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Background and Objectives: Advances in virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies have resulted in their increased application across many medical specialties. VR’s main application has been for teaching and preparatory roles, while AR has been mostly used as a surgical adjunct. The objective of this study is to discuss the various applications and prospects for VR, AR, and MR specifically as they relate to spine surgery. Materials and Methods: A systematic review was conducted to examine the current applications of VR, AR, and MR with a focus on spine surgery. A literature search of two electronic databases (PubMed and Scopus) was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The study quality was assessed using the MERSQI score for educational research studies, QUACS for cadaveric studies, and the JBI critical appraisal tools for clinical studies. Results: A total of 228 articles were identified in the primary literature review. Following title/abstract screening and full-text review, 46 articles were included in the review. These articles comprised nine studies performed in artificial models, nine cadaveric studies, four clinical case studies, nineteen clinical case series, one clinical case–control study, and four clinical parallel control studies. Teaching applications utilizing holographic overlays are the most intensively studied aspect of AR/VR; the most simulated surgical procedure is pedicle screw placement. Conclusions: VR provides a reproducible and robust medium for surgical training through surgical simulations and for patient education through various platforms. Existing AR/MR platforms enhance the accuracy and precision of spine surgeries and show promise as a surgical adjunct.

Background: In India, policymakers are in an opined that expansion of public-funded health insurance is the key to achieve Universal Health Coverage (UHC). Despite untapped potential to be part of such government-run public-funded health scheme, many private service providers are reluctant to join in such scheme due to various reasons. This paper aims to evaluate facilitators, barriers and perception to participation of the private sector health facilities in Health Insurance & government-led schemes. Methods: Present study was conducted in 83 private hospitals of Vadodara city. Sampling frame was formed & with the use of random number table, 83 hospitals were selected randomly. Data collection was done in pre tested, pre formed questionnaire & respondents were the medical superintendent or the person in-charge of the hospital. Results: In the present study 30% & 26% of hospitals were enrolled for cashless private health insurance facility & government health schemes respectively. Social service, competition and increase clientele were the common reason sought for enrolment in government health schemes. Low & delayed reimbursement, bribe to clear payment, limited services covered, administrative issues were the common problems identified for non-enrolment. There was an association between a number of beds in the hospital & enrolment to government health scheme (p

Background: Type 2 Diabetes Mellitus (DM) is another pandemic of 21 century, and its control is of immense importance. Researchers developed many predictor models using soft computing techniques. The present study developed a prediction model for Type 2 DM using machine learning classifiers. The analysis excludes plasma glucose concentration and insulin concentration as predictors to explore relationships with other predictors. Background: Methods: This cross-sectional study enrolled 108 participants aged 25 to 67 years from SMS Medical College, Jaipur (Rajasthan, India), after approval from the ethics committee. The study developed a prediction model using machine learning techniques. The classifiers used in the application include decision trees, support vector machines, K-nearest neighbors, and ensemble learning classifiers. A total of 25 predictors were collected and underwent feature reduction. The response levels include diabetes mellitus, prediabetes, and no diabetes mellitus. The models were run using three predictors and a response variable. The prediction model with the best accuracy and area under the receiver operator characteristic curve was selected. Results: The features that vary among the three groups include age, WHR, biceps skinfold thickness, total lipids, phospholipids, triglycerides, total cholesterol, LDL, VLDL, and serum creatinine, and family history of DM. After feature reduction, the age, biceps skinfold thickness, and serum creatinine were run on the Classification learner application to predict the diabetic category. The best model was subspace discriminant with accuracy, sensitivity, specificity, and AUC under the ROC curve was 62.4%, 74%, 94%, and 0.70, respectively. Conclusion: The present study concludes that age, biceps skinfold thickness, and serum creatinine combination have higher specificity in predicting type 2 DM. The study emphasized the selection of appropriate predictors along with newer machine learning algorithms.

Background: India is home to a major proportion of children under five years of age and has been facing challenges to improvise their health including nutritional status. The study conducted with objectives to find out the nutritional status of children of under-five age and knowledge of mothers on malnutrition. Method: A community based cross-sectional study was conducted in a town of Gujarat. Child ren between age group of 1 to 5 years were participants for the study of selected Anganwadi. The inclusion criteria for study participants were the participants to be residents of the selected Anganwadis area and the age limit for the children was set to less than or equal to five years. Total 390 children and 325 mothers participated in study. After obtaining ethical approval, data was collected from the Anganwadis and analysed. Result: Major findings showed that most children (66.6%) were of first order of birth, majority (77%) weighed between 2.5-3.4 kgs at birth, 94% were exclusively breast fed upto six months of age. About 20% of them suffered from morbidity in last six months. Anthropometric measurement showed that 88% of children in green zone and 82% of children had midarm circumference is more than 13.5 cm. Conclusion: Thus study conclude that malnutrition in town was below national average. The role of health staff was found proactive.