AIMC Topic: Diet
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Harnessing the fish gut microbiome and immune system to enhance disease resistance in aquaculture.
Fish & shellfish immunology
Aug 1, 2025
The increasing global reliance on aquaculture is challenged by disease outbreaks, exacerbated by antibiotic resistance, and environmental stressors. Traditional strategies, such as antibiotic treatments and chemical interventions, are becoming less e...
From traditional to artificial intelligence-driven approaches: Revolutionizing personalized and precision nutrition in inflammatory bowel disease.
Clinical nutrition ESPEN
Aug 1, 2025
Inflammatory bowel disease (IBD), comprising ulcerative colitis and Crohn's disease, is a chronic inflammatory condition with global prevalence and varying incidence. The IBD pathogenesis involves intricate interactions among genetic, host and enviro...
Associations of dietary patterns with serum 25(OH) vitamin D and serum anemia related biomarkers among expectant mothers: A machine learning based approach.
International journal of medical informatics
Jul 1, 2025
BACKGROUND: Machine learning algorithms (MLA) gained prominence in nutritional epidemiology for analyzing dietary associations and uncovering intricate patterns within data. We explored dietary patterns associated with serum iron biomarkers and vitam...
Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods.
Journal of food science
Jun 1, 2025
Mental health disorders like depression, anxiety, and stress (DAS) are rising globally. Understanding how diet and lifestyle influence these conditions is vital for targeted interventions. This study explores the potential of machine learning (ML) to...
NLP for computational insights into nutritional impacts on colorectal cancer care.
SLAS technology
Jun 1, 2025
Colorectal cancer (CRC) is one of the most prominent cancers globally, with its incidence rising among younger adults due to improved screening practices. However, existing algorithms for CRC prediction are frequently trained on datasets that primari...
DiMB-RE: mining the scientific literature for diet-microbiome associations.
Journal of the American Medical Informatics Association : JAMIA
Jun 1, 2025
OBJECTIVES: To develop a corpus annotated for diet-microbiome associations from the biomedical literature and train natural language processing (NLP) models to identify these associations, thereby improving the understanding of their role in health a...
Boosting Immunity Through Nutrition and Gut Health: A Narrative Review on Managing Allergies and Multimorbidity.
Nutrients
May 15, 2025
The increasing global burden of allergic diseases and multimorbidity underscores the urgent need for innovative strategies to strengthen immune health. This review explores the complex relationships among nutrition, gut microbiota, immune regulation,...
Artificial Intelligence-Based Diets: A Role in the Nutritional Treatment of Metabolic Dysfunction-Associated Steatotic Liver Disease?
Journal of human nutrition and dietetics : the official journal of the British Dietetic Association
Apr 1, 2025
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global health concern. Effective management of this condition relies heavily on lifestyle modifications and dietary interventions. In this study, we sought to e...
Challenges for Predictive Modeling With Neural Network Techniques Using Error-Prone Dietary Intake Data.
Statistics in medicine
Feb 28, 2025
Dietary intake data are routinely drawn upon to explore diet-health relationships, and inform clinical practice and public health. However, these data are almost always subject to measurement error, distorting true diet-health relationships. Beyond m...
Predicting dry matter intake in cattle at scale using gradient boosting regression techniques and Gaussian process boosting regression with Shapley additive explanation explainable artificial intelligence, MLflow, and its containerization.
Journal of animal science
Jan 4, 2025
Dry matter intake (DMI) is a measure critical to managing and evaluating livestock. Methods exist for quantifying individual DMI in dry lot settings that employ expensive intake systems. No methods exist to accurately measure individual DMI of grazin...
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