Search Results (325)

In post-mortem genetic identification, teeth represent one of the most reliable sources of nuclear DNA when other biological samples are unavailable, owing to their ability to protect genetic material from autolysis, microbial degradation, and environmental insults. The present study describes a forensic veterinary case report involving a Staffordshire Bull Terrier litter in which the appearance of blue-coated offspring from a phenotypically black sire and a blue dam prompted parentage verification. The sire died before formal ante-mortem sampling could be carried out. In accordance with Regulation (EC) No. 1069/2009, Article 15, the carcass was interred in the owner’s garden. Before burial, two teeth were collected as the sole biological material available for identification by the official veterinarian, ensuring an unambiguous chain of custody from death certification to laboratory analysis. DNA extraction was performed using two independent protocols: Method A, a silica-membrane-based purification (QIAamp DNA Kit, Qiagen), applied to dental pulp, and Method B, a decalcification-based workflow (T-Bone Ex Kit) followed by DNA extraction using EZ2 Connect Instruments (Qiagen). Quantification of the DNA samples obtained were done by fluorometry. Short Tandem Repeat (STR) genotyping was performed using the Canine Genotypes™ Panel 1.1 by Applied Biosystems (Thermo Fisher Scientific, Waltham, MA, USA) targeting the 18 autosomal ISAG-recommended microsatellite loci. Parentage assignments were carried out against reference STR profiles obtained from peripheral blood of the dam and offspring. The two methods, considering a different biological matrix and different operating conditions, allowed us to obtain the STR profile, but Method B showed quality electropherograms, improving the confidence and interpretation of all the markers analyzed. The differences observed between the two approaches may reflect a combination of factors, including the different dental matrices analysed, the quantity and quality of the starting material, tooth identity and tissue preservation, and differences inherent to the extraction procedures themselves; therefore, in this case, the more complete and clearer STR profile obtained with Method B should be considered a case-specific observation rather than evidence of a direct effect of the extraction method. Overall, regardless of the DNA extraction method used, the results did not exclude the biological paternity of the blue-mantled offspring. Furthermore, this case report provides practical methodological guidance for DNA recovery from canine teeth and supports their use as a reliable source of post-mortem DNA for STR-based parentage testing. Full article

Background/Objectives: Dental implants are a reliable treatment for tooth loss, but identifying the implant brand when patient records are unavailable remains a clinical challenge that complicates prosthetic repair and complication management. This study aimed to develop and evaluate a deep learning-based system for automated identification of dental implant brands from panoramic and periapical radiographs. Methods: In this retrospective study, anonymized radiographs containing implants of twelve known brands were obtained from the archives of Necmettin Erbakan University Faculty of Dentistry. A two-stage pipeline was employed: a YOLOv11 detector first localized and cropped the implant regions, after which an EfficientNetV2-M convolutional neural network, fine-tuned via transfer learning, classified the implant brand. Class imbalance was addressed through offline and online data augmentation. Results: On the held-out test set of 531 implant crops spanning twelve brands, the classifier achieved an overall accuracy of 96.23% (95% CI 94.5–97.7%), a macro-averaged F1-score of 0.953, and a macro-averaged ROC-AUC of 0.991; the complete pipeline evaluated end to end on detector-predicted crops reached 96.0% match-conditional implant-level accuracy, corresponding to a precision-aware end-to-end identification F1-score of 88.2% (precision 81.6%, recall 96.0%) when all predicted boxes, including false detections, were counted. Grad-CAM analysis, including misclassified and low-confidence cases, indicated that predictions were based on clinically meaningful implant morphology. Conclusions: These findings indicate that the proposed two-stage approach provides accurate and interpretable implant brand identification, supporting its potential as a clinical decision support tool. Full article

Background: Pemphigus is a rare group of autoimmune blistering diseases characterized by autoantibody-mediated loss of keratinocyte adhesion, resulting in intraepithelial blister formation involving the skin and mucous membranes. Pemphigus Vulgaris (PV) is the most common type of pemphigus, which usually presents in adulthood and has a prevalence rate of about 2.83 cases per million person years worldwide. The prevalence rate in children and adolescents is relatively uncommon; however, it accounts for about 1.4 to 3.7 percent of all cases of pemphigus vulgaris reported worldwide. The involvement of oral cavity is clinically significant since it can present prior to the disease or as its predominant feature. Hence, this narrative review seeks to review literature on pemphigus with involvement of oral cavity in children and adolescents. Objective: The objective of this study was to review the current literature regarding epidemiology, pathogenesis, clinical presentation, diagnosis, histopathological characteristics, treatment, and outcomes of pemphigus with oral involvement in children and adolescents. Materials and Methods: A literature search was conducted using the PubMed/MEDLINE, Scopus, and Cochrane Library databases to identify relevant studies published between 2015 and 2026. The search strategy included terms related to pemphigus, pediatric patients, and oral manifestations. Articles involving patients younger than 18 years of age with oral involvement were screened according to predefined inclusion criteria. Following database screening, duplicate removal, and full-text assessment, 25 studies comprising a total of 51 patients with documented oral or orofacial involvement were included in the final review. Results: Pemphigus vulgaris was the predominant subtype, with oral lesions representing the initial or sole manifestation in the majority of patients. The gingiva, mucosa, tongue, lip, and palate were the most common affected areas in the mouth. The lesions in these areas usually appeared as painful erosion, ulcers, and desquamative gingivitis. Histopathology with the presence of acantholysis in the suprabasal area and direct immunofluorescence was the most definitive test for diagnosis. Systemic corticosteroids were the mainstay of treatment in conjunction with steroid sparing agents in some cases, with good results in difficult cases using rituximab. Overall, most patients achieved partial or complete clinical remission following appropriate treatment, although relapses were occasionally reported. Conclusions: Pemphigus in children and adolescents is rarely encountered; however, it should be included as a differential diagnosis of erosive/ulcerative lesions. Early identification, diagnosis, and treatment through a collaborative approach from dental practitioners are critical. Further research is required at multiple centers to gain a better understanding of the disease and to develop evidence-based guidelines for diagnosis and treatment of pediatric patients. Full article

Artificial intelligence (AI) is increasingly being incorporated into oral healthcare and dental education, yet the quality and effectiveness of randomized evidence supporting these interventions remain uncertain. We systematically reviewed randomized controlled trials (RCTs) evaluating AI-based interventions in oral healthcare and dental education. A comprehensive literature search was conducted in PubMed, Embase, ClinicalTrials.gov, and OpenGrey from database inception to 30 June 2026. Twenty-eight published RCTs involving 2306 participants and 26 registered unpublished RCTs were identified. Educational applications represented the largest category (53.6%), followed by diagnosis and clinical decision support, treatment planning, and disease monitoring. Across the included RCTs, AI-based interventions were more frequently associated with improvements in educational performance, diagnostic accuracy, treatment planning, clinical decision-making, and periodontal monitoring than conventional approaches, although findings were heterogeneous, several studies reported no significant differences, and a small number favored conventional methods. No serious adverse events were reported, but safety reporting was inconsistent. Most published RCTs presented some concerns regarding risk of bias (82.1%), primarily related to randomization, deviations from intended interventions, and selective reporting. APPRAISE-AI demonstrated predominantly moderate methodological quality (median score 44%), with recurrent weaknesses in robustness, reproducibility, and data quality. The identification of 26 registered RCTs without published results also suggests a potential risk of dissemination bias. These findings indicate that AI-based interventions show promise across multiple applications in oral healthcare and dental education, but improvements in trial methodology, reporting quality, and transparency are needed to strengthen the evidence base and support clinical implementation. Full article

Background/Objectives: Human teeth naturally fluoresce due primarily to organic components within dentine, contributing to their vitality and appearance under ultraviolet (UV) light. Contemporary resin composites increasingly incorporate fluorescent agents to reproduce this property and improve aesthetic integration with natural dentition. This narrative review examines the fluorescence characteristics of natural teeth and direct resin composites, evaluates methods used to assess dental fluorescence, and identifies factors influencing fluorescence stability and biomimetic performance. Methods: Peer-reviewed studies published between 2004 and 2025 investigating fluorescence in human teeth and direct resin-based composites under UV, visible or infrared illumination were reviewed. Studies focused on indirect restorative materials, non-human specimens or caries detection alone were excluded. Results: Natural teeth generally exhibit fluorescence emission peaks between 410 and 500 nm, with dentine demonstrating greater fluorescence intensity than enamel. Composite resins commonly incorporate rare-earth oxide fluorophores and often display similar emission wavelengths (approximately 450–485 nm), although fluorescence intensity varies considerably among brands and shades. Material fluorescence is influenced by composition, aging and environmental exposure. Spectrofluorometry provides highly accurate quantitative assessment, whereas photographic and quantitative light-induced fluorescence (QLF) methods offer greater clinical applicability. Despite advances in fluorescence mimicry, variability among contemporary materials generally continues to permit differentiation from natural tooth. Conclusions: Although substantial progress has been made in reproducing natural tooth fluorescence, significant variability and temporal instability remain among contemporary composite materials. Fluorescence-based methods therefore remain useful for restoration identification. However, continued improvements in biomimetic performance may reduce their future effectiveness. Greater standardisation of fluorescence assessment and investigation of alternative detection approaches are needed to support clinical and forensic applications. Full article

Reliable personal identification remains a major challenge in forensic medicine, particularly in cases involving decomposition, thermal injury, or absence of DNA and dental records. In this context, anatomically protected and morphologically unique structures such as the sphenoid sinus may serve as valuable biometric markers. The aim of this study was to evaluate the forensic applicability of sphenoid sinus morphology using computed tomography (CT), automated segmentation, and three-dimensional (3D) computational analysis. The proposed framework integrates CT-based image preprocessing, automated 3D segmentation using nnU-Net architecture, and geometric comparison of reconstructed sphenoid sinus models through point cloud analysis and deep learning approaches. Morphological variability, spatial configuration, and structural stability of the sphenoid sinus were analyzed as discriminative biometric features. The GSA-Net–based identification model demonstrated high recognition performance, achieving Top-1 accuracies exceeding 97.8% and Top-3 accuracies up to 100% in controlled datasets. The results support the concept that the sphenoid sinus possesses sufficient individuality and anatomical preservation to enable reliable ante-mortem and post-mortem identification. The study highlights the potential of integrating automated segmentation and AI-driven 3D analysis into forensic workflows and emphasizes the importance of standardized imaging protocols and larger annotated datasets for future clinical and forensic implementation. Full article

Background and Objectives: This study aimed to investigate the prevalence and severity of dental erosion using the Basic Erosive Wear Examination (BEWE) index in an adult cohort, exploring its associations with systemic health, gastrointestinal symptoms, and dietary habits. Materials and Methods: An observational cross-sectional study was conducted with 170 adult patients at a university clinic in Cluj-Napoca, Romania, between January and June 2026. Participants completed a standardized questionnaire detailing general characteristics, diet, systemic conditions, and symptoms. Dental erosion was clinically recorded via the highest BEWE score per sextant. Chairside tests evaluated unstimulated salivary pH and buffering capacity. Data were analyzed using chi-square tests and ordinal logistic regression. Results: Most patients exhibited low BEWE risk. Bivariate analyses revealed significant associations between elevated BEWE risk and advanced age, GERD, eating disorders, and carbonated beverage consumption. However, in the adjusted ordinal logistic regression model, only advanced age, GERD, digestive symptoms (difficulty swallowing, digestive burns), and specific medications (anti-asthma drugs, aspirin) remained independent predictors of increased erosive wear risk. Conclusions: Dental erosion is a multifactorial condition strongly shaped by advanced age, GERD, eating disorders, and carbonated beverage intake. A comprehensive risk-based approach incorporating medical histories, symptom screening, and dietary counseling is essential for early clinical identification and prevention. Full article

Background: Dental caries detection using intraoral radiographs is essential for early diagnosis but may be affected by observer variability. Artificial intelligence (AI)-based systems have emerged as potential tools to improve diagnostic consistency. This study evaluated the diagnostic accuracy of a deep learning-based AI system (Better Diagnostics Caries Assist (BDCA) Version 1.0) for detecting dental caries on bitewing (BW) and intraoral periapical (IOPA) radiographs, and examined its performance across demographic, technical, and lesion-based subgroups. Methods: A retrospective validation study was conducted using anonymized digital BW and IOPA radiographs with expert-defined ground truth at the tooth-surface level. The AI software independently analyzed each image to identify carious lesions. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with 95% confidence intervals. Generalized estimating equations were applied to adjust for the clustering of multiple surfaces per image. Subgroup analyses were performed by age, sex, digital sensor type, and lesion category. Results: The AI system demonstrated high diagnostic accuracy for both modalities. For BW radiographs, sensitivity was 0.892 and specificity was 0.995, while for IOPA radiographs sensitivity was 0.882 and specificity was 0.991. NPVs exceeded 0.99 for both modalities. Across age groups, BW sensitivity ranged from 0.881 to 0.901 and IOPA sensitivity from 0.854 to 0.906, with consistently high specificity (>0.989). Sex-based differences were minimal. Sensor-wise analysis showed sensitivity ranging from 0.833 to 0.933 for BW and 0.828 to 0.933 for IOPA, while specificity remained above 0.984 for all sensors. Detection performance was comparable for primary (sensitivity 0.883) and secondary caries (0.879), although PPV was slightly lower for secondary lesions. The lower AUC indicated reduced accuracy in lesion identification in the absence of BDCA v 1.0, the difference between BDCA v1.0 and Ground truth was 0.042 at 95% CI 0.030 to 0.055, and the difference was also statistically significant with (p ≤ 0.001). Conclusions: The evaluated AI system demonstrated excellent and consistent performance for detecting dental caries on both BW and IOPA radiographs across demographic groups, sensor technologies, and lesion types, supporting its potential role as a reliable decision support tool in dental radiographic interpretation. Full article

The oral cavity represents an extremely complex and dynamic microbial ecosystem capable of rapidly adapting to antimicrobial stress. These characteristics make it a promising environment for the identification of novel bioactive molecules with therapeutic potential. At the same time, the increasing prevalence of resistant pathogens in dental and oral-maxillofacial infections highlights the limitations of current therapeutic strategies and the urgent need for new effective antibacterial agents. This review examines the central role of advanced analytical techniques, with particular emphasis on mass spectrometry, in the discovery and characterization of bioactive metabolites and biomarkers relevant to future antibacterial discovery and to the understanding of biological responses to pathogens or therapeutic interventions. It discusses how the integration of metabolomics approaches, imaging mass spectrometry, and bioinformatics platforms is transforming the antibacterial discovery process by accelerating the identification of active compounds and improving the understanding of microbial interactions within the oral cavity. Overall, this work provides an up-to-date overview of current knowledge regarding the oral microbiome as a source of bioactive molecules and biomarkers. It highlights how emerging analytical technologies are opening new perspectives for the development of innovative therapeutic strategies against resistant oral infections. Full article

Comparative medical imaging is a well-established scientific method of personal identification that utilizes concordant anatomical features observed in antemortem and postmortem medical images. As access to antemortem medical imaging and digital image archives has expanded, radiographic comparison has become an increasingly important component of medicolegal death investigations. Despite its widespread use, many medicolegal practitioners receive limited formal training in comparative medical imaging. This paper provides a practical primer on radiographic comparison for personal identification. Common imaging modalities encountered in forensic casework, including conventional radiography, dental radiography, computed tomography (CT), cone beam computed tomography (CBCT), and magnetic resonance imaging (MRI), are reviewed with emphasis on image acquisition and characteristics, limitations, and comparative considerations. Frequently utilized anatomical regions, common comparative features, and methodological considerations are also discussed. Accurate radiographic identification requires not only the recognition of concordant anatomical features but also the appropriate interpretation of explainable differences arising from biological, radiographic, and postmortem factors. This paper provides foundational guidance intended to assist practitioners in conducting scientifically defensible radiographic comparisons and highlights the need for greater incorporation of comparative medical imaging training within forensic curricula. Full article

Background/Objectives: Damage to the contents of the mandibular canal is of great concern during endodontological procedures, particularly in the region of the mandibular molar teeth due to their proximity to the mandibular canal. The objectives of this study were to assess variations in the morphology and dimensions of the mandibular canal between the sexes in a Black South African population. Materials and Methods: CBCT scans of 30 mandibles were analysed bilaterally to determine the presence of an anterior loop and/or bifid canals in the sagittal view. The distance between the root apices of the first and second mandibular molars and the mandibular canal were determined using the coronal CBCT view. The distance between the mandibular canal and the buccal and lingual cortical plates were also measured. Results: Anterior loops were identified in 11.67% of cases, while bifid mandibular canals were present in 5% of cases. The mean distance between the mandibular canal ranged between 5.45 and 6.71 mm for the first molar mesial and distal roots and between 2.37 and 3.62 mm for the second molar roots. The mean distance between the mandibular canal and the lingual cortical plate ranged between 1.75 and 2.84 mm for first molars and 1.40–1.91 mm for second molars. The buccal cortical plate distances ranged from 3.14 to 4.97 mm and 5.21–5.75 mm for the first and second molars, respectively. No statistically significant sex differences were demonstrated for the morphometric measurements. Conclusions: The observed mandibular canal variations between populations and sexes highlight the need for patient-specific planning prior to oral dental procedures. Imaging modalities, such as CBCT scans, are essential for accurate measurements between landmarks and the identification of anatomical variations. These findings provide preliminary insights into mandibular canal anatomy in a Black South African sample that should be considered in dental procedures. No significant sex differences were observed; however, these findings should be interpreted with caution given the study’s limited statistical power. These findings cannot yet be generalised or extrapolated to the Black South African population. Full article

Background/Objective: Artificial intelligence (AI) is increasingly transforming healthcare and has emerged as a promising tool in pediatric dentistry. This narrative review examines the methodological foundations, current applications, limitations, and future directions of AI in pediatric dental practice. Methods: A structured literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar for relevant English-language publications from database inception through to May 2026, using predefined search terms and eligibility criteria. Following screening and full-text assessment, 72 unique publications were retained for the narrative synthesis. Results: AI applications in pediatric dentistry include early childhood caries detection and risk prediction, dental plaque assessment, identification of mesiodens and supernumerary teeth, dental age estimation, automated tooth detection, fissure-sealant evaluation, and craniofacial growth prediction. Several imaging-based models demonstrated performance comparable to experienced clinicians. However, many studies relied on retrospective, single-center datasets and lacked external or prospective validation. Additional concerns included algorithmic bias, data privacy, limited interpretability, and infrastructure requirements. Conclusions: AI has considerable potential to improve diagnostic efficiency, consistency, and personalized preventive care in pediatric dentistry. However, it should be used as a clinical decision-support tool rather than a replacement for professional judgment. Future research should prioritize prospective multicenter validation, multimodal and explainable systems, standardized reporting, and ethical clinical implementation. Full article

Pediatric sleep-disordered breathing (SDB) is underdiagnosed despite its associations with adverse neurobehavioral, psychosocial, and cardiometabolic outcomes. Pediatric dental providers routinely evaluate craniofacial growth and maintain longitudinal contact with children, yet practical approaches for integrating SDB risk assessment into dental settings remain limited. This protocol describes a prospective, cross-sectional observational study that will enroll 60 school-aged children aged 8–13 years from a university-based pediatric dental clinic and classify them as low-risk or high-risk for SDB using the Pediatric Sleep Questionnaire (PSQ; threshold ≥ 0.33). The primary aim is to examine associations between PSQ-based risk classification and objective physiologic sleep parameters, including the Apnea–Hypopnea Index, Respiratory Disturbance Index, Sleep Apnea Indicator, and Sleep Quality Index, obtained from a U.S. Food and Drug Administration-cleared wearable home sleep monitor—SleepImage Ring (MyCardio LLC, Denver, CO, USA)—worn for a minimum of three consecutive nights. Secondary aims will evaluate associations between SDB risk classification and body mass index, Mallampati score, Brodsky tonsillar grade, and psychosocial functioning (anxiety, depression, perceived stress, and daytime sleepiness). Exploratory craniofacial analyses will be conducted among participants with clinically available lateral cephalometric radiographs. This protocol could position pediatric dental visits as an accessible touchpoint for early identification of children with unrecognized SDB and inform pathways for timely referral. Protocol Version: 1.4, dated 13 May 2026. Trial Registration: ClinicalTrials.gov NCT07581938. Full article

Background: Finite element analysis (FEA) has become a cornerstone of dental biomechanics, supporting the simulation of stress, strain, and displacement in biological structures and biomaterials. Methods: This combined bibliometric and narrative review examines the development, geographic and disciplinary distribution, and methodological state of FEA-based dental research by analysing 3379 publications indexed in Scopus between 1969 and 2024. Results: The dataset shows continued exponential growth in annual output, particularly after 2000, with China, the United States, and India as the leading contributors. Journal articles account for the bulk of the literature, and implant dentistry remains the most prominent focus area. The 100 most-cited works concentrate on implant biomechanics, prosthodontics, and scaffold design, while interdisciplinary contributions linking FEA to regenerative biomaterials, orthodontics, and endodontics are increasing. Conclusions: FEA in dentistry is a mature but still rapidly expanding field. Persistent methodological gaps remain, including the routine use of isotropic and linear-elastic bone, idealised osseointegration, static loading, limited patient-specific variability, and scarce in vivo validation. Future work should converge on patient-specific multiscale models, fatigue and contact-aware formulations, standardised validation pipelines, and the integration of artificial intelligence into both pre-processing (mesh generation, parameter identification) and post-processing (surrogate models, outcome prediction). The wider relevance of FEA across orthopaedic, thoracic, and reconstructive biomechanics confirms its role as a unifying computational tool for clinical translation. Full article

Historically, the study of oral fungal species was limited by the inability to cultivate most of them. However, advances in metagenomic techniques have enabled the direct identification of microbial genomes from human samples, markedly broadening our understanding of the oral mycobiome. This narrative review aims to analyze the available scientific evidence on the composition and dynamics of the oral mycobiome, as well as its influence on the development of local pathological conditions. The oral mycobiome is highly diverse, with emphasis on genus Candida, followed by Cladosporium, Aureobasidium and Saccharomyces. Candida albicans remains the most frequently identified species in both health and diseases state. However, individuals with oral candidiasis present a higher detection of Candida dubliniensis, Candida parapsilosis, Pichia kudriavzevii, Antrodiella micra and Cladosporium sphaerospermum. In dental caries, C. albicans and C. dubliniensis are associated with advanced lesions, whereas Debaryomyces and Rhodotorula may exert protective effects against Streptococcus mutans, a cariogenic bacterium. In periodontitis, an increase in yeast-bacteria interactions is observed. Additionally, C. albicans has been implicated in oral carcinogenesis through multiple mechanisms. These findings highlight the need for a deeper understanding of the oral mycobiome to enable early detection of oral diseases and the development of therapeutic approaches. Full article