The olive tree (Olea europaea L.) has been cultivated for millennia, with olive oil representing both a cornerstone of the Mediterranean diet and a major agricultural commodity. Its composition, rich in monounsaturated fatty acids, polyphenols, tocopherols and squalene, supports well-documented cardioprotective, antioxidant and anti-inflammatory benefits. Olive oil production generates substantial secondary streams, including pomace, leaves, pits and mill wastewater, which are rich in phenols, triterpenes and fibers. This review consolidates recent advances in their phytochemical characterization, innovative extraction technologies and health-promoting effects, while highlighting the economic and regulatory prospects for industrial adoption. Comparative analysis shows that olive leaves can produce up to 16,674.0–50,594.3 mg/kg total phenolics; oleuropein 4570.0–27,547.7 mg/kg, pomace retains 2.24 g GAE/100 g dried matrix (DM)total phenolics; oil 13.66% DM; protein 6.64% DM, and wastewater contains high concentration of phenolics content of olives. Innovative extraction techniques, such as ultrasound and microwave-assisted methods, allow for a recovery, while reducing solvent use and energy input. The analysis highlights opportunities for integrating these by-products into circular bioeconomy models, supporting the development of functional foods, nutraceutical applications and sustainable waste management. Future research should address techno-economic feasibility, regulatory harmonization and large-scale clinical validation to accelerate market translation.

Background: Diabetes mellitus is a heterogeneous chronic disease with an increasing global prevalence. In Romania, 11.6% of the population is affected, yet only 6.46% receive treatment. Among diabetic patients, 15–25% develop skin lesions that may progress to ulceration and necrosis, significantly impairing quality of life and increasing the risk of complications. Methods: We conducted a prospective study including 28 patients (14 in the control group and 14 in the intervention group) with type I or II diabetes and chronic ulcers of the calf or foot (>4 cm²). The control group received standard therapy with debridement, dressings, antibiotics when indicated, and local and systemic ozone therapy. The intervention group was treated with an Integrative Therapeutic Protocol combining ozone therapy, pulsed electromagnetic field therapy (PEMF), colon hydrotherapy with probiotic supplementation, and an anti-inflammatory alkaline diet. Wound healing (reduction in ulcer surface area) was the primary endpoint; secondary endpoints included changes in glycemia and inflammatory biomarkers. Results: After 8 weeks, the intervention group achieved 86.2% re-epithelialization versus 58.2% in controls (p < 0.01). Significant improvements were also observed in blood glucose level (−38%), HbA1c (−25%), CRP (−26%), and fibrinogen (−28%) relative to baseline, with differences versus controls reaching statistical significance. Conclusions: The Integrative Therapeutic Protocol accelerated wound healing and improved glycemic and inflammatory profiles compared with ozone therapy alone. Although an alkaline diet was recommended, adherence and its specific contribution were not objectively monitored; therefore, this component should be interpreted with caution.

Background: Chronic periodontitis is a prevalent inflammatory disease. It goes beyond the oral cavity, exerting systemic immunomodulatory effects through continuous low-grade inflammation, microbial dysbiosis, and cytokine spillover. Accumulating evidence suggests that the immunological consequences of periodontitis may influence systemic immune homeostasis and alter responses to cancer immunotherapies, specifically checkpoint blockade. Objectives: This narrative review describes how periodontal inflammation induces systemic immune reprogramming. It also investigates possible effects on the efficacy of immunotherapy. Methods: The paper synthesizes current findings on molecular and cellular mechanisms linking periodontitis to immune dysfunction. It underscores the mutual signaling pathways NF-κB, STAT3, and PD-1/PD-L1 that connect oral and systemic immunity. Results: Chronic periodontal inflammation reprograms innate and adaptive immune responses. It elevates proinflammatory mediators, such as IL-1β, IL-6, and TNF-α. It alters T-cell polarization and promotes myeloid cell “training”. This process may lead to immune exhaustion, impaired antigen presentation, and treatment resistance. Preclinical and new clinical data suggest that controlling periodontal inflammation may partially reduce systemic inflammatory burden, although clinical evidence in immunotherapy-treated cancer cohorts remains limited. Conclusions: Periodontal health should be considered in the management of immunotherapy. This can facilitate new studies that integrate oral and systemic immunology. Understanding the two-way link between periodontal inflammation and systemic immune reprogramming may offer fresh opportunities for personalized immunomodulation and combined interventions.

Background: Saliva has been identified as a valuable diagnostic biofluid due to its non-invasive collection and its capacity to reflect oral and systemic biological processes. Advances in analytical chemistry, biosensing technologies, and artificial intelligence (AI)-assisted data integration have broadened the applications of salivary diagnostics. Among salivary exposome components, heavy metals such as lead, cadmium, mercury, nickel, chromium, arsenic, and aluminum serve as biologically and clinically relevant indicators of environmental exposure, toxic burden, and disease-associated molecular disorders. Methods: This structured review integrates clinical, experimental, and translational studies published between January 2020 and January 2026 that examined salivary heavy metal profiling in relation to oral health. Evidence was identified using systematic searches of PubMed/MEDLINE and supplementary sources. Studies were qualitatively assessed regarding analytical methodologies, reported concentration ranges, biological mechanisms, disease associations, and the development of digital and AI-assisted diagnostic applications. Results: Thirteen human clinical studies and six animal or in vivo investigations met the inclusion criteria. Across these studies, altered salivary metal profiles were linked to oxidative stress, inflammatory signaling, immune dysregulation, microbiome disturbances, and genotoxic markers relevant to periodontal disease, oral mucosal pathology, and the risk of oral squamous cell carcinoma. Inductively coupled plasma mass spectrometry was the predominant analytical platform, while emerging biosensor technologies showed potential for rapid detection and monitoring. Digital and AI-based approaches were identified as promising tools for integrating metallomic data with clinical and molecular biomarkers to support exposure-informed risk stratification. Conclusions: Salivary heavy metal profiling represents a biologically informative, non-invasive method for exposure-aware risk assessment in oral health. Although current clinical translation is limited by methodological variability, small cohort sizes, and the lack of standardized reference ranges, integration with digital biosensing platforms and explainable AI frameworks might facilitate scalable, precision-oriented salivary diagnostics.

The global rise in cancer incidence and mortality represents a major challenge for modern healthcare. Although current screening programs rely mainly on histological or immunological biomarkers, cancer is a multifactorial disease in which biological, psychological, and behavioural determinants interact. Psychological dimensions such as stress, anxiety, and depression may influence vulnerability and disease evolution through neuro-endocrine, immune, and behavioural pathways, especially by affecting adherence to therapeutic recommendations. However, these dimensions remain underexplored in current screening workflows. This review synthesizes current evidence on the integration of biological markers (tumor and inflammatory biomarkers), psychometric profiling (stress, depression, anxiety, personality traits), and behavioural digital phenotyping (facial micro-expressions, vocal tone, gait/posture metrics) for potential early cancer risk evaluation. We examine recent advances in computational sciences and artificial intelligence that could enable multimodal signal harmonization, structured representation, and hybrid data fusion models. We discuss how structured computational information management may improve interpretability and may support future AI-assisted screening paradigms. Finally, we highlight the relevance of digital health infrastructure and telemedical platforms in strengthening accessibility, continuity of monitoring, and population-level screening coverage. Further empirical research is required to determine the true predictive contribution of psychological and behavioural modalities beyond established biological markers.