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Record W3000458787 · doi:10.3168/jds.2019-17249

Postruminal infusions of amino acids or glucose affect metabolisms of splanchnic, mammary, and other peripheral tissues and drive amino acid use in dairy cows

2020· article· en· W3000458787 on OpenAlexaff
C. Omphalius, Sophie Lemosquet, D.R. Ouellet, Lahlou Bahloul, Hélène Lapierre

Bibliographic record

VenueJournal of Dairy Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSplanchnicInternal medicineEndocrinologyCatabolismMammary glandFlux (metallurgy)ChemistryAmino acidBiologyMetabolismBiochemistryBlood flowMedicine

Abstract

fetched live from OpenAlex

Effects of AA and glucose infusions on efficiency of use of essential AA (EAA) were studied according to a 2 × 2 factorial using 5 multicatheterized cows in a 4 × 4 Latin square plus one cow, with 2-wk periods. The diet provided 87% of energy and 70% of metabolizable protein requirements, and the 4 treatments were abomasal infusions of (1) water, (2) an AA mixture with a casein profile (695 g/d), (3) glucose (1,454 g/d), or (4) a combination of AA and glucose infusions. Milk samples were collected on the last 6 milkings. On d 14, 6 blood samples were collected from arterial, and portal, hepatic, and mammary venous vessels. Splanchnic plasma flow was calculated by dilution of p-aminohippurate and mammary flow by the Fick principle using Phe + Tyr. The net flux of AA across tissues [splanchnic, i.e., portal-drained viscera (PDV) + liver, and mammary gland] was calculated as the efflux minus the influx across that tissue. The efficiency of EAA was calculated as the sum of exported true proteins [milk protein yield (MPY), scurf, and metabolic fecal protein] multiplied by their respective AA profile and divided by the predicted AA supply minus AA endogenous urinary loss. In addition, catabolism was estimated for each tissue: AA supply - (portal net flux + metabolic fecal protein) for the PDV; -hepatic net flux for the liver; splanchnic net flux - (-mammary net flux + scurf) for the other peripheral tissues; and -mammary net flux - milk for the mammary gland. The MIXED procedure (SAS Institute Inc., Cary, NC) was used with cow as a random effect. No AA × glucose interaction existed for most of the measured parameters. With infusions of AA and glucose, MPY increased by 17 and 14%, respectively. The decreased efficiency of EAA-N with AA infusion resulted from increased EAA-N in MPY smaller than the increased EAA-N supply and was accompanied by increased liver catabolism of His + Met + Phe (representing group 1 AA) and increased mammary and PDV catabolisms of group 2 AA-N (Ile, Leu, Lys, and Val). In contrast, the increased efficiency of EAA-N with glucose infusion, resulting from increased EAA-N in MPY with no change in EAA-N supply, was accompanied by decreased mammary catabolism of group 2 AA-N and hepatic catabolism of His + Met + Phe. No mammary catabolism of His, Met, and Phe existed in all treatments, as indicated by the mammary uptake to milk output ratio close to one for these EAA. Therefore, the mammary gland contributes significantly to variations of efficiency of group 2 AA-N through variations of AA catabolism, in response to both AA and glucose supplies, whereas additional PDV catabolism was observed with increased AA supply. Partition of AA use between tissues allows to delineate their anabolic or catabolic fate across tissues and better understand changes of efficiency of EAA in response to protein and energy supplies.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.265
Teacher spread0.233 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations23
Published2020
Admission routes1
Has abstractyes

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