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Record W2981905744 · doi:10.1113/jp278476

Greater post‐contraction hyperaemia below <i>vs</i>. above heart level: the role of active vasodilatation <i>vs</i>. passive mechanical distension of arterioles

2019· article· en· W2981905744 on OpenAlexafffund
Mytchel J.T. Lynn, Olivia K. Mew, P Drouin, Noah L. Liberman, Michael E. Tschakovsky

Bibliographic record

VenueThe Journal of Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsHyperaemiaDistensionVasodilationMedicineCardiologyIsometric exerciseInternal medicineContraction (grammar)ForearmBlood pressureBlood flowAnesthesiaAnatomy

Abstract

fetched live from OpenAlex

Key points The immediate increase in skeletal muscle blood flow following contraction is greater when the contracting muscle is below vs. above heart level. This has been attributed to muscle pump‐mediated venous emptying and subsequent widening of the arterial to venous pressure gradient, which can occur below but not above heart level. However, alternative explanations could include greater rapid onset vasodilatation and/or transmural pressure‐mediated mechanical distension of resistance vessels, but these remain unexplored. We demonstrate that active vasodilatation is not responsible for greater post‐contraction hyperaemia below the heart. Instead, an increased transmural pressure‐mediated mechanical distension of resistance vessels is a key mechanism responsible for this phenomenon. Our findings establish the importance of considering/accounting for local mechanical arteriolar distension effects when investigating exercise hyperaemia. They also inform the application of exercise for rehabilitative purposes and prompt investigation into whether arteriolar distension accompanying vasodilatation is reduced with diseases or ageing, thereby compromising exercising muscle perfusion. Abstract We tested the hypotheses that increased post‐contraction hyperaemia in higher (H; below heart) vs. lower (L; above heart) transmural pressure conditions is due to (1) greater active vasodilatation or (2) greater transmural pressure‐mediated arteriolar distension. Participants (n = 20, 12 male, 8 female; combined mean age 24.5 ± 2 years) performed a 2 s isometric handgrip contraction, where arm position was maintained within or changed between H and L during contraction, resulting in four starting–finishing arm position conditions (LL, HL, LH, HH). Post‐contraction forearm blood flow (echo and Doppler ultrasound) was higher with contraction release in H vs. L environments (P < 0.05). However, contraction initiated in H did not result in greater vasodilatation (forearm vascular conductance; FVC) than contraction initiated in L, regardless of contraction release condition (peak FVC: LL 217 ± 104 vs. HL 204 ± 92 ml min−1 (100 mmHg)−1, P = 0.313, LH 229 ± 8 vs. HH 225 ± 85 ml min−1 (100 mmHg)−1, P = 0.391; first post‐contraction cardiac cycle FVC: same comparisons, both P = 0.317). However, FVC of the first post‐contraction cardiac cycle was greater for contractions released in H vs. L regardless of pre‐contraction condition (LL 106 ± 67 vs. LH 152 ± 76 ml min−1 (100 mmHg)−1, P < 0.05; HL 80 ± 51 vs. HH 119 ± 58 ml min−1 (100 mmHg)−1, P < 0.05). These findings refute the hypothesis that greater hyperaemia following a single contraction in higher transmural pressure conditions is due to greater active vasodilatation. Instead, our findings reveal a key role for increased transmural pressure‐mediated mechanical distension of arterioles in creating a greater increase in vascular conductance for a given active vasodilatation following skeletal muscle contraction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.232
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2019
Admission routes2
Has abstractyes

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