Lasers in dentistry began to gain popularity in the 1990s. Lasers in dentistry are used as a treatment tool or as an adjunct tool. By using the laser in the field of dentistry, the main goal is to overcome the disadvantages, which are currently being experienced in conventional dental treatment procedures. Many specialties in dentistry including oral surgery, implants, oral medicine, periodontics, pediatrics, and operative use the current new laser technology. The ability of lasers to provide minimally invasive procedures with less discomfort to the patient has been useful in the patient delivery system in dental practice. This article describes in brief on the uses of lasers in oral mucosal lesions.

Short-chain fatty acids (SCFAs), primarily acetate (C2), propionate (C3), and butyrate (C4), are crucial microbial metabolites formed by the fermentation of dietary fibers by gut microbiota in the colon. These SCFAs, characterized by fewer than six carbon atoms, serve as an essential energy source for colonic epithelial cells and contribute approximately 10% of the body's total energy requirement. They are central to maintaining gut health through multiple mechanisms, including reinforcing intestinal barrier function, exerting anti-inflammatory effects, regulating glucose and lipid metabolism, and influencing host immune responses. Butyrate, in particular, plays a pivotal role in protecting the gut mucosa and modulating inflammatory processes. Beyond their intestinal functions, SCFAs affect systemic metabolic outcomes such as body weight regulation and appetite control by modulating the secretion of gut hormones and interacting with G-protein coupled receptors. Despite strong experimental evidence, mainly from animal models, clinical applications of SCFA modulation remain in preliminary stages, with limited translational findings in human therapeutics.In parallel, the oral microbiome also produces SCFAs, such as propionic, butyric, isobutyric, and isovaleric acids, as metabolic by-products in biofilm ecosystems like dental plaque. These acids contribute to interspecies communication, “cross-feeding” dynamics, and possibly biofilm stability or pathogenicity, especially in caries and periodontal disease. SCFAs in the oral cavity may act as signaling molecules or competitive factors, modulating microbial behavior and ecological balance within the oral niche. Collectively, these insights highlight SCFAs as integral to host-microbiota interactions, both in the gut and oral environments, with potential implications for targeted microbiome-based therapies in health and disease.

The current systematic review and meta-analysis was carried out to compare the diagnostic accuracy of pulp vitality and pulp sensibility tests in assessing pulpal health. PubMed/MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science, Google Scholar and Open Grey databases were searched and after assessing eligibility criteria the data were extracted. True-positive, false-positive, true-negative, false-negative, sensitivity and specificity values were extracted or calculated if not presented. Quality of studies was evaluated based on the QUADAS 2 tool. Meta-analysis was performed in MetaDTA (v2.0; Shinyapps, RStudio PBC, Boston, MA, USA) and Review Manager 5.3 (RevMan web; The Cochrane Collaboration, London, UK). Ten articles were included for qualitative synthesis and five for meta-analysis. The pooled diagnostic odds ratio for pulse oximeter (PO), electric pulp tester (EPT), cold test (CT) and heat test (HT) was 628.5, 10.75, 17.24 and 3.47, respectively. Pairwise comparison demonstrated a higher pooled mean sensitivity and specificity with PO compared with EPT. Comparison between PO and CT and between PO and HT also demonstrated a higher pooled mean sensitivity and specificity for PO. Summary points on receiver operating characteristic curves confirmed the ability of PO to correctly screen negatives in presenting patients as compared to EPT, CT and HT but no study was rated as good on quality assessment. PO can be considered as the most accurate diagnostic method as compared to EPT, CT and HT. This review provides information about the reliability and diagnostic accuracy of using pulp vitality and sensibility tests for assessing pulp status.

Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed.

Artificial intelligence (AI) has significantly transformed the diagnosis and treatment of dental caries, a prevalent issue in oral health care. Traditional diagnostic procedures such as eye inspection and radiography have limitations in detecting early-stage degradation. Artificial intelligence (AI) provides a viable alternative to improve diagnostic precision and effectiveness. This systematic review examines the diagnostic precision of artificial intelligence systems in identifying dental caries using X-ray images. The literature search utilized electronic web resources such as PubMed, Scopus, Web of Science, IEEE Explore, Google Scholar, Embase, and Cochrane. We conducted the search using specific MeSH key phrases and collected data up to January 2024. The QUADAS-2 assessment method was used to assess the risk of bias using a graph and a heat map. We conducted the statistical analysis using R v 4.3.1 software, which included the “meta,” “metafor,” “metaviz,” and “ggplot2” packages. We displayed the results using odds ratios (OR) and forest plots with a 95% confidence interval (CI). We used a comprehensive search approach in accordance with the PRISMA guidelines to find appropriate studies. The meta-analysis incorporates fourteen of the 21 articles included in this review. The research mostly uses convolutional neural networks (CNNs) for analyzing images, showing outstanding accuracy, sensitivity, and specificity in detecting caries. Significant variability in study results highlights the need for additional research to comprehend the components affecting AI effectiveness. Despite challenges in implementation and data availability, this systematic review provides essential information about AI and shows great potential caries detection, improve diagnostic consistency, and ultimately enhance patient care in dentistry.

Introduction: Mandibular first premolars are familiar with their varied root canal morphology, causing difficulties and challenges for successful endodontic procedures. This systematic review and meta-analysis aim to study the characterization of root and canal morphology of the first mandibular premolar using micro-computed tomography. Methodology: The literature search was conducted using electronic web databases like PubMed, Scopus, ScienceDirect and Cochrane with the chosen MeSH key words and data was retrieved until May 2023. Further to perform the statistical analysis, R v 4.3.1 software with "meta", 'metafor" "metaviz" " ggplot2" package was used, and results were represented by odds ratios (OR) and the percentage of forest plots along a 95 per cent confidence interval (CI). Results: The total number of studies meeting the inclusion criteria was 13; these studies were conducted on mandibular first premolar using Micro-CT; the total sample size was 1817. To scan the sample, an X-ray micro-focus CT system (Siemens Inveon CT, Erlangen, Germany) was used in four studies and seven different machines were used in the respective studies. Mimics 10.01 software (Materialize, Leuven, Belgium) and NRecon v.1.6.9 software (Bruker, Kontich, Belgium) were commonly operated. The minimum and maximum voxel size ranges between 11.94 and 50 μm. Vertucci’s classification was frequently used (9), while one study applied Ahmed et al. and Vertucci’s classification. Conclusion: This systematic review provides essential information about the root and canal configurations, radicular grooves, accessory canals, and apical foramina through Micro-CT, aiming to improve the accuracy of endodontic treatment and help practitioners.