Dental caries is a microbial disease, characterized by demineralization of inorganic content and dissolution of organic material which further leads to cavity formation. Histopathology of cavity formation shows sequence of events which has been mentioned in dental literature, but most of the time it is very difficult to understand by the patients. Therefore, author herself has designed 3D animation video as well as comics using dental caries model for better understanding of the same.

To systematically synthesise evidence on salivary inflammatory, fibrotic, matrix remodelling, molecular signalling, and transcriptomic biomarkers in oral submucous fibrosis (OSMF), with emphasis on expression patterns and reported diagnostic utility. This systematic review was conducted according to PRISMA guidelines, with relevant Preferred reporting systems for systematic reviews and meta analysis- diagnostic test accurracy (PRISMA-DTA) items considered for diagnostic outcomes. PubMed/MEDLINE, Scopus, Cochrane Library, and Google Scholar were searched up to 31 May 2026. Human observational studies evaluating salivary biomarkers in OSMF were included if OSMF-specific data were extractable. Studies assessing only non-salivary biomarkers, broad salivary chemistry, oxidative stress markers, trace elements, or mixed oral potentially malignant disorders (OPMDs) without separate OSMF data were excluded. Risk of bias was assessed using domain-based appraisal, and findings were synthesised narratively because meta-analysis was not appropriate. Ten independent studies were included. The biomarkers assessed included MMP-12, MMP-8, IL-1β, IL-8, IL-10, IL-17, TNF-α, TGF-β isoforms, epidermal growth factor receptor (EGFR), S100A7, S100P, OAZ1, SAT, and miRNA-21. MMP-12 showed the strongest individual diagnostic signal, with increased salivary levels in OSMF and reported receiver operating characteristic-based diagnostic utility. MMP-8 showed an inverse expression pattern. TGF-β isoforms and IL-17 demonstrated disease-associated changes, whereas IL-10, salivary IL-1β, and TNF-α showed limited or inconsistent discriminatory value. EGFR, S100A7, miRNA-21, and transcriptomic panels showed promising but preliminary findings. Salivary biomarkers show potential as adjunctive tools for OSMF assessment, particularly MMP-12, TGF-β isoforms, and multi-marker transcriptomic panels. However, current evidence remains low to very low in certainty and requires external validation before clinical use.

Oral squamous cell carcinoma (OSCC) accounts for more than 90% of oral malignancies globally and remains a major health burden in South and Southeast Asia. Emerging evidence implicates mitochondrial dysfunction and mitochondrial DNA (mtDNA) mutations in OSCC carcinogenesis, progression, and prognosis. This systematic review and meta-analysis aimed to determine the pooled prevalence of mtDNA mutations in OSCC, map recurrent mutational hotspots, and evaluate their diagnostic and prognostic relevance. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, PubMed, Scopus, Web of Science, and EMBASE were searched (2000–2025) for observational studies analysing somatic mtDNA mutations in OSCC. Risk of bias was assessed using the Joanna Briggs Institute (JBI) checklist for prevalence studies, and a DerSimonian–Laird random-effects proportional meta-analysis was performed. Subgroup analyses explored coding versus noncoding mutations, D-loop hotspots, and heteroplasmic versus homoplasmic patterns. Eight studies comprising 403 patients were included. The pooled prevalence of mtDNA mutations in OSCC was 88.1% (95% CI: 69.9–99.1). Individual study prevalence ranged from 26.7% (Yuan et al.) to 100% (Fendt, Kloss-Brandstätter, Mondal (b), and Palodhi). Heterogeneity was high (I² = 94.9%, Cochran’s Q = 137.97, P < 0.001; τ² = 0.3467). In the leave-one-out sensitivity analysis, the pooled prevalence ranged from 84.5% to 93.6%. These findings indicate that mtDNA mutations are highly prevalent in OSCC, although heterogeneity is considerable; larger standardised studies are needed to establish their diagnostic and prognostic value.

Oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs) are major challenges in global health. Convolutional neural network (CNN)-based deep learning and artificial intelligence (AI) models have demonstrated superior results when used automatically. However, no prior meta-analysis examines the pooled diagnostic performance of AI models. An electronic search was performed in MEDLINE via PubMed, Scopus, Web of Science, Springer Link, and Elsevier up to 30 June 2025, without a period restriction. The articles that investigated OSCC and OPMDs using AI and CNN-based deep learning on clinicopathological images were included for review. Quality assessment of articles was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A bivariate random-effects model was used to estimate the pooled sensitivity and specificity, I² values, receiver-operating characteristic (ROC) curves, and diagnostic odds ratios (DOR) were calculated using RevMan 5.4 and Open Meta-Analyst for Sierra software. Publication bias was evaluated using Deeks’ funnel plot. Only ten studies, totalling more than 47,124 clinical photos, satisfied the inclusion requirements out of the 175 original records that were found. Transfer learning is a common training method, and the majority of research employed CNN-based models for analysis, such as ResNet, EfficientNet, VGG, InceptionV3, and DenseNet. The results showed that the pooled diagnostic odds ratio was 87.43 (95% confidence interval [CI]: 42.18–181.26) with a pooled sensitivity of 0.86 (95% CI: 0.84–0.89), and pooled specificity of 0.92 (95% CI: 0.89–0.95). There is no statistically significant evidence of publication bias according to the Deeks funnel plot asymmetry test (P = 0.31).