METHODS: This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algorithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model.
RESULTS: We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%).
CONCLUSIONS: The random forest algorithm demonstrated the highest prediction accuracy (81%) compared with the decision tree, eXtreme Gradient Boosting, and k-nearest neighbors algorithms. The accuracy was then enhanced to 96% using the MVBHE model. The MVBHE model is recommended by the present study as the best way to predict malnutrition in under-five children.
METHODS: The cross-country data were collected from 91 countries from the United Nations agencies in 2012. LE is the response variable with demographics (total fertility rate, and adolescent fertility rate), socioeconomic status (mean year of schooling, and gross national income per capita), and health factors (physician density, and HIV prevalence rate) are as the three main predictors. Stepwise multiple regression analysis is used to extract the main factors.
RESULTS: The necessity of more healthcare resources and higher levels of socioeconomic advantages are more likely to increase LE. On the other hand, demographic changes and health factors are more likely to increase LE by way of de-cease fertility rates and disease prevalence.
CONCLUSION: These findings suggest that international efforts should aim at increasing LE, especially in the low income countries through the elimination of HIV prevalence, adolescent fertility, and illiteracy.
METHODS: In this study, a dystrophin-deficient myoblast cell line established from the skeletal muscle of a dystrophic (mdx) mouse was used as a model. The dfd13 (dystrophin-deficient) and C2C12 (non-dystrophic) myoblasts were cultured in low mitogen conditions for 10 days to induce differentiation. The cells were subjected to total protein extraction prior to Western blotting assay technique. Protein sub-fractionation has been conducted to determine protein localization. The live-cell analysis of autophagy assay was done using a flow cytometer.
RESULTS: In our culture system, the dfd13 myoblasts did not achieve terminal differentiation. PTEN expression was profoundly increased in dfd13 myoblasts throughout the differentiation day subsequently indicates perturbation of PI3K/Akt/mTOR regulation. In addition, rictor-mTORC2 was also found inactivated in this event. This occurrence has caused FoxO3 misregulation leads to higher activation of autophagy-related genes in dfd13 myoblasts. Autophagosome formation was increased as LC3B-I/II showed accumulation upon differentiation. However, the ratio of LC3B lipidation and autophagic flux were shown decreased which exhibited dystrophic features.
CONCLUSION: Perturbation of the PTEN-PI3K/Akt pathway triggers excessive autophagosome formation and subsequently reduced autophagic flux within dystrophin-deficient myoblasts where these findings are of importance to understand Duchenne Muscular Dystrophy (DMD) patients. We believe that some manipulation within its regulatory signaling reported in this study could help restore muscle homeostasis and attenuate disease progression. Video Abstract.
METHODS: Scoping review methodology guided the synthesis of 272 publications on factors influencing physical activity. Bibliometric analysis examined publication trends, productivity, influential studies, content themes, and collaboration networks.
RESULTS: Since 2010, the United States has led a significant increase in research output. Highly cited articles identified physiological limitations and psychosocial determinants as key barriers and facilitators. Extensive focus was seen in clinical medicine and exercise science journals. Analysis revealed predominant attention to psychosocial factors, physiological responses, and applications in respiratory disease. Gaps remain regarding policy and environmental factors.
CONCLUSION: This review showed major advances in elucidating determinants while revealing the remaining needs to curb the pandemic of inactivity globally. Expanding international collaboration, contemporary theoretical models, and tailored mixed-methods approaches could promote progress through greater global participation. Addressing knowledge gaps across populations and disciplines should be a priority.