Evidence map›Paper›PMID 40723589›Full record

ArticleAnimals : an open access journal from MDPI2025

Characterizing the Dynamic Protein and Amino Acid Deposition in Tissues of Pregnant Gilts: Implications for Stage-Specific Nutritional Strategies.

Christian D Ramirez-Camba, Pedro E Urriola, Crystal L Levesque

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christian D Ramirez-CambaDepartment of Animal Science, University of Minnesota, St. Paul, MN 55108, USA.ORCID 0009-0000-3070-3506
Pedro E UrriolaDepartment of Animal Science, University of Minnesota, St. Paul, MN 55108, USA.ORCID 0000-0003-4090-6627
Crystal L LevesqueDepartment of Animal Science, South Dakota State University, Brookings, SD 57007, USA.ORCID 0000-0003-2978-5852

Funding

Foundation for Food and Agriculture Research New Investigator AwardNational Pork Board through the Real Pork Trust Consortium 0USDA National Institute of Food and Agriculture SD00H666-18
6 · The paper itself

Abstract

Understanding protein and amino acid deposition in pregnant gilts is important for developing nutritional strategies that meet these demands and enhance reproductive performance. Current models, such as the NRC (2012) gestating sow model, assume a constant proportional protein and amino acid content in tissues throughout pregnancy. However, empirical data suggest that gestational tissue growth and composition change dynamically. In this study, we developed a gestation model that characterizes the dynamic changes in growth, crude protein, and amino acid deposition throughout gestation. Based on a systematized search of published data, mathematical functions were developed to estimate daily protein and amino acid deposition in key tissues, including allantoic and amniotic fluid, uterus, placenta, fetus, mammary gland, and maternal body. Our results suggest that dietary crude protein levels and amino acid profiles should be adjusted to meet metabolic demands, particularly in early gestation, where a potential nutritional deficiency was identified. Additionally, the amino acid profile of deposited protein shifts during late gestation, suggesting a changing demand for specific amino acids. These findings challenge existing models and highlight the need for adaptive dietary strategies that better align with pregnancy's biological demands. By refining protein and amino acid deposition estimates, this study provides a framework guiding future research on precision feeding, ultimately improving gilt and sow reproductive performance.

Indexed as

amino acid requirementsdietary optimizationgestation modelgilt growthprotein depositionreproductive performanceswine nutrition

Identifiers

PMID40723589
PMCPMC12291760

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.