Background: Liver transplant recipients are highly susceptible to infectious complications due to surgical invasiveness and immunosuppressive therapy, and post-transplant bloodstream infection is associated with substantial morbidity and mortality. Although several prediction models for bloodstream infection have been proposed, most focus on emergency department or general ward populations and rely on black-box approaches. This limits their applicability and clinical interpretability in liver transplant settings. Therefore, this study aimed to develop predictive models for post-transplant bloodstream infection using preoperative and perioperative clinical data and to derive an interpretable risk equation through symbolic regression. Methods: We conducted a retrospective observational study including 245 adult liver transplant recipients treated at a single tertiary center. Clinical and laboratory variables were extracted from electronic medical records and analyzed using standard statistical methods. For prediction tasks, multiple conventional machine learning models were developed and compared with a symbolic regression-based model. Predictive performance and model interpretability were evaluated using discrimination metrics and Shapley Additive Explanations. Results: Post-transplant bloodstream infection occurred in 82 patients (33.4%). In the test set, conventional machine learning models showed modest discriminative performance (area under the curve, 0.53–0.64). The symbolic regression model achieved comparable discrimination (area under the curve, 0.63) while providing transparent, threshold-based risk equations. While conventional models primarily relied on laboratory variables, symbolic regression additionally identified perioperative clinical factors and viral serologic markers as important predictors. Discussion: Although overall predictive performance was modest, symbolic regression highlighted viral serologic markers as potential indicators of immunologic vulnerability, extending beyond standard laboratory predictors. Conclusions: This interpretability-focused approach may inform future risk stratification models incorporating richer perioperative data.

Background/Objectives: Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide. Despite advances in treatment, outcomes for advanced CRC remain unsatisfactory due to uncontrolled proliferation, metastasis, and recurrence. This study investigated the anti-cancer effects of KMU-11342, an indolin-2-one-based multi-protein kinase inhibitor with previously reported anti-inflammatory properties, in human colorectal cancer models. Methods: The anti-cancer effects of KMU-11342 were evaluated in colorectal cancer cells and further investigated in three-dimensional (3D) spheroid and patient-derived organoid models. Cell proliferation, migration, apoptosis, and cell cycle progression were assessed. Kinase activity profiling and molecular docking analyses were performed to identify potential targets and characterize the underlying signaling pathways. Results: KMU-11342 significantly inhibited the proliferation and migration of CRC cells. It reduced CRC cell density by 58.9% and 83.3% at 0.5 and 1 μM, respectively. These effects were accompanied by G2/M cell cycle arrest and apoptotic cell death. In 3D models, spheroid formation was markedly reduced and stemness-related characteristics were diminished. Patient-derived CRC organoids also showed decreased viability, exhibiting 38.6% and 77.4% reductions at 1 and 2 μM, respectively. These effects were observed in a dose-dependent manner in both two-dimensional (2D) and 3D colorectal cancer models. Kinase activity profiling and molecular docking analyses identified glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase 1 (CDK1) as potential mediators of the anti-cancer effects of KMU-11342 through the p53/nuclear factor kappa B (NF-κB) and FoxO1 signaling axes, respectively. Conclusions: KMU-11342 exhibits potent anti-tumor activity against CRC through suppressing proliferation, migration, and stemness in both 2D and 3D models, including patient-derived organoids. Its effects may be mediated, at least in part, through modulation of GSK3β and CDK1 via the p53/NF-κB and FoxO1 signaling pathways.