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Record W2529996144 · doi:10.3168/jds.2016-11711

Effect of reducing milk production using a prolactin-release inhibitor or a glucocorticoid on metabolism and immune functions in cows subjected to acute nutritional stress

2016· article· en· W2529996144 on OpenAlexafffund
S. Ollier, Frédéric Beaudoin, N. Vanacker, P. Lacasse

Bibliographic record

VenueJournal of Dairy Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversité de SherbrookeAgriculture and Agri-Food Canada
FundersNational Research Council CanadaAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaCommission on Higher Education
KeywordsNEFAProlactinEndocrinologyInternal medicineLactationDry matterAnimal scienceChemistryInsulinBiologyHormoneMedicinePregnancy

Abstract

fetched live from OpenAlex

When cows are unable to consume enough feed to support milk production, they often fall into severe negative energy balance. This leads to a weakened immune system and increases their susceptibility to infectious diseases. Reducing the milk production of cows subjected to acute nutritional stress decreases their energy deficit. The aim of this study was to compare the effects on metabolism and immune function of reducing milk production using quinagolide (a prolactin-release inhibitor) or dexamethasone in feed-restricted cows. A total of 23 cows in early/mid-lactation were fed for 5 d at 55.9% of their previous dry matter intake to subject them to acute nutritional stress. After 1 d of feed restriction and for 4 d afterward (d 2 to 5), cows received twice-daily i.m. injections of water (control group; n=8), 2mg of quinagolide (QN group; n=7), or water after a first injection of 20mg of dexamethasone (DEX group; n=8). Feed restriction decreased milk production, but the decrease was greater in the QN and DEX cows than in the control cows on d 2 and 3. As expected, feed restriction reduced the energy balance, but the reduction was lower in the QN cows than in the control cows. Feed restriction decreased plasma glucose concentration and increased plasma nonesterified fatty acid (NEFA) and β-hydroxybutyrate (BHB) concentrations. The QN cows had higher glucose concentration and lower BHB concentration than the control cows. The NEFA concentration was also lower in the QN cows than in the control cows on d 2. Dexamethasone injection induced transient hyperglycemia concomitant with a reduction in milk lactose concentration; it also decreased BHB concentration and decreased NEFA initially but increased it later. Feed restriction and quinagolide injections did not affect the blood concentration or activity of polymorphonuclear leukocytes (PMN), whereas dexamethasone injection increased PMN blood concentration but decreased the proportion of PMN capable of inducing oxidative burst. Incubation of peripheral blood mononuclear cells in serum harvested on d 2 of the restriction period reduced their ability to react to mitogen-induced proliferation, and injection of quinagolide or dexamethasone could not alleviate this effect. This experiment shows that prolactin-release inhibition could be an alternative to dexamethasone for reducing milk production and energy deficit in cows under acute nutritional stress, without disturbing immune function.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.267
Teacher spread0.250 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

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