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Background: Stunting is a major public health issue in most of developing countries. Although, its worldwide prevalence is decreasing slowly but the number of stunted children is still rising in Pakistan. Stunting is highly associated with several long-term consequences, including higher rate of mortality and morbidity, deficient cognitive growth, school performance, learning capacity, work capacity and work productivity. To prevent stunting, we proposed Stunting Diagnostic and Education app. This app includes detailed knowledge of stunting and it's all forms, symptoms, causes, video tutorials and guidelines by the Pediatricians and Nutritionists. Methods: A cross-sectional study has been conducted in schools of Multan District, Pakistan for the period of January 2019 to June 2019. Sample data of 1420 children, aged 4 to 18 years using three age groups, were analyzed by using SPSS version 21.0 to assess the prevalence of stunting and to analyze the risk factors associated with it in children under and over 5 age. Chi square test was applied in comparison with rural and urban participants and p-value < 0.05 was considered as significant. This study includes distribution of sociodemographic characteristics, parental education, working status of mothers, dietary patterns of school going children and prevalence of stunting in school going children. After getting study results, Stunting Diagnostic and Education app was developed according to the instructions of child experts and nutritionists. Results: 354 (24.93%) participants were stunted out of 1420, 11.9% children were obese and 63.17% children were normal. Out of 354 stunted children, higher ratio of stunting was found in the age group of 8-11 years children with 51.98 percentage. 37.85% stunted children were found in the age group of 4-7 years and 10.17% stunting was found in the age group of 12-18 years children. It was observed in the study that male children were highly stunted than female with 57.91 and 42.09% respectively. Children living in rural areas were more stunted affected as compared to the children living in urban society with percentage 58.76 and 41.24 respectively.

Advancement in the field of nanotechnology has prompted the need to elucidate the deleterious effects of nanoparticles (NPs) on reproductive health. Many studies have reported on the health safety issues related to NPs by investigating their exposure routes, deposition and toxic effects on different primary and secondary organs but few studies have focused on NPs’ deposition in reproductive organs. Noteworthy, even fewer studies have dealt with the toxic effects of NPs on reproductive indices and sperm parameters (such as sperm number, motility and morphology) by evaluating, for instance, the histopathology of seminiferous tubules and testosterone levels. To date, the research suggests that NPs can easily cross the blood testes barrier and, after accumulation in the testis, induce adverse effects on spermatogenesis. This review aims to summarize the available literature on the risks induced by NPs on the male reproductive system.

Background Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions The study concluded that it may be used to build an automated system for the detection of severity of CKD.