Background/Objectives: Colorectal cancer (CRC) remains a major global health burden, marked by complex tumor–microenvironment interactions, genetic heterogeneity, and varied treatment responses. Effective preclinical models are essential for dissecting CRC biology and guiding personalized therapeutic strategies. This review aims to critically evaluate current experimental CRC models, assessing their translational relevance, limitations, and potential for integration into precision oncology. Methods: A systematic literature search was conducted across PubMed, Scopus, and Web of Science, focusing on studies employing defined in vitro, in vivo, and emerging integrative CRC models. Studies were included based on experimental rigor and relevance to therapeutic or mechanistic investigation. Models were compared based on molecular fidelity, tumorigenic capacity, immune interactions, and predictive utility. Results: CRC models were classified into in vitro (2D cell lines, spheroids, patient-derived organoids), in vivo (murine, zebrafish, porcine, canine), and integrative platforms (tumor-on-chip systems, humanized mice, AI-augmented simulations). Traditional models offer accessibility and mechanistic insight, while advanced systems better mimic human tumor complexity, immune landscapes, and treatment response. Tumor-on-chip and AI-driven models show promise in simulating dynamic tumor behavior and predicting clinical outcomes. Cross-platform integration enhances translational validity and enables iterative model refinement. Conclusions: Strategic deployment of complementary CRC models is critical for advancing translational research. This review provides a roadmap for aligning model capabilities with specific research goals, advocating for integrated, patient-relevant systems to improve therapeutic development. Enhancing model fidelity and interoperability is key to accelerating the bench-to-bedside translation in colorectal cancer care.

The global burden of heart failure (HF) continues to escalate, with a lifetime risk approaching one in four adults in the United States. Concurrently, advances in cancer therapeutics have created a burgeoning population of long-term survivors, who now face the significant morbidity and mortality of chemotherapy-induced cardiovascular disease (CVD). This review addresses the critical overlap of these two pathologies, which share fundamental drivers such as oxidative stress, inflammation, and metabolic dysregulation. Epigallocatechin gallate (EGCG), the most abundant and biologically active polyphenol in green tea, has demonstrated pleiotropic bioactivity in preclinical models, encompassing potent antioxidant, anti-inflammatory, and anti-apoptotic properties. The central aim of this review is to provide a critical and comprehensive synthesis of the evidence supporting EGCG’s dual protective role. This review dissects its molecular mechanisms in modulating key pathways in HF and cardio-oncology, evaluates its translational potential, and importantly, delineates the significant gaps that must be addressed for its clinical application. This analysis uniquely positions EGCG not merely as a nutraceutical, but as a multi-target molecular therapeutic capable of simultaneously addressing the convergent pathological cascades of heart failure and cancer-related cardiotoxicity. The synthesis of preclinical evidence with a critical analysis of its translational barriers offers a novel perspective and a strategic roadmap for future research.

Background Large Language Models (LLMs) represent an ever-emerging and rapidly evolving generative artificial intelligence (AI) modality with promising developments in the field of medical education. LLMs can provide automated feedback services to medical trainees (i.e. medical students, residents, fellows, etc.) and possibly serve a role in medical imaging education. Aim This systematic review aims to comprehensively explore the current applications and educational outcomes of LLMs in providing automated feedback on medical imaging reports. Methods This study employs a comprehensive systematic review strategy, involving an extensive search of the literature (Pubmed, Scopus, Embase, and Cochrane), data extraction, and synthesis of the data. Conclusion This systematic review will highlight the best practices of LLM use in automated feedback of medical imaging reports and guide further development of these models.