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Record W3159246360 · doi:10.3390/ani11061545

Appeasing Pheromones against Bovine Respiratory Complex and Modulation of Immune Transcript Expressions

2021· article· en· W3159246360 on OpenAlexaff
Caroline Hervet, Justine Boullier, Marlène Guiadeur, Léa Michel, Laure Brun-Lafleur, Anne Aupiais, Jianzhong Zhu, Béatrice Mounaix, François Meurens, Fanny Renois, Sébastien Assié

Bibliographic record

VenueAnimals · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsImmune systemPheromoneSex pheromoneBiologyAntimicrobialRespiratory systemMicrobiologyImmunologyZoologyEcologyAnatomy

Abstract

fetched live from OpenAlex

Bovine respiratory disease is still a major concern and has major economic impact. Another consequence of respiratory infections is the use of antimicrobial molecules to control bacterial pathogens. This can participate in the emergence and shedding of antimicrobial resistance that can threaten animal as well as human health. Appeasing pheromones with their capacity to reduce stress and thus their ability to preserve the functions of the immune system have been proposed to reduce the use of antimicrobial substances. In this study, we assessed the effect of appeasing pheromone administration on bovine health and performance during the fattening period. Zootechnical and health parameters and whole blood immune transcript expressions were measured over four weeks in bulls to determine the effect of the pheromone. We observed increased clinical signs on Day 8 (D8) and decreased clinical signs on D30 in bulls who received the pheromone and a higher expression of interleukin 8 transcripts in this group than in the control group on D8. Our results are overall in line with previous reports in livestock species. Further studies are needed to shed more light on the effect of appeasing pheromones and decipher their exact mechanisms of action.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.087
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

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 head0.051
GPT teacher head0.302
Teacher spread0.251 · 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 teacher head, 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

Citations8
Published2021
Admission routes1
Has abstractyes

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