Diet & Nutrition

Deep learning-assisted Raman spectroscopy for automated identification of specific minerals.

Raman spectroscopy is applied as an important method for material identification in field geology. However, analyzing the collected Raman spectroscopy results is time-consuming and labor-intensive, which arises a demand for labeling and sorting a large volume of in-situ Raman measurements automatically. In this study, we consider the spectral characteristics of mineral to develop a convolutional a...

Identification of hub biomarkers in liver post-metabolic and bariatric surgery using comprehensive machine learning (experimental studies).

BACKGROUND: The global prevalence of non-alcoholic fatty liver disease (NAFLD) is approximately 30%, and the condition can progress to non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. Metabolic and bariatric surgery (MBS) has been shown to be effective in treating obesity and related disorders, including NAFLD.

Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning.

Waist-to-hip ratio (WHR) is an essential predictor of cardiometabolic diseases, but traditional tape-based WHR measurements in children and adolescent...

Machine Learning Analysis of Nutrient Associations with Peripheral Arterial Disease: Insights from NHANES 1999-2004.

BACKGROUND: Peripheral arterial disease (PAD) is a common manifestation of atherosclerosis, affecting over 200 million people worldwide. The incidence...

Risk factor assessment of prediabetes and diabetes based on epidemic characteristics in new urban areas: a retrospective and a machine learning study.

To explore in depth the characteristics of the risk factors for diabetes and prediabetes pathogenesis and progression in special regions. We investiga...

Machine learning models for water safety enhancement.

Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human acti...

The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis.

BACKGROUND: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resis...

Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models.

Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease m...

Comparative analysis of phytonutrients of leaves from South Africa and Nigeria, and their antimicrobial and antioxidant potentials by UPLC-ESI-QToF-MS and OPLS-DA chemometric analysis.

BACKGROUND: Lam. has bioactive phytonutrients in abundance and offers diverse health benefits. The leaves of this plant have established significance...

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of...

Artificial intelligence-enabled obesity prediction: A systematic review of cohort data analysis.

BACKGROUND: Obesity, now the fifth leading global cause of death, has seen a dramatic rise in prevalence over the last forty years. It significantly i...

Application of machine learning and genomics for orphan crop improvement.

Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop impr...

Optimization of dried garlic physicochemical properties using a self-organizing map and the development of an artificial intelligence prediction model.

The experiments were conducted at different levels of infrared power, airflow, and temperature. The relationships between the input process factors an...

Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

BACKGROUND: Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition ...

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study.

BACKGROUND: Type 2 diabetes (T2D) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the wor...

Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes.

OBJECTIVE: This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in ...

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality o...

Predicting metabolite response to dietary intervention using deep learning.

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods a...

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report.

BackgroundIntensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanic...

The status of serum 25(OH)D levels is related to breast cancer.

AIM: Breast cancer is the second most common cancer among women and the leading cause of cancer-related mortality in this population. Numerous factors...