Background: Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is simply not scalable, while deterministic automated approaches to nutrition coaching may lack the personalization needed to address these diverse challenges.
Objective: We report the development and validation of a novel large language model (LLM)-powered agentic workflow designed to provide personalized nutrition coaching by directly identifying and mitigating patient-specific barriers.
The present study sought to intensify oestrus symptoms in heat-stressed pre-pubertal Murrah buffalo heifers. The first experiment aimed at lowering the blood cortisol level. Twenty pre-pubertal buffalo heifers approximately 36-40 months of age were randomly allocated to four groups of five buffaloes.
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