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Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks.
Trop Anim Health Prod
January 2025
Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243 122, India.
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model.
View Article and Find Full Text PDFDietary Disruptors in Romania: Seasonality, Traditions, and the COVID-19 Pandemic.
Nutrients
January 2025
Center for Health Outcomes & Evaluation, Splaiul Unirii 45, 030126 Bucharest, Romania.
Background: The global rise in obesity has been significantly influenced by shifts in dietary habits that have been exacerbated by external factors such as the COVID-19 pandemic. This study aims to analyze the trends in Romanian dietary habits from 2015 to 2023, focusing on the impact of the COVID-19 pandemic and the role of socio-economic factors, seasonality, and cultural practices.
Methods: For dietary habits, we used nationally representative data from the Romanian Household Budget Survey provided by the Romanian National Institute of Statistics.
The relationship between the three-factor eating questionnaire (TFEQ) subscales (dietary restraint, disinhibition, and hunger) and the body mass index: A cross-sectional study among female students.
Heliyon
December 2024
Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, PO Box 11433, Saudi Arabia.
Background: Identification of an individuals vulnerability to specific eating behaviours could explain weight variations, which could help develop tailored interventional programs to prevent obesity and other pathological eating behaviours. However, there is no baseline data available on the associations between the subscales of the Three-Factor Eating Questionnaire (TFEQ) (dietary restraint, disinhibition, and susceptibility to hunger) and the body mass index (BMI) in a sample of the Saudi population. Thus, this study examines the relationship between the BMI and eating behaviours of Saudi female students, using the scores of the TFEQ subscales.
View Article and Find Full Text PDFArticle Synopsis
- Leprosy is a significant public health issue in Ethiopia, causing social stigma and poor quality of life, emphasizing the need for collaborative actions to address its impact in endemic regions.
- A cross-sectional study involving 256 leprosy-suspected cases was conducted to assess the acid-fast positivity rate and identify factors contributing to the disease, using interviews and laboratory testing.
- The study found a 19.6% positivity rate for leprosy, with significant contributing factors including being male, living in rural areas, poor diet, inadequate hygiene, and co-infections.
Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study.
JMIR Mhealth Uhealth
September 2024
Emteq Ltd., Brighton, United Kingdom.
Background: The increasing prevalence of obesity necessitates innovative approaches to better understand this health crisis, particularly given its strong connection to chronic diseases such as diabetes, cancer, and cardiovascular conditions. Monitoring dietary behavior is crucial for designing effective interventions that help decrease obesity prevalence and promote healthy lifestyles. However, traditional dietary tracking methods are limited by participant burden and recall bias.
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