosumab and zoledronic acid (ZOL) in treating osteoporosis in PBC patients. Methods: This multicenter, randomized, open-label trial enrolled Japanese patients with PBC and osteoporosis. Patients were randomized to receive either subcutaneous denosumab 60 mg every 6 months (denosumab group) or i.v. zoledronic acid 5 mg yearly (ZOL group). The primary endpoint was the mean percent change in bone mineral density (BMD) at the lumbar spine and total hip from baseline to 12 months. Results: Of 47 enrolled patients, 41 (87.2%) completed the study (denosumab: n=21; ZOL: n=20). At 12 months, lumbar spine BMD increased by 7.5% in the denosumab group and 6.4% in the ZOL group, demonstrating the non-inferiority of denosumab (95% CI: −1.6% to 3.8%). Although the total hip BMD increased more in the denosumab group than in the ZOL group (5.0% vs. 2.6%, p<0.01), the difference did not meet the predefined non-inferiority margin (95% CI: −1.3% to 6.2%). Serum ALP to upper limit of normal ratio and bone turnover markers significantly decreased in both groups; however, the rates of change were not significantly different between them. The incidence of adverse events was significantly lower in the denosumab group compared with the ZOL group (14.3% vs. 50.0%, p=0.013). Conclusions: Denosumab is a safe and effective treatment option for osteoporosis in patients with PBC.

Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens (HBsAgs) is the final therapeutic endpoint for chronic hepatitis B. Reliable predictors of the disappearance or reduction in HBsAg levels have not been established. Accurate predictions are vital to successful treatment, and corresponding efforts are ongoing worldwide. Therefore, this study aimed to identify an optimal deep learning model to predict the changes in HBsAg levels in daily clinical practice for inactive carrier patients. We identified patients whose HBsAg levels were evaluated over 10 years. The results of routine liver biochemical function tests, including serum HBsAg levels for 1, 2, 5, and 10 years, and biometric information were obtained. Data of 90 patients were included for adaptive training. The predictive models were built based on algorithms set up by SONY Neural Network Console, and their accuracy was compared using statistical analysis. Multiple regression analysis revealed a mean absolute percentage error of 58%, and deep learning revealed a mean absolute percentage error of 15%; thus, deep learning is an accurate predictive discriminant tool. This study demonstrated the potential of deep learning algorithms to predict clinical outcomes.