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Propagation of Capillary‐initiated Conducted Vasodilation in Skeletal Muscle – A Novel Paracrine Signaling Pathway

2016· article· en· W4389008276 on OpenAlexafffund
Nicole Novielli, Coral L. Murrant

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPurinergic receptorVasodilationSkeletal muscleArterioleAdenosineInternal medicineEndocrinologyChemistryPurinergic signallingP2 receptorAdenosine receptorMicrocirculationCell biologyReceptorBiologyMedicineAgonist

Abstract

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Coordination of arteriolar vasodilation is required for increases in blood flow to specific capillaries supplying active skeletal muscle fibers. Capillary stimulation results in a directional path of conducted upstream dilation to supplying arterioles resulting in increased capillary perfusion. This demonstrates the prominent role of capillaries directing blood flow recruitment in skeletal muscle tissue; however, mechanisms governing this response are not well defined. Cell membrane hemi‐channels known as pannexins have been implicated in arterial vascular control. Pannexins release adenosine triphosphate (ATP) into the extra‐cellular space and activate purinergic receptors on local and neighbouring cells. Activated purinergic receptors can thereafter promote pannexin‐mediated ATP release and subsequent purinergic receptor activation along capillaries, propagating paracrine cell‐to‐cell communication. We sought to determine the role of endothelial cell purinergic receptors in the propagation of capillary‐initiated conducted vasodilatory responses expressed at supplying terminal arterioles in skeletal muscle. Using intravital video microscopy of the hamster cremaster, we stimulated a local capillary site by (i) micropipette application of vasoactive drugs: potassium chloride (KCl; 10mM; n=5), adenosine (ADO, 10 −4 M; n=6), pinacidil (PIN, 10 −5 M; n=8), S‐nitroso‐N‐acetylpenicillamine (SNAP, nitric oxide donor; 10 −6 M; n=11), and acetylcholine (ACh; 10 −4 mM; n=4); and (2) microelectrode stimulation of skeletal muscle fibers underlying a small group of capillaries (15 contractions per minute, 20Hz contraction frequency, 250ms train duration; n=10). Subsequent diameter change was measured at the upstream supplying arteriole in the absence and presence of suramin (non‐specific P 2x and P 2y antagonist; 10 −5 M) applied via micropipette at a capillary site between the initiating capillary stimulation site and upstream arteriolar observation site. Arteriolar diameter increased significantly by 33% following muscle fiber stimulation, and by 35%, 35%, 32%, 24%, and 33% respectively, following micropipette application of vasoactive drugs (KCl, ADO, PIN, SNAP, and ACh) at the capillary. In the presence of suramin, conducted arteriolar dilatory responses were significantly attenuated by 29% following muscle fiber contraction under capillaries, and by 86%, 45%, 61% and 36% respectively, following micropipette application of KCl, ADO, PIN and SNAP to the capillary. In contrast, suramin had no effect on the conducted vasodilatory responses initiated by capillary exposure to ACh, highlighting the divide between ACh‐dependent mechanisms responsible for propagation of vasodilation at upstream arterioles, and those relevant to vasoactive products of muscle contraction. These novel data demonstrate a role for purinergic receptors in the propagation of a dilatory response along capillaries, expressed at the arteriolar level. Additionally, these findings implicate a novel paracrine signaling pathway in capillary‐initiated conducted vasodilation in skeletal muscle arterioles. Support or Funding Information Research funded by NSERC

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.250
Teacher spread0.222 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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