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Record W2908520994 · doi:10.1139/cjpp-2018-0716

Comparative effects of nitric oxide dependent and independent vasodilation on impaired hindlimb revascularization in eNOS<sup>−/−</sup> mice

2019· article· en· W2908520994 on OpenAlexvenueno aff
Manuel Martínez-Ruiz, Hilda Vargas‐Robles, Amelia Rı́os, Daniel Sánchez, Bruno Escalante

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2019
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsVasodilationEnosArteriogenesisHindlimbNitric oxideIschemiaInternal medicineMedicineAngiogenesisCardiologyNeovascularizationPerfusionHemodynamicsNitric oxide synthaseEndocrinologyAnesthesia

Abstract

fetched live from OpenAlex

Ischemia due to vascular occlusion induces vasodilation as an initial response, followed by arteriogenesis or angiogenesis. Vasodilation through nitric oxide (NO) independent and dependent mechanisms may be sufficient to restore the altered neovascularization in pathological situations where the NO is altered. Using a posterior limb claudication model to evaluate ischemia-induced revascularization in eNOS −/− mice, we compared the effects of sodium nitrite, a NO-dependent vasodilator, and prazocin, an alpha-adrenergic blocker and NO-independent vasodilator, on hindlimb revascularization. We evaluated the blood flow of the hindlimbs, NO and nitrites metabolites, the expression of tissue endothelial cell markers and proangiogenic factors, as well as the gait locomotion. Our results suggest that the use of a peripheral vasodilator can substitute the initial absence of NO as an endogenous vasodilator. However, final resolution of the ischemic process requires a NO-mediated pathway, which through changes in vascular hemodynamics, promotes the generation of angiogenic messengers facilitating the functional recovery of the damaged limb.

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 score0.854
Threshold uncertainty score0.442

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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