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Procyanidins, as a kind of dietary flavonoid, have excellent pharmacological properties, such as antioxidant, antibacterial, anti-inflammatory and anti-tumor properties, and so they can be used to treat various diseases, including Alzheimer’s disease, diabetes, rheumatoid arthritis, tumors, and obesity. Given the low bioavailability of procyanidins, great efforts have been made in drug delivery systems to address their limited use. Nowadays, the heavy burden of oral diseases such as dental caries, periodontitis, endodontic infections, etc., and their consequences on the patients’ quality of life indicate a strong need for developing effective therapies. Recent years, plenty of efforts are being made to develop more effective treatments. Therefore, this review summarized the latest researches on versatile effects and enhanced bioavailability of procyanidins resulting from innovative drug delivery systems, particularly focused on its potential against oral diseases.

The oral microbiome is an emerging field that has been a topic of discussion since the development of next generation sequencing and the implementation of the human microbiome project. This article reviews the current literature surrounding the oral microbiome, briefly highlighting most recent methods of microbiome characterization including cutting edge omics, databases for the microbiome, and areas with current gaps in knowledge. This article also describes reports on microorganisms contained in the oral microbiome which include viruses, archaea, fungi, and bacteria, and provides an in-depth analysis of their significant roles in tissue homeostasis. Finally, we detail key bacteria involved in oral disease, including oral cancer, and the current research surrounding their role in stimulation of inflammatory cytokines, the role of gingival crevicular fluid in periodontal disease, the creation of a network of interactions between microorganisms, the influence of the planktonic microbiome and cospecies biofilms, and the implications of antibiotic resistance. This paper provides a comprehensive literature analysis while also identifying gaps in knowledge to enable future studies to be conducted.

Digital tools have greatly improved the detection and diagnosis of oral and dental disorders like cancer and gum disease. Lip or oral cavity cancer is more likely to develop in those with potentially malignant oral disorders. A potentially malignant disorder (PMD) and debilitating condition of the oral mucosa, oral submucous fibrosis (OSMF), can have devastating effects on one’s quality of life. Incorporating deep learning into diagnosing conditions affecting the mouth and oral cavity is challenging. Mouth and Oral Diseases Classification using InceptionResNetV2 Method was established in the current study to identify diseases such as gangivostomatitis (Gum), canker sores (CaS), cold sores (CoS), oral lichen planus (OLP), oral thrush (OT), mouth cancer (MC), and oral cancer (OC). The new collection, termed \"Mouth and Oral Diseases\" (MOD), comprises seven distinct categories of data. Compared to state-of-the-art approaches, the proposed InceptionResNetV2 model’s 99.51% accuracy is significantly higher.

Molar-Incisor Hypomineralization (MIH) is a qualitative defect of enamel of unknown etiology, affecting one or more permanent molars and may include incisors. This condition is a clinical challenge and its prevalence is still uncertain given the recent increase in research. Thus, we aimed to comprehensively estimate the overall prevalence of MIH and associated characteristics. This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). We searched articles using PubMed, MEDLINE, CENTRAL, Web of Science, SciELO, LILACS and TRIP databases, until July 2021. Heterogeneity and publication bias were computed via I2 test statistics and Egger’s significance test, respectively. Random-effects meta-analysis of prevalence were processed. We used the Strength of Recommendation Taxonomy [SORT] to grading the strength of evidence. Overall, 116 observational studies were included, with one study with moderate methodological quality and the remaining of high methodological quality. Subgroup analysis confirmed an influence of not using the 2003 MIH case definition (p = 0.0066). The pooled prevalence of MIH was 13.5% (95% CI 12.0–15.1, I2 = 98.0%). Affected incisors were seen in 36.6% (95% CI 30.0–43.7, I2 = 92.5%) of the cases. Lastly, the prevalence of hypomineralization of the second primary molars was observed in 3.6% of the MIH cases (95% CI 1.9–6.8, I2 = 96.3%). America was the continent with highest prevalence (15.3, 95% CI 12.8–18.3, p < 0.001, I2 = 96.3%) and Asia had the lowest prevalence (10.7, 95% CI 8.5–13.5, p < 0.001, I2 = 98.7%), however no continental differences were found. Sample size and year of publication were slight contributing factors to the heterogeneity in the analysis. Overall, these results were classified with a SORT A recommendation.

Automated oral disease detection systems face significant challenges from degraded radiographic imaging quality and limited pathological training data, particularly for rare conditions in public health screening environments. We introduce DentoSMART-LDM, the first framework to integrate metaheuristic optimization with latent diffusion models for dental imaging, featuring a novel Dynamic Self-Adaptive Multi-objective Metaheuristic Algorithm for Radiographic Tooth enhancement (DSMART) combined with a specialized pathology-aware Latent Diffusion Model (DentoLDM). Our pioneering DSMART algorithm represents the first metaheuristic approach specifically designed for dental radiographic enhancement, treating optimization as a multi-objective problem that simultaneously balances five dental quality indices through adaptive search mechanisms, while our innovative DentoLDM introduces the first pathology-specific attention mechanisms that preserve diagnostic integrity during synthetic data generation. This groundbreaking dual-component architecture addresses both image degradation and data scarcity simultaneously – a capability unprecedented in existing dental AI systems. For the first time in dental imaging research, we demonstrate adaptive optimization that dynamically adjusts processing intensity based on anatomical characteristics including bone density variations, soft tissue artifacts, and metallic restoration interference. Evaluated on the OralPath Dataset comprising 25,000 high-resolution dental radiographs across 12 pathological conditions with comprehensive external validation across seven independent clinical datasets (82,300 images), DentoSMART-LDM achieved superior performance with SSIM of 0.941 ± 0.023 and PSNR of 34.82 ± 1.47 dB, representing statistically significant improvements of 9.0% and 11.5% respectively compared to competing methods ( p  < 0.001). Diagnostic models trained on DentoSMART-LDM enhanced datasets achieved 97.3 ± 0.18% overall accuracy (95% CI: 97.09–97.51%), maintaining 87.7 ± 0.8% average accuracy across diverse clinical settings under natural class imbalance conditions. Blinded expert assessment by 20 board-certified oral pathologists revealed significant improvements in diagnostic accuracy (+ 17.4%, 95% CI: 15.8–19.0%) and expert confidence (+ 23.4%, p  < 0.001), while few-shot learning evaluation demonstrated exceptional performance with only 2 samples per pathology (89.2 ± 1.7% accuracy). This novel integration of multi-objective metaheuristic optimization with medical generative models represents a paradigm shift in dental AI, offering the first comprehensive solution that balances enhancement quality, diagnostic preservation, and computational efficiency while providing unprecedented few-shot learning capabilities for rare oral pathologies in underserved communities.

In recent years, salivary metabolome studies have provided new biological information and salivary biomarkers to diagnose different diseases at early stages. The saliva in the oral cavity is influenced by many factors that are reflected in the salivary metabolite profile. Oral microbes can alter the salivary metabolite profile and may express oral inflammation or oral diseases. The released microbial metabolites in the saliva represent the altered biochemical pathways in the oral cavity. This review highlights the oral microbial profile and microbial metabolites released in saliva and its use as a diagnostic biofluid for different oral diseases. The importance of salivary metabolites produced by oral microbes as risk factors for oral diseases and their possible relationship in oral carcinogenesis is discussed.

C-reactive protein (CRP) and estimated glomerular filtration rate (eGFR) are key biomarkers reflecting systemic inflammation and metabolic dysfunction. This study explored systemic and oral health indicators, including CRP and eGFR, as potential factors associated with periodontitis, using a longitudinal clinical dataset comprising 23,742 records from patients identified by ICD-10 codes between 2015 and 2022. Univariate Cox analysis and Gompertz models, selected based on AIC and BIC after evaluating alternative models, were employed to assess the predictive roles of CRP and eGFR in periodontitis incidence, adjusting for oral and systemic health factors. Elevated CRP (>15 mg/L) and reduced eGFR (<60 mL/min/1.73 m2) were significant predictors of periodontitis, with hazard ratios (HR) of 1.36 [1.05–1.77] and 1.39 [1.08–1.78], respectively. Atherosclerosis (HR: 2.12 [1.11–4.06]), diseases of the hard tissues of the teeth (HR: 7.30 [5.45–9.78]), and disorders of the teeth and supporting structures (HR: 3.02 [2.05–4.43]) also demonstrated strong predictive associations. CRP and eGFR emerged as potential biomarkers for predicting periodontitis, enabling early interventions to prevent tooth loss and systemic complications. Patients with chronic kidney disease, atherosclerotic heart disease, and lipid metabolism disorders are at higher risk, emphasizing the need for integrated care addressing both systemic and oral health factors.

The oral cavity is a unique complex ecosystem colonized with huge numbers of microorganism species. Oral cavities are closely associated with oral health and sequentially with systemic health. Many factors might cause the shift of composition of oral microbiota, thus leading to the dysbiosis of oral micro-environment and oral infectious diseases. Local therapies and dental hygiene procedures are the main kinds of treatment. Currently, oral drug delivery systems (DDS) have drawn great attention, and are considered as important adjuvant therapy for oral infectious diseases. DDS are devices that could transport and release the therapeutic drugs or bioactive agents to a certain site and a certain rate in vivo. They could significantly increase the therapeutic effect and reduce the side effect compared with traditional medicine. In the review, emerging recent applications of DDS in the treatment for oral infectious diseases have been summarized, including dental caries, periodontitis, peri-implantitis and oral candidiasis. Furthermore, oral stimuli-responsive DDS, also known as “smart” DDS, have been reported recently, which could react to oral environment and provide more accurate drug delivery or release. In this article, oral smart DDS have also been reviewed. The limits have been discussed, and the research potential demonstrates good prospects.

Saliva is an interesting alternative diagnostic body fluid with several specific advantages over blood. These include non-invasive and easy collection and related possibility to do repeated sampling. One of the obstacles that hinders the wider use of saliva for diagnosis and monitoring of systemic diseases is its composition, which is affected by local oral status. However, this issue makes saliva very interesting for clinical biochemistry of oral diseases. Periodontitis, caries, oral precancerosis, and other local oral pathologies are associated with oxidative stress. Several markers of lipid peroxidation, protein oxidation and DNA damage induced by reactive oxygen species can be measured in saliva. Clinical studies have shown an association with oral pathologies at least for some of the established salivary markers of oxidative stress. This association is currently limited to the population level and none of the widely used markers can be applied for individual diagnostics. Oxidative stress seems to be of local oral origin, but it is currently unclear whether it is caused by an overproduction of reactive oxygen species due to inflammation or by the lack of antioxidants. Interventional studies, both, in experimental animals as well as humans indicate that antioxidant treatment could prevent or slow-down the progress of periodontitis. This makes the potential clinical use of salivary markers of oxidative stress even more attractive. This review summarizes basic information on the most commonly used salivary markers of oxidative damage, antioxidant status, and carbonyl stress and the studies analyzing these markers in patients with caries or periodontitis.

The oral microbiome functions as an intricate and coordinated microbial network, residing throughout the oral cavity in both health and disease. Although most oral microbiome research has focused on bacteria, there is a growing interest in oral fungal communities, known as the oral mycobiome. The oral cavity hosts a complex and diverse mycobiome comprising of an estimated 100 fungal species; however, the roles of these fungi have been largely overlooked and remain insufficiently characterized, particularly in children. This represents a critical gap, as early life is a key window for establishing oral microbial communities that shape lifelong oral and systemic health and offer opportunities for early intervention. Recent technological advances, especially next-generation sequencing, have enabled the identification of new fungal species and deepened our understanding of the diversity, structure, and interactions among fungal, bacterial, and other components within the oral cavity. Yet, research on the pediatric oral mycobiome remains fragmented and limited in scope. Addressing this gap is important since the early-life oral mycobiome may play an underappreciated role in shaping immune development, influencing susceptibility to oral diseases, and potentially contributing to systemic conditions during childhood and beyond. In this review, we examine the oral mycobiome in children, focusing on its formation and dynamics in health and in disease, including dental caries, periodontal disease, endodontic infection, and cleft lip/palate, and exploring its connections to several systemic consequences. By synthesizing current findings on fungal-related biological risk factors, we aim to inform the development of improved diagnostic tools and to guide the advancement of preventive and therapeutic strategies from fungal perspective.