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Record W2068566204 · doi:10.1139/y07-074

Kappa opioids modulate the arterial baroreflex control of heart rate in conscious young sheep

2007· article· en· W2068566204 on OpenAlexafffundvenue
Qi Wei, Francine G. Smith

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2007
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsBaroreflexHeart rateBlood pressureMedicineAnesthesiaMean arterial pressureOpioidInternal medicineReceptor

Abstract

fetched live from OpenAlex

The present study tested the hypothesis that kappa-opioids modulate the arterial baroreflex control of heart rate in conscious young sheep. Various parameters governing the arterial baroreflex control of heart rate were assessed before and after activation of kappa-opiate receptors (KOR) by i.v. administration of the specific KOR agonist U-50488H (experiment 1) or vehicle (experiment 2) to conscious, chronically instrumented lambs aged 42 +/- 2 days (n = 6). The 2 experiments were administered in random order at minimum intervals of 48 h. Thirty min after U-50488H treatment, there was an increase in diastolic and mean arterial pressure and in heart rate, returning to control levels by 90 min. A significant increase in the arterial pressure at the midpoint of the baroreflex range and in the minimum heart rate as well as a significant decrease in the heart rate range over which the arterial baroreflex operates were also seen at 30 min after U-50488H, gradually returning to control levels over 120 min. Vehicle had no effect on any of the parameters governing the arterial baroreflex control of heart rate. These data provide the first direct evidence that under physiological conditions in young lambs, the arterial baroreflex control of heart rate is altered after administration of the specific KOR agonist U-50488H, revealing a previously unidentified role for this opioid receptor.

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)0.002
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 score0.856
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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
Published2007
Admission routes3
Has abstractyes

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