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Record W2116615209 · doi:10.1113/jphysiol.2013.264259

Prostaglandins induce vasodilatation of the microvasculature during muscle contraction and induce vasodilatation independent of adenosine

2014· article· en· W2116615209 on OpenAlexafffund
Coral L. Murrant, Jason Dodd, Andrew Foster, Kristin A. Inch, Fiona Muckle, Della A. Ruiz, Jeremy A. Simpson, Jordan H. P. Scholl

Bibliographic record

VenueThe Journal of Physiology · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCremaster muscleVasodilationContraction (grammar)Skeletal muscleMicrocirculationAdenosineChemistryInternal medicineMuscle contractionEndocrinologyArterioleCyclooxygenaseNitric oxideAnatomyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Key points The role of prostaglandins in the changes in blood flow and microvascular vasodilation associated with exercise and muscle contraction is controversial. Whether prostaglandins are produced independently during muscle contraction or whether their production is dependent on the production of adenosine is not well understood. We show that prostaglandins are an important component of the microvascular vasodilation associated with muscle contraction but only under specific contractile conditions. Further, we show that microvascular vasodilation in response to adenosine is not dependent on prostaglandins. Therefore, we conclude that there are specific contractile conditions under which prostaglandins are an important component of the vasodilation induced by muscle contraction and we propose that prostaglandin‐induced microvascular vasodilation during exercise is independent of adenosine. Abstract Blood flow data from contracting muscle in humans indicates that adenosine (ADO) stimulates the production of nitric oxide (NO) and vasodilating prostaglandins (PG) to produce arteriolar vasodilatation in a redundant fashion such that when one is inhibited the other can compensate. We sought to determine whether these redundant mechanisms are employed at the microvascular level. First, we determined whether PGs were involved in active hyperaemia at the microvascular level. We stimulated four to five skeletal muscle fibres in the anaesthetized hamster cremaster preparation in situ and measured the change in diameter of 2A arterioles (maximum diameter 40 μm, third arteriolar level up from the capillaries) at a site of overlap with the stimulated muscle fibres before and after 2 min of contraction [stimulus frequencies: 4, 20 and 60 Hz at 15 contractions per minute (CPM) or contraction frequencies of 6, 15 or 60 CPM at 20 Hz; 250 ms train duration]. Muscle fibres were stimulated in the absence and presence of the phospholipase A 2 inhibitor quinacrine. Further, we applied a range of concentrations of ADO (10 −7 –10 −5 m ) extraluminally, (to mimic muscle contraction) in the absence and presence of l ‐NAME (NO synthase inhibitor), indomethacin (INDO, cyclooxygenase inhibitor) and l ‐NAME + INDO and observed the response of 2A arterioles. We repeated the latter experiment on a different level of the cremaster microvasculature (1A arterioles) and on the microvasculature of a different skeletal muscle (gluteus maximus, 2A arterioles). We observed that quinacrine inhibited vasodilatation during muscle contraction at intermediate and high contraction frequencies (15 and 60 CPM). l ‐NAME, INDO and l ‐NAME + INDO were not effective at inhibiting vasodilatation induced by any concentration of ADO tested in 2A and 1A arterioles in the cremaster muscle or 2A arterioles in the gluteus maximus muscle. Our data show that PGs are involved in the vasodilatation of the microvasculature in response to muscle contraction but did not obtain evidence that extraluminal ADO causes vasodilatation through NO or PG or both. Thus, we propose that PG‐induced microvascular vasodilation during exercise is independent of ADO.

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)0.001
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 score0.688
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.232
Teacher spread0.223 · 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

Citations25
Published2014
Admission routes2
Has abstractyes

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