Inflammation serves as the primary driver of periodontal pathogenesis, yet it remains frequently neglected despite its profound impact on systemic health. Characterized by dysbiotic microbial accumulation, the disease triggers a persistent host immune response that inadvertently facilitates destruction of periodontium. Upregulated inflammatory markers, specially C-reactive protein and pro-inflammatory cytokines, accelerate proteolytic enzyme activity and collagen degradation, leading to irreversible alveolar bone loss. Conventional diagnostic tools offer only limited, static insights into the disease state, and often fail to provide a dynamic or patient-specific assessment. Consequently, there is a growing need for more accurate and personalized strategies in periodontal healthcare. This review explores the growing transition towards precision medicine paradigm that integrates molecular diagnosis with nanotechnology. Biomarkers referred to as the biological indicators are detectable in fluids like saliva, gingival crevicular fluid (GCF), and blood which can reflect early disease activity and inflammatory processes. These biomarkers can be implemented for the “smart” nano-delivery systems ensuring site-specific, sustained release of therapeutics to optimize therapeutic response. Engineered nanomaterials, including nanoparticles, nanofibers, biosensors, and dendrimer-based systems can target diseased areas directly while ensuring sustained release of medication, and promoting healing at the cellular level. The convergence of nanotechnology and biomarker research holds the potential for the development of personalised treatment protocols, facilitating a shift from generalized care for periodontal disease management.
Orthodontic miniscrews are widely used as temporary anchorage devices, yet failure rates ranging from 10 to 20 https://doi.org/10.17605/OSF.IO/M5CR2 ). Risk of bias was assessed using the QUIN tool for in vitro studies. In total, 639 records were screened following an updated search through may 2026, of which 17 in vitro studies were included. Nanoparticles assessed included zinc oxide, silver, titanium dioxide, hydroxyapatite, chlorhexidine hexametaphosphate, chitosan, and composite formulations.). Antimicrobial efficacy was assessed using inhibition zone diameter, colony-forming unit (CFU) counts, and percentage bacterial reduction against key oral pathogens including S. mutans, S. aureus, and P. gingivalis. Surface parameters evaluated were roughness, wettability, morphology (via SEM/TEM), and elemental composition (via XRD, XPS, EDS, and FTIR. The QUIN tool evaluation revealed that most studies (80
This systematic review aimed to evaluate different approaches for palatal donor areas of gingival grafts in the reduction of postoperative pain and to categorize protection/action methods for these donor sites. The review was conducted in accordance with PRISMA 2020; Cochrane handbook guidelines and registered in PROSPERO (CRD42023454193). Databases PubMed, Embase, Scopus, Web of Science and Cochrane Library were searched. Randomized controlled clinical trials (RCTs) comprising free gingival grafts (FGG) or de-epithelialized connective-tissue grafts (dSCTG) with data about postoperative pain (Visual analogue scale-VAS) were included. Risk of bias of included RCTs was assessed using RoB 2. A narrative and descriptive synthesis of data was performed. A total of 85 articles were initially identified for title screening, but 33 articles were selected for full-text reading. 28 studies met inclusion criteria for qualitative analysis (n=1187). Thirteen studies (46.4
Periodontitis can lead to the formation of intrabony defects. Enamel matrix derivative (EMD) is a well-established biomaterial for regeneration. Currently, hyaluronic acid (HA) has emerged as a promising alternative with anti-inflammatory and osteoinductive properties. This meta-analysis compares the clinical efficacy of HA and EMD in the regenerative treatment of periodontal intrabony defects. In December 2025, we conducted a systematic search of PubMed, Scopus, Web of Science, and the Cochrane Library for randomized and non-randomized controlled trials directly comparing HA to EMD in patients with periodontitis and intrabony defects. The primary outcomes were clinical attachment level (CAL) and probing pocket depth (PPD). For the meta-analysis, we used R 4.5.0 with R Studio 2024.12.1 + 563. We included four studies with a total of 167 defects in 156 patients. There were no statistically significant differences between HA and EMD in CAL at 6 months (MD = 0.08 mm, 95
Peri-implant diseases remain a major cause of late implant failure, and current risk assessment tools show limited capacity to integrate prosthetic factors, salivary biomarkers and artificial intelligence-based prediction. This study aimed to develop the Implant Success Prediction Tool (ISPT), a multifactorial peri-implant risk stratification system structurally designed for modular integration with artificial neural networks and salivary omics data. ISPT development followed three main pillars: (1) incorporation of Implant Disease Risk Assessment (IDRA)-validated clinical vectors, including bleeding on probing percentage, number of sites with probing depth ≥ 5 mm, bone loss in relation to age, periodontitis susceptibility, supportive periodontal therapy and hygiene/compliance parameters; (2) qualitative usability testing of IDRA by implantologists, who identified elements to maintain, clarify or expand; and (3) alignment with a precision medicine framework, establishing collaboration with a salivary diagnostics laboratory SalivaTec ( https://ciis.ucp.pt/salivatec ) to enable systematic saliva collection and future deep phenotyping. The final ISPT structure comprises ten standardized risk vectors displayed in a colour-coded radial traffic-light diagram, integrating six adapted IDRA-derived vectors and four novel vectors: abutment height/angulation; saliva collection/deep phenotyping vector (“salivaomics”); foreign bodies, titanium particles and tribocorrosion; and other for occlusal loading and functional risk. The tool is conceptually prepared to function as a structured input matrix for artificial neural networks, supporting longitudinal training with combined clinical and salivary data to predict implant outcomes (peri-implant health, mucositis, peri-implantitis) over a minimum 5-year monitoring period. ISPT represents the first peri-implant risk assessment tool explicitly designed for modular integration of artificial intelligence and salivary omics data within a precision dentistry framework. Its standardised vectors, traffic-light visualisation and longitudinal validation methodology provide a scalable structure for future externally validated predictive models of implant success and failure.
