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Renal Haemodynamic Effects of B<sub>2</sub> Receptor Agonist Bradykinin and B<sub>2</sub> Receptor Antagonist HOE 140 in Conscious Lambs

2000· article· en· W2141525766 on OpenAlexaff
Avni Patel, Francine G. Smith

Bibliographic record

VenueExperimental Physiology · 2000
Typearticle
Languageen
FieldMedicine
TopicRenin-Angiotensin System Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBradykininInternal medicineEndocrinologyRenal blood flowAgonistVascular resistanceKidneyAntagonistHemodynamicsBlood pressureRenal circulationReceptor antagonistReceptorMedicineMean arterial pressureHeart rate

Abstract

fetched live from OpenAlex

The present study was designed to test the hypothesis that the high renal vascular resistance characteristic of the newborn results from age-dependent changes in the responsiveness of the renal vasculature to kinins. Two studies were carried out in conscious, chronically instrumented lambs aged 1 and 6 weeks. Firstly, we measured the renal blood flow response to intra-arterial injection of the B2 receptor agonist bradykinin over the range of doses 0-800 ng x kg(-1). The ED50 renal blood flow response to bradykinin was 50 ng x kg(-1) in both age groups of lambs. Secondly, we measured the effects of intravenous administration of 12.5 microg x kg(-1) of the specific B2 receptor antagonist HOE 140; this dose attenuated the renal blood flow response to 50 ng x kg(-1) of bradykinin in both age groups. HOE 140 administration was associated with an age-dependent increase in mean arterial pressure, with little effect on heart rate or renal vascular resistance. This study provides new information regarding the effects of kinins in modulating renal haemodynamics during postnatal maturation. We reject our hypothesis and conclude that the high renal vascular resistance of the newborn does not appear to result from age-dependent changes in the responsiveness of the renal vasculature to endogenous kinins.

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 categoriesMeta-epidemiology (narrow)
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 score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
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)0.0000.000

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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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.

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

Citations10
Published2000
Admission routes1
Has abstractyes

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