Introduction and objective: Vitamin D is essential for overall health, and particularly of the musculoskeletal system, including teeth. Its deficiency is linked to many oral health issues, such as caries susceptibility, impaired odontogenesis, delayed teeth eruption, implant failure and...

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

Background. Despite their high global prevalence, depression and oral diseases are rarely addressed within a unified clinical framework. Recent evidence highlights a bidirectional relationship with overlapping biological and behavioral underpinnings.Aim. This review consolidates current knowledge on the mutual influence of depression and oral health and explores the underlying mechanisms.Methods. A structured search of PubMed/MEDLINE, EMBASE, PsycINFO, and the Cochrane Library identified systematic reviews, meta-analyses, observational, and mechanistic studies published between 2016 and 2025.Results. Depression is consistently linked to caries, periodontitis, tooth loss, and xerostomia, while periodontal inflammation may contribute to depressive symptomatology. Underlying mechanisms span neuroendocrine, immune, microbial, behavioral, pharmacological, and psychosocial domains. Conclusions. Recognizing depression and oral disease as mutually reinforcing conditions supports the case for integrated, interdisciplinary care models that incorporate routine cross-screening.

Background: Denture stomatitis is strongly associated with Candida biofilms on prosthetic surfaces and remains difficult to manage due to biofilm persistence and antifungal resistance. Selenium-based nanomaterials, particularly biogenic selenium nanoparticles (SeNPs) functionalized with natural polymers or phytochemicals, have emerged as potential material-centered strategies for biofilm control. Objective: To systematically evaluate the antifungal and antibiofilm effects of selenium-based nanomaterials on Candida biofilms in the context of denture materials. Methods: A systematic review was conducted in accordance with the PRISMA guidelines and registered in PROSPERO. Multiple databases were searched from inception without language restrictions. Eligible studies included experimental investigations of biogenic or functionalized SeNPs or organoselenium compounds targeting Candida biofilms on denture materials or in relevant in vitro models. A qualitative synthesis was performed due to anticipated heterogeneity. Results: Eleven studies met the inclusion criteria. Of these, four studies directly evaluated selenium-based interventions on denture materials, while seven provided supporting mechanistic evidence using in vitro models on non-denture substrates. Across denture-related studies, selenium-based modifications reduced fungal adhesion, biofilm biomass, and colony-forming units, without detrimental effects on material properties. Functionalization with polymers or phytochemicals was associated with enhanced antifungal activity and nanoparticle stability. Mechanistic studies suggested multimodal antifungal effects, including membrane disruption, inhibition of virulence factors, and modulation of biofilm-related pathways. Methodological quality was moderate, with common limitations in reporting and experimental standardization. Conclusions: Functionalized biogenic SeNPs show promising antifungal and antibiofilm activity against Candida in preclinical denture-related models. However, all available evidence is in vitro, with no in vivo or clinical studies identified. Substantial heterogeneity and limited long-term safety data preclude clinical recommendations. Further research should focus on standardized methodologies, clinically relevant in vivo models, and controlled clinical trials to assess translational potential.