The calcifying odontogenic cyst (COC) accounts for 1 % of jaw cysts, found most commonly as central lesion. The COC usually manifests itself as a painless, slow growing swelling, anterior to the first molar region. Radiographically it appears as a well-defined, unilocular radiolucency, and may have a radiopaque mass in its center. Majority of the cases appear before the fourth decade of life. The COC is found rarely in patients in the first decade of life. Histopathological features include a cystic lining demonstrating characteristic "Ghost" epithelial cells with a propensity to calcify. Here we report the unusual occurrence of a case of 8 year child diagnosed with bilateral COC on right and left side of mandible.

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The treatment of pediatric maxillofacial fractures is unique due to the psychological, physiological, developmental, and anatomical characteristics of children. Method. This case report describes the management of symphysis fracture of mandible in a 5-year-old boy. The fracture was treated by acrylic splint with circummandibular wiring. Results. The splint was removed after 3 weeks. The patient had no complaints, and radiograph revealed a healed fracture. Conclusion. The clinical outcome in the present case indicates the management of mandibular fractures in pediatric patients using acrylic splint with circummandibular wiring.

The glandular odontogenic cyst is an infrequent cyst of odontogenic origin, seen in maxilla or mandible, with an unpredictable and possibly aggressive behavior. It also has the tendency to grow to a large size and predisposition towards recurrence. It is also known as Sialo-Odontogenic Cyst. GOC can be easily misdiagnosed microscopically as a central muco epidermoid carcinoma. The diagnosis of GOC can be extremely difficult due to the rarity of the cyst and lack of clear diagnostic criteria. This article reports a case of GOC in a 45-year-old male and discusses the clinical, radiological and histopathological features of GOC.

In recent scores, diabetes mellitus (DM)is regarded as a chronic illness and one of the leading critical health challenges throughout the earth. About eighty percent of demise occurs because of DM(Type II) which could be avoided by the earlier diagnosis of persons with this threat. Nevertheless, presently machine learning techniques can be employed for diabetics’ detection very precisely. We are proffering a health care monitoring system comprising ECG sensors. The criteria that have a considerable volume of significance will be sensed by the ECG sensors that remain important for remote monitoring of the sick person. A mobile app observance will be employed for consistently monitoring the sick person’s ECG and diverse data extraction approaches will be executed upon the ECG wave for extracting features to properly prognosis heart illnesses. Hence, this study proffers the employment of a metaheuristic optimization algorithm called Real Coded Binary Ant Bee Colony (RC-BABC) for optimized feature choosing, and ReliefF methodology will be employed for excerpting the features and computing the features’ scores centered upon the disparities in feature values and class values betwixt nearby cases. An effectual attempt will be carried out for detecting cardiac demist at early phases emerging out of the intensity of DMin which feature prognosis before heart rate variability assessment will be executed. The DM’sfeatures would be analyzed out of the diabetic’s dataset for detecting the reason for abrupt cardiac arrest. Next, the excerpted features are classified employing the Fuzzy C-means Neural Network (FCNN). The performance analysis is carried out to exhibit that FCNN executes properly in prognosticating the illnesses. The proffered FCNN paradigm attains 97% and 84% of testing and training (t&t) accuracy, 93% and 82% of t&t specificity, 95% and 81% of t&t sensitivity and 92% and 85% of t&tF1-score.