Relationship between skeletal maturity indicators and dental calcification stages in a sample pediatric population.

Context: Human growth shows considerable variation. In addition to constitutional differences in the timing of maturation, there are diseases, systemic disorders and environmental factors that may affect a child's physiologic maturity. Interrelationships among skeletal, somatic and sexual maturity have been shown to be consistently strong. The study was conducted to evaluate the relationship between dental calcification stages and skeletal maturity indicators. Materials and Methods: The hand wrist radiographs, the lateral cephalogram and the orthopantomogram (OPG) of 120 patients (60 males and 60 females, age group 8-14 years) were taken. The hand wrist radiographs were analyzed using Fishman's skeletal maturity index (SMI), and the cephalograms analyzed by cervical vertebrae maturation (CVM) by Baccetti. OPG was analyzed according to the Demirjian's system. Result: On assessment, cervical vertebrae maturation indicator (CVMI) staging showed significant results with a correlation coefficient of 0.716 being the highest in canines in males while assessment of dental calcification stages and its comparison with skeletal maturity indicator showed insignificant results. The correlation coefficient values were 0.11, 0.09, 0.09 and 0.13 for canine, first premolar, second premolar and second molar respectively. Conclusion: There is a strong correlation between the maturation stage of cervical vertebrae and calcification stages of canine, first premolar, second premolar and second molar as determined by Demirjian's method.

Intraoral Lipoma: A Case Report.

The soft tissue tumor lipoma is quite frequent. Although it is the most mesenchymal tumor of the trunk and proximal regions of the extremities, it is quite uncommon to find it on the oral mucosa, accounting for just 1% to 5% of benign oral tumors. There is no specific location that is more prone to lipoma development in the mouth. Some frequent sites are the tongue, buccal mucosa, and the mouth's floor. A Yellowish mass with no symptoms is the normal clinical appearance. In most cases, the overlaying epithelium is unharmed, and superficial blood vessels may be seen passing through and around the tumor. The differential diagnosis might also include granular cell tumors, neurofibroma, traumatic fibroma, and diseases of the salivary glands (mucocele and mixed tumor). Herein a detailed case of a 28-year-old woman with a right-sided lower-lip oral lipoma is presented in this paper.

Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients.

A new artificial intelligence (AI) supported T-Ray imaging system designed and implemented for non-invasive and non-ionizing screening for coronavirus-affected patients. The new system has the potential to replace the standard conventional X-Ray based imaging modality of virus detection. This research article reports the development of solid state room temperature terahertz source for thermograph study. Exposure time and radiation energy are optimized through several real-time experiments. During its incubation period, Coronavirus stays within the cell of the upper respiratory tract and its presence often causes an increased level of blood supply to the virus-affected cells/inter-cellular region that results in a localized increase of water content in those cells & tissues in comparison to its neighbouring normal cells. Under THz-radiation exposure, the incident energy gets absorbed more in virus-affected cells/inter-cellular region and gets heated; thus, the sharp temperature gradient is observed in the corresponding thermograph study. Additionally, structural changes in virus-affected zones make a significant contribution in getting better contrast in thermographs. Considering the effectiveness of the Artificial Intelligence (AI) analysis tool in various medical diagnoses, the authors have employed an explainable AI-assisted methodology to correctly identify and mark the affected pulmonary region for the developed imaging technique and thus validate the model. This AI-enabled non-ionizing THz-thermography method is expected to address the voids in early COVID diagnosis, at the onset of infection.

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network.

A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. This would allow patients to choose the best accessible kidney donor and the optimal immunosuppressive medication. Several studies have attempted to identify factors that predispose to graft rejection, but the results have been contradictory. As a result, the goal of this paper is to use the African buffalo-based artificial neural network (AB-ANN) approach to uncover predictive risk variables related to kidney graft. These two feature selection approaches combine to provide a novel hybrid feature selection technique that could select the most important elements to improve prediction accuracy. The feature analysis revealed that clinical features have varied effects on transplant survival. The collected data is processed in both training and testing methods. The prediction model's performance, in terms of accuracy, precision, recall, and F-measure, was examined, and the results were compared with those of other existing systems, including naive Bayesian, random forest, and J48 classifier. The results suggest that the proposed approach can forecast graft survival in kidney recipients' next visits in a creative manner and with more accuracy compared with other classifiers. This proposed method is more efficient for predicting kidney graft survival. Incorporating those clinical tools into outpatient clinics' everyday workflows could help physicians make better and more personalised decisions.

Comparative evaluation of salivary immunoglobulin a levels between pedodontic subjects.

BACKGROUND AND AIMS: Host immune response is altered by a series of physiologic and pathologic factors like age, gender, inflammation, surgery, medication etc., The present study was conducted to evaluate differences in salivary IgA (S-IgA) levels among pedodontic subjects undergoing active orthodontic treatment with fixed and removable appliance. The levels of S- IgA were determined before 3 months and 6 months post active orthodontic treatment. METHODS: A total of 40 healthy pedodontic subjects (aged 8-15 years) were recruited in the present study. They were equally divided into Group A (fixed orthodontic group) and Group B (removable orthodontic group) with 20 subjects each. 1.5 mL of saliva per subject was obtained before 3 and 6 months after treatment. Enzyme Linked Immunosorbent Assay (ELISA) technique was used for measurement of Salivary IgA levels. RESULTS: Group A and B both showed significant rise in S-IgA levels 3 months and 6 months post active orthodontic treatment. Mean value of S-IgA 3 months post treatment in the saliva of children in group B and group A were (144.27 ± 5.32) and (164.0 ± 3.23) µg/ml respectively. While mean value of S-IgA after 6 months of treatment in group B and group A were (149.8 ± 6.02) and (166.4 ± 3.65) µg/ml respectively. CONCLUSION: Salivary Immunoglobulin A level values were significantly higher statistically in both group A and group B post active orthodontic treatment than before. The results however, showed that Group A (fixed orthodontic group) showed statistically significant higher levels of S-IgA than Group B (removable orthodontic group). Active orthodontic treatment triggered a stronger stimulus for oral secretory immunity, hence the increase in levels were detected. There is a significant positive correlation between S-IgA and active fixed as well as removable orthodontic treatment. Orthodontic treatment is hence a local immunogenic factor.