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Cortisol in Saliva and Plasma of Cattle After ACTH Administration and Milking

2004· article· en· W2166711543 on OpenAlexaff
João Alberto Negrão, Marco Aurélio de Felício Porcionato, A.M. de Passillé, J. Rushen

Bibliographic record

VenueJournal of Dairy Science · 2004
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsAgriculture and Agri-Food Canada
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSalivaMilkingEndocrinologyInternal medicineHydrocortisoneBlood samplingMedicineChemistryBiologyAnimal science

Abstract

fetched live from OpenAlex

Interest in the measurement of salivary cortisol has increased recently because saliva can be easily collected before and after an imposed stress. This study evaluated the relationship between plasma and salivary concentrations of cortisol following ACTH administration in calves (experiment 1) and machine milking of adult cows (experiment 2). A catheter was inserted into the jugular vein of all animals 72 h before the beginning of experiments. Blood and saliva samples were collected before and after ACTH administration (0.6 IU/kg BW) in calves or before and after machine milking of cows. Using a cotton swab, each saliva sample was taken immediately following the blood sample. In general, cortisol profiles were similar in plasma and saliva and correlated in both experiments; however, plasma concentrations were significantly higher than salivary concentrations. In addition, the differences between cortisol concentrations measured in saliva and plasma within each experiment varied substantially between animals and samples. Furthermore, in experiment 2, nearly 10% of salivary samples were below limits of detection. The sharp peaks in cortisol after ACTH administration in both the plasma and saliva were reflected adrenal stimulation. In addition, increases in cortisol in response to milking in both the plasma and saliva suggest that salivary sampling is a reliable option when studying cortisol responses to normal physiological events.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.191

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.287
Teacher spread0.265 · 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

Citations135
Published2004
Admission routes1
Has abstractyes

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