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Record W4405484888 · doi:10.3168/jds.2024-25362

Impact of a reduced metabolizable protein supply during the transition period on the immune and metabolic status of dairy cows

2024· article· en· W4405484888 on OpenAlexafffund
G Tapp, P. Lacasse, Frédéric Beaudoin, D.R. Ouellet, François Malouin, Céline Ster

Bibliographic record

VenueJournal of Dairy Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsAgriculture and Agri-Food CanadaUniversité de Sherbrooke
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPeriod (music)Immune statusDairy cattleAnimal scienceDairy industryImmune systemMilk proteinFood scienceChemistryBiologyImmunology

Abstract

fetched live from OpenAlex

During the transition period, the incidence of diseases increases due to a negative energy balance that affects metabolic and immune status. Limiting milk production at the beginning of lactation by milking once a day or by incompletely milking twice a day improves the metabolic and immune status of cows. Previous studies have shown that milk production is highly responsive to additional MP. Therefore, reducing MP intake could provide an alternative strategy. This study evaluated whether reducing the MP supply during the transition period could limit milk yield temporarily and thereby improve the cows' metabolic and immune status. A first group of cows (n = 11, 80% MP) was fed a diet formulated to supply 80% of their MP needs during the treatment period (from 10 d before expected calving to 14 DIM) while they received a diet formulated to supply 100% of their MP needs before treatment (adaptation, d 28 to d 11 before expected calving) and after (follow-up, 15 to 28 DIM) the treatment period. A second group of cows (n = 12, 100% MP) was fed a diet formulated to supply 100% of their MP needs during the 3 periods. Blood samples were taken on average at d 26 ± 3.6; d 19 ± 3.7; d 12 ± 3.6; d 9 ± 3.6 before calving and at 2, 5, 7, 10, 14, 21, and 28 DIM to assess the metabolic and immune status. Milk samples were collected at the same frequency to assess milk composition. The milk production of the 80% MP cows was numerically smaller than that of the 100% MP cows (2.4 kg/d) from d 0 to 14, but not after the MP restriction ended. The 80% MP cows had numerically more plasma BHB during the treatment period than the 100% MP cows (694.6 vs. 574.0 ± 389.8 µM, respectively). The 80% MP cows had significantly less glucose on d 7 and d 10 than the 100% MP cows (3.14 vs. 3.44 ± 0.06 and 3.15 vs. 3.42 ± 0.07 mM, respectively). Throughout the 3 experimental periods, no differences were observed for nonesterified fatty acids or for some of the markers of the mobilization of muscle reserves. However, the concentration in 3-methyl-His was greater for the 80% MP cows during treatment and was not different during the follow-up period. The PMN oxidative burst of the 80% MP cows was numerically greater during treatment and on d 21. The treatment did not alter the proliferation of peripheral blood mononuclear cells or PMN phagocytosis capacity. In conclusion, a 20% reduction in MP supply during the transition period did decrease milk production, but this decrease was not sufficient to improve energy balance and to reduce metabolic and immune disturbances.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
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)0.0010.000

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 head0.017
GPT teacher head0.261
Teacher spread0.243 · 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 designObservational
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

Citations4
Published2024
Admission routes2
Has abstractyes

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