Childhood Environmental Instabilities and Their Behavioral Implications: A Machine Learning Approach to Studying Adverse Childhood Experiences.

Adverse childhood experiences (ACEs) include a range of abusive, neglectful, and dysfunctional household behaviors that are strongly associated with long-term health problems, mental health conditions, and societal difficulties. The study aims to uncover significant factors influencing ACEs in children aged 0-17 years and to propose a predictive model that can be used to forecast the likelihood of ACEs in children. Machine learning models are applied to identify and analyze the relationships between several predictors and the occurrence of ACEs.

Exploring racial and secondary substance use differences in route of administration of opioid drugs: Analysis of the 2015-2019 treatment admission data.

Introduction: The opioid crisis continues to evolve with increasing opioid-related overdose deaths among under-represented minorities. A better understanding of substance use differences in the route of administration for people using heroin and other opioids can lead to targeted strategies and interventions.

Methods: Using the 2015-2019 Treatment Episode Data Set - Admissions (TEDS-A), a multinomial logistic regression model examined the relationship between race/ethnicity and secondary substance use with route of administration in a subset of 591,078 admissions.

Using machine learning models to predict the quality of plant-based foods.

Plant-based foods (PBFs) are considered healthy, especially, minimally processed whole foods, fruits, whole grains, and legumes while highly processed PBFs maybe less nutritious. Educating consumers on nurient quality will help to guide their choices. This study was aimed at estimating and predicting the nutrient quality of PBFs using their Nutri-Score and micronutrient content.

Classification of oils and margarines by FTIR spectroscopy in tandem with machine learning.

This study assessed the combined utility of ATR-FTIR spectroscopy and machine learning (ML) techniques for identifying and classifying pure njangsa seed oil (NSO), palm kernel oil (PKO), coconut oil (CCO), njangsa seed oil-palm kernel oil (NSOPKO) and njangsa seed oil-coconut oil (NSOCCO) margarine. Additionally, it quantified the degree of adulteration in each oil and margarine using ML regression models and sunflower oil and canola-flaxseed oil margarine as adulterants. Fingerprints of the oils and the margarines derived in the spectra region 4000-600 cm were combined with ML models.

Machine Learning Approaches for Predicting Fatty Acid Classes in Popular US Snacks Using NHANES Data.

In the US, people frequently snack between meals, consuming calorie-dense foods including baked goods (cakes), sweets, and desserts (ice cream) high in lipids, salt, and sugar. Monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) are reasonably healthy; however, excessive consumption of food high in saturated fatty acid (SFA) has been related to an elevated risk of cardiovascular diseases. The National Health and Nutrition Survey (NHANES) uses a 24 h recall to collect information on people's food habits in the US.

Valorization of seed and kernel marcs and evaluation of their antioxidant potential.

The processing and consumption of mango (Mangifera indica) generate a sizeable amount of kernel waste with enormous and largely unexplored potential, while by-products from njangsa (Ricinodendron heudelotii) seed and bush mango (Irvingia gabonensis) kernel oil extraction are often discarded. This study aims to repurpose these kernels and seed wastes into added/high-value products and evaluate the ethanolic and methanolic extracts of their pressed marcs for polyphenolic content and potential antioxidant activity. The total phenolic content (TPC) and total flavonoid content (TFC) in the marc extracts ranged between 47.