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Record W2313648273 · doi:10.1139/cjpp-2013-0206

Intraluminal hyperglycaemia causes conduit and resistance artery dilatation and inhibits vascular autoregulation in the anaesthetised pig

2013· article· en· W2313648273 on OpenAlexvenueno aff
Therese Ruane-O’Hora, Christine M. Shortt, Deirdre Edge, F. Markos, Mark I. M. Noble

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2013
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
Fundersnot available
KeywordsAutoregulationVasodilationVascular resistanceBlood flowArteryBlood pressureMedicineInternal medicineHemodynamicsCardiologyAnesthesiaEndocrinologyChemistry

Abstract

fetched live from OpenAlex

The effect of intraluminal hyperglycaemia was investigated in the iliac artery of 11 anaesthetised pigs. Following isolation of a test segment, hyperglycaemic blood (40 mmol·L(-1)) caused a significant dilatation of the artery of 167 ± 208 μm (mean ± SD; n = 6, P = 0.031). Dilatations were reduced by N(G)-nitro-l-arginine methyl esther (250 μg·mL(-1)) from 145 ± 199 to 38 ± 5 μm), but this was not statistically significant (n = 6, P = 0.18). Intra-arterial infusions of d-glucose (20-40 mmol·L(-1)·min(-1)), during graded constrictions, caused statistically significant increases in blood flow (n = 11, P = 0.0013). Vasodilatation was confirmed by measurements of the ratio of immediate pressure steps to flow steps (∂P/∂F) during the graded obstruction experiments, showing a decrease in instantaneous vascular resistance from a control of 0.62 ± 0.30 to 0.33 ± 0.34 mm Hg·mL(-1)·min(-1) (n = 7, P = 0.016). Autoregulation was assessed from the slopes of the plots of steady-state flow versus pressure. There were significant increases in the slope from 2.32 ± 1.03 to 5.88 ± 5.60 mL·min(-1)·(mm Hg)(-1) (n = 7, P = 0.0078), indicating significant impairment of autoregulation. In conclusion, luminal hyperglycaemia relaxes both arterial and resistance vessel smooth muscle.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.235

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)0.0000.000

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 head0.012
GPT teacher head0.242
Teacher spread0.231 · 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 designObservational
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

Citations3
Published2013
Admission routes1
Has abstractyes

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