The authors look at malnutrition in children and how socio-economic factors impact this.
Read More...Socio-economic factor impact on malnutrition in South Indian government school children
The authors look at malnutrition in children and how socio-economic factors impact this.
Read More...Indole-3 carbinol on lipid accumulation in Caenorhabditis elegans as a novel therapeutic for Type II Diabetes
In this study the authors look at the use of Indole 3 Carbinol as a treatment for Type II Diabetes finding that it may be an effective treatment.
Read More...The impact of COVID-19 quarantine on physical activities in Basra, Iraq: A cross-sectional study
As the COVID-19 pandemic continues, the authors noticed a change in the physical activity of many people, as well as a change in the type of physical activity they practice. Here, the authors used a cross-sectional survey of 150 participants from the province of Basra in Iraq. They found an overall decrease in the number of days of physical activity for participants along with an increasing proportion of at-home exercises compared to other activities that are performed inside sports clubs during the pandemic.
Read More...Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis
Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.
Read More...Mathematical modeling of plant community composition for urban greenery plans
Here recognizing the importance of urban green space for the health of humans and other organisms, the authors investigated if mathematical modeling can be used to develop an urban greenery management plan with high eco-sustainability by calculating the composition of a plant community. They optimized and tested their model against green fields in a Beijing city park. Although the compositions predicted by their models differed somewhat from the composition of testing fields, they conclude that by using a mathematical model such as this urban green space can be finely designed to be ecologically and economically sustainable.
Read More...The precision of machine learning models at classifying autism spectrum disorder in adults
Autism spectrum disorder (ASD) is hard to correctly diagnose due to the very subjective nature of diagnosing it: behavior analysis. Due to this issue, we sought to find a machine learning-based method that diagnoses ASD without behavior analysis or helps reduce misdiagnosis.
Read More...Investigating the impact of electrocardiography biofeedback on POTS symptom management
The authors test electrocardiography biofeedback as a treatment for individuals with Postural Orthostatic Tachycardia Syndrome.
Read More...Interleukin family (IL-2 and IL-1β) as predictive biomarkers in Indian cancer patients: A proof of concept study
Here, recognizing that the immune response to cancer results in biomarkers that can be used to assess the immune status of cancer patients, the authors investigated the concentrations of key cytokines (TH1 and TH2 cytokines) in healthy controls and cancer patients. They identified significant changes in resting and activated cytokine profiles, suggesting that data of biomarkers such as these could serve as a starting point for further treatment with regard to a patient's specific immune profile.
Read More...A comparative analysis of machine learning approaches for prediction of breast cancer
Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.
Read More...Deciphering correlation and causation in risk factors for heart disease with Mendelian randomization
Here, seeking to identify the risk of coronary artery disease (CAD), a major cause of cardiovascular disease, the authors used Mendelian randomization. With this method they identified several traits such as blood pressure readings, LDL cholesterol and BMI as significant risk factors. While other traits were not found to be significant risk factors.
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