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Effects of Dynamic Arm and Leg Exercise on Muscle Sympathetic Nerve Activity and Vascular Conductance in the Inactive Leg

2019· article· en· W2928586556 on OpenAlexafffundabout
Philip J. Millar, Trevor J. King, Anthony V. Incognito, Jordan B. Lee, Andrew D. Shepherd, Joseph Cacoilo, Joshua T. Slysz, Jamie F. Burr, Connor J. Doherty

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Guelph-HumberUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroneurographyMedicineHeart rateHemodynamicsBrachial arteryBlood pressureBlood flowCardiologyVascular resistanceInternal medicineAnesthesiaBaroreflex

Abstract

fetched live from OpenAlex

The aim of this study was to comprehensively assess the effects of rhythmic handgrip (RHG), one‐leg cycling, and concurrent arm and leg exercise on muscle sympathetic nerve activity (MSNA) and vascular conductance responses in the inactive leg. Thirty‐five participants completed two study visits in a randomized order. During visit 1, hemodynamic (Finometer) and superficial femoral artery (SFA) blood flow (Doppler ultrasound) measurements were assessed at rest and during 3 min of RHG (1:1 duty cycle; 40% MVC), one‐leg cycling (17 ± 3% peak power output), and concurrent exercise at the same intensities. During visit 2, hemodynamic and MSNA (fibular nerve microneurography) measurements were acquired during the same exercise modes (MSNA; n=12). SFA blood flow and MSNA were both acquired in the inactive leg. Heart rate, blood pressure, cardiac output, and respiration rate increased during each exercise mode (all, P <0.0001), with the largest increases during concurrent exercise (all, P <0.006); no differences were detected between visit 1 and 2 (all, P> 0.05). SFA mean blood flow increased during RHG (Δ24 ± 30 ml/min, P <0.0001) and concurrent exercise (Δ10 ± 38 ml/min, P =0.03) but not cycling (Δ2.3 ± 30 ml/min, P =0.9). SFA vascular conductance was unchanged during RHG (Δ0.02 ± 0.29 ml·min −1 ·mmHg −1 , P =0.9) but reduced similarly during concurrent and cycling exercise (Δ−0.14 ± 0.33 ml·min −1 ·mmHg −1 ; Δ−0.14 ± 0.38 ml·min −1 ·mmHg −1 , P <0.003). RHG increased MSNA burst frequency (Δ4 ± 8 burst/min, P =0.04) without altering burst amplitude (Δ7 ± 14 %, P =0.7) or total MSNA (Δ40 ± 40 %, P =0.07). In contrast, cycling and concurrent exercise had no effects on MSNA burst frequency (Δ−3 ± 6 burst/min and Δ4 ± 6 burst/min, P ≥0.1) but increased burst amplitude (Δ34 ± 30 %; Δ31 ± 31 %, P ≤0.001) and total MSNA (Δ59 ± 63 %; Δ94 ± 100 %, P ≤0.007). In conclusion, during one‐leg cycling or concurrent exercise, reductions in inactive leg SFA vascular conductance occurred in parallel with increases in burst amplitude and total MSNA but not burst frequency. Exercise studies relying solely on measures of MSNA burst occurrence may not accurately reflect the corresponding changes in vascular conductance. Support or Funding Information Research was supported by a Natural Science and Engineering Research Council (NSERC) of Canada Discovery Grant (P.J.M; #06019), the Canada Foundation for Innovation (P.J.M.; #34379), and the Ontario Ministry of Research, Innovation, and Science (P.J.M. #34379). This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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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 score0.003

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 head0.006
GPT teacher head0.230
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 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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