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Muscle Sympathetic Nerve Activity and Vascular Conductance Responses at the Onset of Arm and Leg Exercise in the Inactive Limb

2019· article· en· W3177033629 on OpenAlexafffundabout
Trevor J. King, Connor J. Doherty, Philip J. Millar

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroneurographyMedicineHemodynamicsBlood flowCardiologyBlood pressureBrachial arteryHeart rateInternal medicinePhysical therapyBaroreflex

Abstract

fetched live from OpenAlex

Local mechanical and metabolic factors are known to regulate the blood flow response at the onset of exercise. However, little is known regarding the contribution of muscle sympathetic nerve activity (MSNA) to this onset blood flow response. The aim of the study was to examine the MSNA and vascular conductance response to exercise anticipation and onset in the inactive leg. Two identical visits were performed on 16 participants (n = 6 female). During visit 1, hemodynamic (Finometer) and MSNA (fibular nerve microneurography) measurements were acquired during rest (1‐minute), exercise anticipation (verbal preparation, 30‐seconds), and 1 minute of rhythmic handgrip (RHG, 1:1 duty cycle, 40% MVC) and one‐legged cycling (17 ± 3% peak power output). During visit 2, hemodynamic and superficial femoral artery (SFA) blood flow (Doppler ultrasound) measurements were acquired during the same exercise modes. Leg vascular conductance was calculated as SFA mean blood flow/mean arterial pressure. Total MSNA (burst frequency x burst area) was analyzed in 15‐second moving windows (5‐second progressions), and SFA blood flow was analyzed as 5‐second time bins aligned with the last 5 seconds of each MSNA moving window; both were expressed as relative change from baseline (%). Burst frequency, burst incidence, and total MSNA (height) followed the same pattern as total MSNA (area), and thus only total MSNA (area) is presented. Significance was set as p < 0.05, and data are mean ± SE. When participants were prompted to prepare for upcoming exercise (i.e. anticipation), total MSNA decreased from baseline prior to RHG (−40 ± 6%, p < 0.001) and cycling (−20 ± 6%, p = 0.03). During anticipation of RHG, this decrease in vasoconstrictor drive was followed 5 seconds later by a trend for increased SFA vascular conductance (+24 ± 12%, p = 0.07). At the onset of exercise, total MSNA decreased during RHG (−28 ± 6%, p = 0.002) and cycling (−39 ± 7%, p = 0.009). This was followed by an increase in SFA vascular conductance during both RHG (+34 ± 12%, p = 0.02) and cycling (+34 ± 20%, p = 0.005) conditions. In the absence of mechanical or metabolic factors in the inactive limb, these data suggest that a reduction in total MSNA can contribute to the exercise hyperemia that is observed at the onset of exercise. Furthermore, the anticipatory response of MSNA suggests a partial contribution of feedforward central command in this response. Support or Funding Information Natural Science and Engineering Research Council of Canada Ontario Ministry of Research, Innovation, and Science Canadian Foundation for Innovation This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.267
Teacher spread0.250 · 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 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

Citations0
Published2019
Admission routes3
Has abstractyes

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