Dr. R. Ahmed Dental College and Hospital

Mandibular fractures pose a major public-health burden because their prevalence, anatomical patterns and causes vary significantly across populations, complicating prevention and treatment planning. Therefore, it is of interest to analyze 1,500 radiographically and clinically confirmed mandibular fractures identified from 3,600 outpatient records (2019-2023) at R. Ahmed Dental College and Hospital, Kolkata, after excluding incomplete and pathological cases. Demographic data, etiology, fracture site distribution and fracture multiplicity were recorded and evaluated independently by two examiners using descriptive statistics and chi-square tests at a 5% significance level. Young adult males, especially in the 21-30-year age group, were most frequently affected, with road traffic accidents as the leading cause and parasymphysis and condyle emerging as the predominant fracture sites, mostly as single-site fractures. Thus, we document parasymphysis fractures in young men secondary to traffic accidents as the dominant pattern in this setting, underscoring the need for targeted road-safety policies and maxillofacial-trauma-prevention strategies.

BACKGROUND:Immediate implant placement in the posterior mandible is associated with a risk of inferior alveolar nerve (IAN) injury due to anatomical variations such as lingual concavities and variability in mandibular canal positioning. Accurate three-dimensional assessment of these structures is therefore essential. OBJECTIVES:This study aimed to evaluate the root apex-mandibular canal distance (RAC), prevalence and morphology of lingual concavities, and cross-sectional ridge patterns in the posterior mandible to identify high-risk sites for IAN injury during immediate implant placement. MATERIALS AND METHOD:Cone-beam computed tomography (CBCT) scans of 60 patients (600 posterior mandibular sites) were analyzed. Cross-sectional ridge morphology (C, P, and U types), lingual concavity dimensions (angle, depth, height), concavity zones (A, B, C), and RAC values for premolars and molars were assessed. Statistical analysis included normality testing followed by appropriate parametric or non-parametric tests, with significance set at p < 0.05. RESULTS:U-type ridges were the most prevalent (58.3%), particularly in molar regions, while P-type ridges predominated in premolars (76.7%). The most concave point of the lingual surface was most frequently located in zone B (49.7%). The shortest RAC was observed at the distal roots of second molars (3.00 ± 2.13 mm), indicating the highest risk of IAN injury (p < 0.0001). Lingual concavity angle decreased and depth increased from premolars to second molars, whereas concavity height showed no significant variation. CONCLUSIONS:The distal roots of second molars demonstrate the closest proximity to the mandibular canal and the deepest lingual concavities, representing the highest anatomical risk for IAN injury during immediate implant placement. Pre-surgical CBCT evaluation of RAC, ridge morphology, and lingual concavity characteristics is essential for safe and predictable implant therapy in the posterior mandible.

Orthopaedic treatment of skeletal class III malocclusion in children is critical because it can prevent potential surgical procedures. Initial management of class III malocclusion helps to avoid the harmful effects of facial deformity. Hence, we present four cases on the early orthopaedic therapy of class III malocclusion using rapid maxillary expansion (RME) and a face mask. All these cases presented with class III malocclusion, which included mid-face deficit and an anterior cross bite. All of them were treated with a combination of RME and facemask therapy. Combined skeletal and dental improvements resulted in satisfactory treatment of class III malocclusion.

Background: Oral ulcerative lesions are frequently encountered in pediatric patients and may arise from traumatic, infectious, recurrent aphthous, or inflammatory conditions. Because several of these disorders exhibit overlapping clinical characteristics, establishing an accurate diagnosis can be challenging. Artificial intelligence (AI), particularly deep-learning techniques applied to clinical images, may provide valuable support for lesion recognition and diagnostic classification.Aim: To assess the diagnostic effectiveness of an AI-assisted approach integrating clinical and image-derived characteristics for classifying pediatric oral ulcerative lesions and to compare its performance with conventional clinical diagnosis.Materials and Methods:This study included 120 children aged 6–14 years presenting with clinically identifiable oral ulcerative lesions. Clinical parameters, including pain, perilesional erythema, lesion number, regional lymphadenopathy, systemic symptoms, lesion duration, and lesion size, were documented together with standardized clinical photographs. Image-derived characteristics included ulcer margin, base appearance, surrounding mucosal changes, and lesion number. Three deep-learning architectures—ResNet50, VGG16, and InceptionV3—were assessed, followed by evaluation of a proposed combined AI model. Diagnostic performance was determined using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), precision, F1-score, receiver operating characteristic (ROC) analysis, and area under the curve (AUC). Agreement with the reference diagnosis was assessed using Cohen’s kappa coefficient, while diagnostic performance was compared with conventional clinical assessment.Results: Traumatic ulcers constituted the most frequent diagnostic category (25.0%), followed by recurrent aphthous ulcers (23.3%) and herpetic ulcers (18.3%). Pain and perilesional erythema were observed in 77.5% and 71.7% of participants, respectively. The proposed combined model produced the best overall performance, achieving 95.0% accuracy, 94.2% sensitivity, 96.1% specificity, 93.5% PPV, 96.5% NPV, and a 93.8% F1-score. The overall AUC was 0.978 (95% CI: 0.956–0.992). Diagnostic accuracy across individual lesion categories ranged from 94.2% to 96.7%. Agreement between the AI-generated diagnosis and the reference diagnosis was almost perfect (κ = 0.89; 95% CI: 0.83–0.95), whereas conventional clinical diagnosis demonstrated substantial agreement (κ = 0.70; 95% CI: 0.60–0.80). The AI model also achieved greater overall diagnostic accuracy than conventional clinical assessment (95.0% vs. 84.2%).Conclusion: The proposed AI-assisted approach demonstrated strong diagnostic capability for classifying pediatric oral ulcerative lesions and showed greater concordance with the reference diagnosis than conventional clinical assessment. Combining clinical information with image-based characteristics may enhance diagnostic support in pediatric oral healthcare. Nevertheless, validation in larger, multicenter, and more heterogeneous populations is necessary to establish the model’s generalizability and clinical applicability before routine implementation.

PURPOSE:In the realm of forensic science, the Post Mortem Interval (PMI) is a critical component that determines the time that has passed since the person's physiological death. Although techniques exist to precisely determine the PMI, the results are often unreliable. Hypoxia inducible factor-1 (HIF-1) is a transcriptional factor, and in hypoxic conditions, HIF-1α protein is expressed after proteosomal degradation and ubiquitination pathway involving von Hippel-Lindau protein (pVHL). The aim of the study was to assess HIF-1α mRNA expression in human gingival tissues at different PMIs. METHODS:Gum tissues were collected from cadavers at three definite intervals, namely short PMI (SPMI), medium PMI (MPMI), and long PMI (LPMI). The relative fold change in gene expression of HIF-1α was studied by RT PCR. Histopathological analysis of the tissue samples was done to determine the PMI. RESULTS:In the case of short PMI (SPMI), the relative fold change in gene expression of HIF-1α is 26.90 ± 23.62. However, in the medium PMI (MPMI) and long PMI (LPMI), the relative fold change in gene expression decreased to 6.32 ± 10.90 and 5.33 ± 8.12, respectively. Histopathological analysis of the post mortem samples revealed less necrosis in SPMI than LPMI. Inflammatory cell infiltration is more in SPMI than MPMI, with their notable absence in LPMI. Ulceration was prominent in LPMI. Destructive vasculitis was visible in SPMI and MPMI. Cystic changes were increased in MPMI and LPMI. CONCLUSION:Combined gene expression of HIF-1α and histopathological analysis is a good option for determination of PMI.