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.
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: Mucormycosis is an uncommon fungal infection with high morbidity and mortality. There had been a sudden surge in the cases of mucormycosis during the second wave of Coronavirus Disease 2019 (COVID-19) in India. Objective: The etiology, pathophysiology, and correlations of mucormycosis at tertiary hospital in India is explored in the present study. Methods: In this retrospective observational study, all coronavirus disease associated mucormycosis (CAM) cases admitted at this center between April 2021 to June 2021 were included. The cases were
evaluated in terms of their background, most common presentations, chief underlying etiologies, severity of disease, comorbidities, investigation profiles, prognosis, and treatment provided. Results: Among the total 231 cases reported with mucormycosis, age group of 40 - 50 years (28%) was the most afflicted and the 20-30 year was the least. Men (68%) were more afflicted than Women. 66% patients had a history of vaccination against COVID-19. 63% patients presented with a High-Resolution Computerized Tomography (HRCT) score of 9-16. 60% required oxygen support and 71% required steroids. Diabetes mellitus was the most prevalent comorbidity. Conclusion: The salience of the second inferno wave of COVID-19 was witness to COVID-19 patients who had pre-existing diabetes mellitus.
Individuals with diabetes in general foster more extreme COVID-19 infections and end up using corticosteroids. In any case, the corticosteroids – alongside diabetes – increment the danger of getting mucormycosis. The specific pathophysiology of COVID-19 may represent co-morbidity with Invasive Fungal diseases (IFI).

Pandemic influenza viruses have emerged three times in this century. It is important to examine the potential risk of novel microorganisms/viruses through the add-on research mechanism of Gain of Function Research (GoFR). This mechanism consists of the practice of serial passaging of microorganisms to increase their transmissibility, virulence, immunogenicity, and host tropism through the inclusive feature of selective pressure of culture medium. Although, the GoFR can be a double-edged sword that has the potential to give an insight and better appreciation of current and future pandemics with antecedent apprehension of initiating a pandemic, itself. Moreover, with its inherent potential to give a head start on a virus, GoFR has the potential to develop vaccines or therapeutics, before the virus emerges in its true virulent form. Likewise, the GoFR studies can be vital in research on antivirals and antimicrobial agents and can help inform the development of combination therapies. Passive immunotherapy, which often includes a combination of products, is particularly dependent on GoFR experiments for evaluating efficacy. GoFR if made use of meticulously and with caution could help Medical Sciences and Humankind tremendously.

Although the scoliosis has in Romania relatively the same incidence as in most of the European countries, it is usually late diagnosed, when the Cobb angle has significant values.
Since 2010, Romania has a program of screening and early treatment of spine deformities funded by The Ministry of Health.
In a 14 years period in Pediatric Orthopedic Department of Central Emergengy Hospital for Children "Grigore Alexandrescu" Bucharest and also in the private hospitals "Regina Maria" and "Sanador" were examined and diagnosed 14.853 patients with scoliosis.