Despite substantial advances in digital and computational pathology over the past decade, pragmatic and systematic guidance for translating AI models from research into clinical practice remains limited. Most existing frameworks offer only high-level recommendations on validation, regulatory compliance, and implementation, often lacking pathology-specific roadmaps aligned with laboratory standards. This review first summarizes current progress in digitalization and AI in pathology, with emphasis on key applications in biomarker quantification, molecular status prediction, and multimodality integration. We then outline nine major components essential for pathology AI clinical integration, from defining clinical needs through analytical and clinical validation, workflow interoperability, to health economics, and further summarize and compare eight available frameworks. Next, we introduce the AI Development and Optimization in Pathology for Translation (ADOPT) framework, a practical, phased deployment roadmap specifically designed to align with College of American Pathologists and Clinical Laboratory Improvement Amendments (CAP/CLIA) guidelines, to facilitate safe and effective clinical integration of pathology AI tools. We conclude by discussing future perspectives of digital and computational pathology in cancer care, emphasizing widespread clinical adoption of digitized pathology, effective multidisciplinary collaboration, and optimized pathologist-AI synergy to fully realize the promise of pathology AI.
UHRF1 and UHRF2 are multidomain epigenetic regulators that are paralogous in structure but slightly different in function. UHRF1 main function is to participate in replication coupled maintenance DNA methylation. UHRF1 recognizes hemi-methylated DNA and specific histone states, and it recruits and activates DNMT1 to newly replicated chromatin. This function is a key mechanism for faithful DNA methylation transmission and maintenance of cellular identity. In various cancers, abnormal UHRF1 activity has been linked to abnormal chromatin regulation, genome instability, tumour progression, and resistance to therapy. UHRF2 preferentially binds 5-hydroxymethylcytosine (5hmC), a cytosine modification that is particularly enriched in brain tissue and in differentiated post-mitotic cells. Through this recognition, UHRF2 engages chromatin and DNA repair factors, including XRCC1, linking 5hmC to both chromatin regulation and repair at hydroxymethylated sites. While the UHRF2-5hmC-XRCC1 axis is supported by direct biochemical evidence, its broader roles in neuronal regulation, memory, and development mainly on genetic models and context-dependent observations. Both proteins undergo post-translational modifications, including phosphorylation, ubiquitination, acetylation, and SUMOylation, which affect protein stability, localization, chromatin binding, and molecular activity. This review examines the domain architecture, DNA and histone recognition mechanisms, cell cycle regulation, genome maintenance functions, disease associations, and therapeutic relevance of UHRF1 and UHRF2. It focuses on the difference between UHRF1 associated epigenetic inheritance and UHRF2 associated hydroxymethylation dependent chromatin plasticity. The review also addresses novel therapeutic approaches such as domain selective inhibition, targeted protein degradation, and distinguishes preclinical results from clinically validated findings. These similarities and differences between the paralogs could help design more selective and biologically relevant epigenetic treatments.
Metabolic stress within one organ can have consequences far beyond its site of origin. In metabolic dysfunction-associated steatotic liver disease (MASLD), reactive carbonyl species, purine metabolites, and nitric oxide-related intermediates provide a basis for examining how metabolic disturbances may extend across tissues. Selected metabolites may act as systemic relay nodes when their production, persistence, and transport permit exposure beyond the source tissue. A broadly minutes-to-hours timescale offers a useful starting point for this hypothesis, while chemical form, reactivity, tissue uptake, and clearance determine the reach of each candidate. Plasma disappearance, whole-body elimination, intracellular persistence, and covalent-adduct persistence describe distinct processes that shape the interpretation of measured exposure. Mechanistic studies, analytical measurements, and clinical interventions together inform the links between local stress and distant effects. Emerging AI and multi-omics approaches may help characterize the resulting metabolic heterogeneity. Viewed through this framework, metabolite kinetics become a bridge between molecular mechanisms and candidate approaches to patient stratification, connecting the duration and distribution of exposure with potential sites of intervention.
The increasing prevalence of type 2 diabetes mellitus (T2DM) represents a significant public health challenge with serious implications for cancer incidence and treatment. Currently affecting over 400 million individuals worldwide, T2DM is characterized by chronic hyperglycemia and insulin resistance, which contribute to a pro-tumorigenic environment. This review seeks to elucidate the complex relationship between diabetes medications and cancer treatment, emphasizing how various agents, including metformin, sulfonylureas, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, thiazolidinediones (TZDs), and sodium–glucose cotransporter 2 (SGLT2) inhibitors, interact with critical signaling pathways involved in cancer progression. Evidence suggests that these medications can modulate oncogenic pathways such as AMPK/mTOR, insulin/IGF signaling, and Nuclear factor kappa B (NF-κB), leading to either inhibition or stimulation of tumor growth and survival depending on the drug class and biological context. For instance, metformin activates AMPK, inhibiting mTOR and promoting apoptosis, while TZDs exert anti-inflammatory effects that may reduce obesity-related cancer risks. Conversely, some DPP-4 inhibitors have been associated with pro-tumorigenic effects under certain conditions. Given the intricate interplay among diabetes, obesity, and cancer, understanding the effects of antidiabetic therapies on tumor biology is crucial for refining treatment strategies. This review consolidates current findings on the cellular and molecular impacts of major T2DM drug classes on cancer, highlighting their potential benefits and limitations. In conclusion, the findings presented in this analysis are intended to enhance treatment strategies and shape future research initiatives aimed at effectively addressing the complexities of managing patients who are concurrently affected by diabetes and cancer.
