Introduction: Salivary biomarkers have been extensively studied in relation to oral disease, such as periodontal disease, oral cancer, and dental caries, as well as systemic conditions including diabetes, cardiovascular diseases, and neurological disorders. Literature Review: A systematic literature review was conducted, analyzing recent advancements in salivary biomarker research. Databases such as PubMed, Scopus, and Web of Science were searched for relevant studies published in the last decade. The selection criteria included studies focusing on the identification, validation, and clinical application of salivary biomarkers in diagnosing oral and systemic diseases. Various detection techniques, including enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), mass spectrometry, and biosensor technologies, were reviewed to assess their effectiveness in biomarker analysis. Specific biomarkers, such as inflammatory cytokines, oxidative stress markers, and microRNAs, have been identified as reliable indicators of disease progression. Current Trends and Future Perspectives: Advances in proteomics, genomics, and metabolomics have significantly enhanced the ability to analyze salivary biomarkers with high sensitivity and specificity. Despite the promising findings, challenges remain in standardizing sample collection, processing, and analysis to ensure reproducibility and clinical applicability. Conclusions: Future research should focus on developing point-of-care diagnostic tools and integrating artificial intelligence to improve the predictive accuracy of salivary biomarkers.

Placing antimicrobial treatments directly in periodontal pockets is an example of the local administration of antimicrobial drugs to treat periodontitis. This method of therapy is advantageous since the drug concentration after application far surpasses the minimum inhibitory concentration (MIC) and lasts for a number of weeks. As a result, numerous local drug delivery systems (LDDSs) utilizing various antibiotics or antiseptics have been created. There is constant effort to develop novel formulations for the localized administration of periodontitis treatments, some of which have failed to show any efficacy while others show promise. Thus, future research should focus on the way LDDSs can be personalized in order to optimize future clinical protocols in periodontal therapy.

Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex datasets with speed and precision. Backgrounds and Objectives: This study aimed to evaluate the reliability of AI-assisted dental–periodontal diagnoses compared to diagnoses made by senior specialists, specialists, and general dentists. Material and Methods: A comparative study was conducted involving 60 practitioners divided into three groups—general dentists, specialists, and senior specialists—along with an AI diagnostic system (Planmeca Romexis 6.4.7.software). Participants evaluated six high-quality panoramic radiographic images representing various dental and periodontal conditions. Diagnoses were compared against a reference “gold standard” validated by a dental imaging expert and senior clinician. A statistical analysis was performed using SPSS 26.0, applying chi-square tests, ANOVA, and Bonferroni correction to ensure robust results. Results: AI’s consistency in identifying subtle conditions was comparable to that of senior specialists, while general dentists showed greater variability in their evaluations. The key findings revealed that AI and senior specialists consistently demonstrated the highest performance in detecting attachment loss and alveolar bone loss, with AI achieving a mean score of 6.12 in identifying teeth with attachment loss, compared to 5.43 for senior specialists, 4.58 for specialists, and 3.65 for general dentists. The ANOVA highlighted statistically significant differences between groups, particularly in the detection of attachment loss on the maxillary arch (F = 3.820, p = 0.014). Additionally, AI showed high consistency in detecting alveolar bone loss, with comparable performance to senior specialists. Conclusions: AI systems exhibit significant potential as reliable tools for dental and periodontal assessment, complementing the expertise of human practitioners. However, further validation in clinical settings is necessary to address limitations such as algorithmic bias and atypical cases. AI integration in dentistry can enhance diagnostic precision and patient outcomes while reducing variability in clinical assessments.

Anxiety and depression are highly prevalent mental health disorders often associated with dysregulation of neuroendocrine and immune systems, particularly the hypothalamic–pituitary–adrenal (HPA) axis and the sympathetic–adrenal–medullary (SAM) system. Recent research highlights the potential of salivary biomarkers to serve as non-invasive indicators for psychological distress. This narrative review synthesizes current evidence on key salivary biomarkers, cortisol, alpha-amylase (sAA), secretory immunoglobulin A (sIgA), chromogranin A (CgA), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), C-reactive protein (CRP), brain-derived neurotrophic factor (BDNF), and salivary microRNAs (miRNAs), in relation to anxiety, depression, and stress. A comprehensive literature search (2010–2025) was conducted using multiple databases and relevant MeSH terms. The review reveals consistent associations between these salivary analytes and stress-related disorders, reflecting changes in neuroendocrine activity, immune response, and neuroplasticity. Cortisol and sAA mirror acute stress reactivity, while cytokines and CRP indicate chronic inflammation. BDNF and miRNAs provide insight into neuroplastic dysfunction and gene regulation. Despite promising results, limitations such as variability in sampling methods and biomarker specificity remain. In conclusion, salivary biomarkers offer a promising avenue for early detection, monitoring, and personalization of treatment in mood and anxiety disorders. Conclusions: Cortisol and alpha-amylase serve as the principal markers of acute stress response, whereas cytokines such as IL-6 and TNF-α, together with CRP, indicate chronic inflammation associated with extended emotional distress.

Background: Oral health plays a critical role in systemic wellbeing, with growing evidence supporting strong associations between oral conditions and cardiovascular disease (CVD). These connections extend beyond periodontal disease and involve oral microbiota imbalance, systemic inflammation, and oral side effects of cardiovascular pharmacotherapy. Objective: To explore these links, a narrative literature review was performed using PubMed, Scopus, and ScienceDirect, covering studies published between 2000 and 2025. Methods: A comprehensive literature search was conducted in PubMed, Scopus, and ScienceDirect for studies published between January 2000 and May 2025. Both MeSH and free-text terms related to oral health, periodontal disease, systemic inflammation, endothelial dysfunction, and atherosclerosis were used. Eligible studies included observational and interventional research, systematic reviews, and meta-analyses. Key findings: The evidence consistently supports an association between chronic periodontal inflammation and cardiovascular risk, mediated by systemic dissemination of proinflammatory cytokines (IL-6, TNF-α, CRP) and microbial products that promote endothelial activation and atherogenesis. Interventional data indicate that periodontal therapy may reduce systemic inflammatory burden and improve vascular parameters, though heterogeneity across studies limits causal inference. Conclusions: Current findings highlight a significant oral–systemic connection through inflammatory and endothelial mechanisms. Strengthening interdisciplinary collaboration between dental and cardiovascular care providers is essential to translate this evidence into preventive and therapeutic practice. Further longitudinal and mechanistic studies are required to confirm causality and guide clinical integration.

This study explores the recent advances of and functional insights into hydrogel composites, materials that have gained significant attention for their versatile applications across various fields, including contemporary dentistry. Hydrogels, known for their high water content and biocompatibility, are inherently soft but often limited by mechanical fragility. Key areas of focus include the customization of hydrogel composites for biomedical applications, such as drug delivery systems, wound dressings, and tissue engineering scaffolds, where improved mechanical properties and bioactivity are critical. In dentistry, hydrogels are utilized for drug delivery systems targeting oral diseases, dental adhesives, and periodontal therapies due to their ability to adhere to the mucosa, provide localized treatment, and support tissue regeneration. Their unique properties, such as mucoadhesion, controlled drug release, and stimuli responsiveness, make them ideal candidates for treating oral conditions. This review highlights both experimental breakthroughs and theoretical insights into the structure–property relationships within hydrogel composites, aiming to guide future developments in the design and application of these multifunctional materials in dentistry. Ultimately, hydrogel composites represent a promising frontier for advancing materials science with far-reaching implications in healthcare, environmental technology, and beyond.