Sheikh Mohammed Shariful Islam

TL;DR: In this article, the authors developed the best strategies to develop the best strategy for the treatment of the Sister Mary Joseph nodule (SMJN) arising from malignant gastrointestinal tumors.

TL;DR: In this article , the authors applied machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables, including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF), SVM, GP, Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease.

TL;DR: In this article , the authors used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5-19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020.

TL;DR: In this paper , the authors used 2019 Global Burden of Disease (GBD) data to report the HHD age-standardised prevalence, disability adjusted life years (DALYs), years of life lost (YLLs), and mortality, as well as HHD risk factors attribution percent with their 95% uncertainty interval (UI).