Epigenetic modifications are heritable, reversible alterations that causally reshape chromatin architecture and thereby influence DNA repair without changing nucleotide sequence. DNA methylation, histone modifications and non-coding RNAs profoundly influence DNA repair mechanisms and genomic stability. Aberrant epigenetic patterns in cancer compromise DNA damage recognition and repair, therefore impairing homologous recombination (HR), non-homologous end joining (NHEJ), and base excision repair (BER) by suppressing key repair genes and lowering access to repair sites. Then it is dissected how loss-of-function mutations in Switch/Sucrose non-fermentable, imitation switch and CHD (Chromodomain helicase DNA-binding) chromatin-remodeling complexes impair nucleosome repositioning, preventing effective damage sensing and assembly of repair machinery. Non-coding RNAs contribute to epigenetic silencing at DNA break sites, exacerbating repair deficiencies. This review evaluates recent advances concerning epigenetic dysfunction and DNA repair impairment. It is also highlighted that nanoparticle-mediated delivery strategies are designed to overcome pharmacologic resistance. It is presented how epigenetic dysregulation of DNA repair can guide more effective and drug-resistant cancer therapies.

Background: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common malignancy worldwide and is associated with substantial morbidity and mortality. Although tobacco use, alcohol consumption, and human papillomavirus (HPV) infection remain the principal risk factors, growing evidence suggests that oral microbiome dysbiosis may contribute to tumor initiation, progression, and treatment response. Aim: This review summarizes current evidence linking oral microbiome dysbiosis with HNSCC, evaluates proposed biological mechanisms, and highlights major gaps requiring future investigation. Methods: A literature review was conducted by searching PubMed, Scopus, Web of Science, and Google Scholar for studies published through March 2026. Summary: Prospective cohort studies and case-control analyses have reported enrichment of periodontal-associated pathogens, including Fusobacterium nucleatum, Porphyromonas gingivalis, and Treponema denticola, in tumor tissues and saliva of patients with HNSCC, alongside depletion of commensal genera such as Streptococcus, Rothia, and Actinomyces. These organisms may influence carcinogenesis through NF-κB activation, Wnt/β-catenin signaling, epithelial–mesenchymal transition, apoptosis resistance, and immune modulation. Cancer therapies, particularly radiotherapy and chemotherapy, profoundly alter oral microbial communities and may worsen oral mucositis, candidiasis, and dental complications. Microbiome-targeted strategies, including probiotics, represent promising adjunctive approaches, although robust clinical validation is still required.

Introduction and purpose. Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a leading cause of ischaemic stroke, yet it frequently remains undiagnosed because of its paroxysmal and often asymptomatic nature. Artificial intelligence (AI), and in particular deep learning applied to the electrocardiogram (ECG), has emerged as a transformative approach both for detecting AF when the arrhythmia is present and for predicting future AF from an ECG recorded during sinus rhythm. The purpose of this narrative review is to summarise the current state of knowledge on AI-enabled ECG analysis for the detection and prediction of AF and to critically appraise its clinical readiness. Description of the state of knowledge. Convolutional neural networks can classify AF on single-lead and twelve-lead ECG with cardiologist-level accuracy (area under the curve, AUC, above 0.97 for rhythm classification) and can identify an "electrocardiographic signature" of AF on a normal sinus-rhythm tracing (AUC approximately 0.87). Consumer wearables using photoplethysmography and single-lead ECG achieve pooled sensitivity and specificity around 95%. Predictive models for incident AF reach AUC values of 0.78–0.85 and have been validated prospectively, enriching the diagnostic yield of screening. Nevertheless, false positives, unclassifiable tracings, limited external validity, and the absence of trials demonstrating a reduction in stroke temper enthusiasm. Summary. AI-ECG is a mature and accurate tool for AF detection and a promising instrument for AF prediction, but its translation into improved patient-relevant outcomes remains unproven. Rigorous external validation, outcome-driven randomised trials, and clear regulatory and implementation frameworks are required before routine clinical adoption. Keywords: Atrial Fibrillation; Artificial Intelligence; Deep Learning; Electrocardiography; Mass Screening; Stroke