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@#Introduction: The increased prevalence of oral submucous fibrosis (OSMF) in the last few years relates to the increased consumption of areca nut(AN) products. OSMF is a premalignant condition and risk to progression to oral cancer is more when AN is chewed along with tobacco. Moreover, high copper content in AN is responsible for fibroblast dysfunction and fibrosis. This study was conducted with aim to assess and compare pH and copper content of raw AN and popular Indian commercial AN based (with and without tobacco) products. Method: Six samples each of twelve different brands of AN based commercial products i.e. six without tobacco (pan masala) and with tobacco were analyzed for pH and then the samples were dried, and powdered for estimation of the copper content. Results: For the six raw areca nuts (sample 1-6), the pH was found to range from 3.06±1.08 to 5.04±0.81, among the six non tobacco containing samples (sample 7-12), the pH was found to range from 6.03±1.08 to 9.09±0.81, and for six tobacco containing samples (sample 13-18), the pH was found to range from 9.18±0.90 to 11.07±0.09. The mean copper concentration among raw areca nut samples (sample 1-6) was 4.05±0.18 μg/g, among non-tobacco containing samples (sample 7-12) it was 10.17±1.08μg/g and among tobacco samples (sample 13-18),it was 18.09±1.08 μg/g (p<0.001). Conclusion: High copper content present in quid and commercial AN may be a causative factor for an increased fibrosis in OSMF, our findings need evaluation by further research and standardization.
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Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.
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Oral genodermatoses includes a spectrum of inherited dermatological disorders with varying oral mucosal manifestations. Darier's disease is an autosomal dominant disorder with defect in desmosomal attachment. This case report presents oro cutaneous manifestations of Darier's disease in a 40 year old female patient with a detailed review on etiology, pathogenesis, differential diagnosis and management of the condition.
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AIMS AND OBJECTIVES: (1) To evaluate the role of ultrasonography with colour Doppler in the diagnosis of fascial space infections, (2) To determine the nature, size and extent of the odontogenic infections which involve the primary and secondary fascial spaces of the maxilla and the mandible (3) To evaluate the sensitivity of ultrasonography in determining whether the inflammatory process is in a stage of cellulitis or abscess and to thus determine the appropriate time for a surgical intervention during the course of the infection and to correlate the clinical and radiographic findings with the ultrasonographic findings and (4) to determine the treatment plan. METHODOLOGY: Thirty four patients with odontogenic infections which involved the superficial and the deep fascial spaces of the head and neck were subjected to ultrasonographic examinations over the suspected area in the transverse and axial directions to determine the stage of the infection, its anatomic location and the treatment plan. RESULTS: Ultrasonography showed 95.7% sensitivity and 100% specificity as compared to the clinical and radiographic diagnoses and it proved to be an effective investigation modality in the diagnosis of fascial space infections.
