In-hospital cardiac arrest (IHCA) from non-cardiac causes is a life-threatening condition characterized by high mortality and complex etiology. Despite advances in critical care monitoring, effective early warning systems remain lacking due to challenges in balancing sensitivity and specificity within imbalanced clinical data and the interpretability of machine learning models remains poorly defined.
We developed and evaluated six machine learning algorithms using data from 43,618 ICU patients in the MIMIC-IV database. Model performance was assessed through multiple metrics, and SHapley Additive exPlanations (SHAP) analysis was employed for model interpretability. Decision curve analysis was performed to evaluate clinical utility.
Distinct performance trade-offs were identified across models. XGBoost achieved optimal discriminative ability (AUC = 0.730, 95% CI: 0.695–0.763), while CatBoost favored sensitivity (0.907) to meet clinical guidelines. SHAP analysis identified anion gap (AG), white blood cell count (WBC), red cell distribution width (RDW), Glasgow Coma Scale (GCS), platelet, partial thromboplastin time (PTT), and red blood cell count (RBC) as key predictors, with elevated inflammatory markers and reduced neurological parameters consistently associated with increased risk.
Gradient boosting algorithms demonstrate crucial utility in predicting non-cardiac IHCA. The identified interpretable biomarkers align with established pathophysiological mechanisms and may reflect downstream manifestations of genome instability pathways, representing potential targets for early clinical intervention.
Erectile dysfunction (ED) is increasingly recognized as a vascular disorder in which endothelial nitric oxide synthase (eNOS)-dependent signaling is disrupted early and may precede overt cardiovascular disease. In vasculogenic and endothelial dysfunction-dominant forms of ED, accumulating evidence suggests that epigenetic regulation and genome-maintenance pathways contribute to cavernosal endothelial vulnerability. In this review, we synthesize data linking locus-specific DNA methylation—particularly at NOS3/eNOS-related regions—to reduced nitric oxide bioavailability and impaired cavernosal relaxation, while also examining how DNA repair pathways and the DNA damage response may shape endothelial phenotype under oxidative, hypoxic, metabolic, and inflammatory stress. Available evidence supports roles for base excision repair, homologous recombination, PARP-associated signaling, and broader genome-maintenance mechanisms in modulating endothelial resilience, although the strength of support is not uniform across pathways. Direct ED-related evidence is strongest for methylation-associated dysregulation of nitric oxide signaling and for DNA damage-associated impairment in diabetic or radiation-related cavernosal injury models, whereas substantial parts of the proposed methylation–DNA repair–genome stability axis are still inferred from broader vascular and endothelial biology rather than from matched human cavernosal datasets. Recent human single-cell studies of the corpus cavernosum have strengthened the biological rationale for this framework by demonstrating marked cellular and microenvironmental remodeling in diseased tissue, but they do not directly quantify DNA methylation status or pathway-specific DNA repair capacity. Clinically, current practice can phenotype vascular ED using hemodynamic tools, yet standardized approaches for directly assessing methylation-associated repression, narrowed DNA repair reserve, or genome-instability-related stress in human cavernosal tissue remain lacking. Addressing this gap may improve biological stratification and support more mechanism-informed translational studies in ED.
TRIM33α, a bromodomain-containing member of the TRIM protein family involved in protein ubiquitination and transcriptional regulation, plays a critical role in tumor progression, and its dysregulation has been linked to genomic instability and aberrant transcriptional activity in multiple cancers, highlighting its potential as a therapeutic target in oncology. However, selective inhibitors against TRIM33α have not been developed yet.
This study aims to identify novel TRIM33α inhibitors using in silico strategy integrating structure-based pharmacophore modeling, high-throughput virtual screening, molecular docking, and molecular dynamics (MD) simulations.
A pharmacophore model was derived from the crystal structure of TRIM33α and validated using a benchmark set of known ligands. The optimized model, comprising 12 essential features, was applied to screen over 2.7 million compounds from the ChEMBL and ZINC databases. After pharmacophore-based filtering and drug-likeness selection, 1180 candidates were subjected to LibDock-based molecular docking. The top 15 compounds, selected based on LibDock scores, underwent 100 ns MD simulations and MM/GBSA binding free energy analysis for top 4 molecules. Among these, CHEMBL1795351 exhibited higher binding affinity than the reference ligand IACS-9571.
This study identifies promising lead compounds and establishes a robust computational framework for developing TRIM33α-targeted therapies. The identified compounds may serve as valuable starting points for further experimental validation and structural optimization toward the development of novel anticancer agents targeting TRIM33.
Computational identification of TRIM33α inhibitors through virtual screening, molecular docking and MDsimulations.
Colorectal cancer (CRC) continues to represent one of the leading causes of cancer-related morbidity and mortality, globally. Natural phytochemicals from Commiphora wightii (C. wightii) has recently garnered attention as possible anticancer agents due to their multi-targeting mechanisms. The aim of this study was to investigate the molecular mechanisms of inhibition for these phytochemicals against CRC, via an integrated in silico approach.
Potential targets of C. wightii phytochemicals and CRC-associated genes were retrieved from publicly available databases. Overlapping targets were identified using Venn analysis, which were subjected to protein-protein interaction (PPI) network construction and hub gene selection using Cytoscape and CytoHubba. Functional enrichment was performed through Gene Ontology (GO) and KEGG pathway analyses. The expression and prognostic significance of hub genes were evaluated using GEPIA2 and Kaplan–Meier survival analysis. Immune infiltration correlations were assessed using TIMER 3.0, and molecular docking was performed using CB-Dock2 to explore binding affinities of phytochemicals with hub proteins.
A total of 85 overlapping targets were identified between C. wightii phytochemicals and CRC-associated genes. Network analysis revealed ten hub genes, among which MET, CDK1, MMP9, PLAU, and CCND1 were significantly upregulated in CRC tissues. Immune infiltration analysis demonstrated significant association of MMP9 and PLAU with macrophage infiltration in both COAD and READ, suggesting their potential role in modulating the tumor immune microenvironment. Molecular docking revealed strong binding affinities of E-Guggulsterol toward MMP9 (-10.6 kcal/mol) and MET (-9.4 kcal/mol), while Quercetin showed favorable interactions with PLAU (-8.5 kcal/mol), MMP9 (-8.7 kcal/mol), and MET (-8.3 kcal/mol). These findings highlight E-Guggulsterol-MMP9, E-Guggulsterol-MET, Quercetin-PLAU, and Quercetin-MET as promising compound-target pairs for further investigations.
This study identified key CRC-associated targets potentially modulated by C. wightii phytochemicals. Integrated network pharmacology, immune infiltration, and molecular docking analyses highlighted MMP9, PLAU, and MET as biologically relevant targets. E-Guggulsterol and Quercetin demonstrated favorable interactions with these proteins, particularly E-Guggulsterol-MMP9, E-Guggulsterol-MET, Quercetin-PLAU, and Quercetin-MET, suggesting their potential as lead compounds for CRC therapy. Since this work relies on in silico analysis, we call for further in vitro and in vivo studies to support our findings and check if these effects could have therapeutic implications.