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.

The article presents a case of severe necrotizing fasciitis and rhabdomyolysis-induced acute tubular necrosis resulting from the injection of heroin laced with a plant-fertilizer known as „Pure by Magic“. We also review the literature data regarding the renal adverse effects of heroin. Due to the diversity of adulterants used by drug dealers for cutting heroin and the variety of substances patients mix the heroin with before injecting themselves, the effects of this practice are often unknown and the treatment lacks of antidote, being strictly symptomatic.

Abdominal pseudocysts are rarely reported in peritoneal dialysis and usually arise secondary to repeated dialysisrelated peritonitis. We present the case of a patient with end-stage renal disease treated for 9 years by continuous ambulatory peritoneal dialysis that developed an abdominal pseudocyst in the vicinity of the non-functional and calcified renal graft. Because the adequacy of peritoneal dialysis was optimal, surgical removal of the invaginated peritoneum and closure of the breach allowed the patient to continue peritoneal dialysis treatment.

Over the last decade, several divergent views have been expressed regarding the effect that iodinated contrast agents may have on renal function. Evidence-based medicine often requires the recommendation of high-performance contrast-enhanced imaging exams for precise positive diagnosis. The fear of intravenous contrast use in patients with elevated serum creatinine seems to become an old dogma, outdated by the benefits of the procedures. Patients with glomerular filtration rate below 30 mL/min/1.73 m2 can be protected by peri-procedural hydration and withdrawal of other nephrotoxics. Whatever the degree of risk, current guidelines recommend contrast-enhanced investigations in any situations where the advantages for the diagnosis are certain.

There is increased evidence that the prevalence of peripheral artery disease (PAD) in hemodialysed patients is higher than in general population [1,2]. According to ACC/AHA (American College of Cardiology / American Heart Association) guidelines, the following risk factors are associated with PAD [3,4]

Chronic kidney disease (CKD) is a worldwide health problem [1,2] affecting between 7 - 10% of young individuals (30 - 64 years old) in Europe [2] and approximately 10 - 18% of the population in the USA [3]. In 2013, in Romania, the prevalence of CKD was approximately 13.1%, meaning about 1,900,000 persons, and 13,899 patients were on chronic dialysis [4].
CKD is associated with increased cardiovascular morbidity, even from early stages [5-8]. Decreased glomerular filtration rate (GFR) is a strong predictor of cardiovascular events, even in the absence of other cardiac risk factors [9]. Risk for cardiovascular disease in CKD patients is 10 - 30 times higher than in non-CKD individuals and mortality from cardiovascular diseases (CVD) accounts for approximately 50% from all causes of death in dialysis population [6,10,11,12]. Predisposing features for developing CVD in CKD patients include both traditional and nontraditional - uremia associated - factors [11,12].