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Record W4402541136 · doi:10.1093/jas/skae234.250

54 Development of an equine metabolism model to describe post-absorptive nutrient dynamics

2024· article· en· W4402541136 on OpenAlexaff
Emily M. Leishman, Priska Darani, Sharifa Darani, Scott Cieslar, J.L. Ellis

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNutrientMetabolismDynamics (music)BiologyChemistryBiochemistryEcologyPhysics

Abstract

fetched live from OpenAlex

Abstract Nutrition modelling is instrumental to modern feed formulation and diet optimization across livestock sectors. Mechanistic models of nutrient digestion, absorption, and metabolism are implemented in industry as ‘decision support systems’, and in academia to summarize and examine our cumulative biological knowledge, identify knowledge gaps and test/evaluate hypotheses. However, comparatively less progress in this field has been made in the equine sector. This limits the ability of the equine sector to address complex challenges such as interactions between equine nutrition, management, health, and welfare. To address this gap, a first iteration of a dynamic, deterministic, mechanistic model describing postabsorptive nutrient dynamics for equines has been developed. Model coding and development was performed using Python with specific libraries for mathematical equations and integration. This model was developed primarily based on extant published mechanistic models of protein and energy metabolism in other species. The present model simulates the partitioning of ingested nutrients into digested nutrients, through intermediary metabolism and ultimately to growth and deposition of protein, fat, water, and ash into the body compartments. As shown in Figure 1, the model takes digested nutrients (protein, fat, starch, glucose, volatile fatty acids) as inputs and contains 12 state variables divided into metabolite pools (amino acids, fatty acids, glucose, glycogen, acetyl-CoA, ATP), and body constituent pools (muscle protein, bone protein, hide protein, viscera protein, body fat, body ash). Ingested feed is partitioned into digested nutrients using digestibility coefficients from extant equine literature, and indigestible nutrients are excreted. Meta-analysis was used to develop empirical equations to predict VFA concentrations in the hindgut. VFA absorption is estimated using Michaelis-Menten kinetics including the pH and hindgut volume, assumed as a constant value for a mature horse Acetyl-CoA, a key intermediary metabolism pool, is produced from amino acid, glucose, and fatty acid catabolism and is consumed for maintenance requirements, which is accounted for in an ATP pool. The ATP pool is a ‘zero pool’, which balances the ATP producing and -consuming reactions in the model, with any remaining ATP balance required being pulled from the acetyl-CoA pool. Turnover of protein and fat is represented by differences in accretion and catabolism. Work is ongoing to evaluate the developed model for model performance using data extracted from the literature. The model will be trained and subsequently evaluated using equine studies in three main areas: growth and body composition, protein turnover and nitrogen balance, and energy metabolism. Developing this model for the equine sector will stimulate and assist in defining future research priorities, inform revisions to the nutrient requirements of horses, increase understanding of metabolic processes as well as related disorders, reduce waste via increased focus on precision feeding and support the development of new products and services to improve equine health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.351
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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