Diet & Nutrition

Effects of super high-flux vitamin E-coated and medium cut-off dialyzers on uremic toxins removal and biocompatibility: the E-FLUX randomized controlled study.

BACKGROUND: Medium cut-off hemodialysis (MCO-HD) improves removal of middle molecules (MM) uremic toxins. The effects of the novel super high-flux vitamin E-coated (SHFVE) dialyzer on MM removal and biocompatibility parameters including inflammation and oxidative stress remain to be investigated.

Modeling PFAS Sorption in Soils Using Machine Learning.

In this study, we introduce PFASorptionML, a novel machine learning (ML) tool developed to predict solid-liquid distribution coefficients () for per- and polyfluoroalkyl substances (PFAS) in soils. Leveraging a data set of 1,274 entries for PFAS in soils and sediments, including compounds such as trifluoroacetate, cationic, and zwitterionic PFAS, and neutral fluorotelomer alcohols, the model inco...

Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.

Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent ...

The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.

The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus ...

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few stu...

Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.

OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth re...

Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.

Chronic kidney disease-mineral bone disorder is a common complication in patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD...

The Influence of an AI-Driven Personalized Nutrition Program on the Human Gut Microbiome and Its Health Implications.

Personalized nutrition programs enhanced with artificial intelligence (AI)-based tools hold promising potential for the development of healthy and su...

Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding.

Traditional feeding strategy during lactation can result in nutrient deficiencies and negatively impact long-term productivity, compromising both the ...

Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study.

BACKGROUND: Metabolic syndrome (MetS) is a complex condition that captures several markers of dysregulation, including obesity, elevated blood glucose...

Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach.

The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation...

Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women.

The aim of this study was to establish the optimal prediction model by comparing the prediction effect of 6 kinds of prediction models containing bioc...

The interpretable machine learning model for depression associated with heavy metals via EMR mining method.

Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient mach...

AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.

Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and data-driven ...

Human bone ultrastructure in 3D: Multimodal correlative study combining nanoscale X-ray computed tomography and quantitative polarized Raman spectroscopy.

Unique mechanical properties of cortical bone are defined by the arrangement and ratio of its organic and inorganic constituents. This arrangement can...

Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permane...

Exploring the impact of bioactive peptides from fermented Milk proteins: A review with emphasis on health implications and artificial intelligence integration.

This review explores the health benefits of bioactive peptides (BAPs) from fermented milk proteins, emphasizing the transformative role of artificial ...

Can AI reveal the next generation of high-impact bone genomics targets?

Genetic studies have revealed hundreds of loci associated with bone-related phenotypes, including bone mineral density (BMD) and fracture risk. Howeve...

Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland.

For efficient decision-making and optimal land management trajectories, information on soil properties in relation to safety guidelines should be proc...

Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children.

BACKGROUND: Child nutrition in Ethiopia is a significant concern, particularly for preschool-aged children. Children must have a varied diet to ensure...