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Evidence for sympatholysis at the onset of forearm exercise

2002· article· en· W2015871572 on OpenAlexaff
Darren S. DeLorey, Simon S. Wang, J. Kevin Shoemaker

Bibliographic record

VenueJournal of Applied Physiology · 2002
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsWestern University
Fundersnot available
KeywordsSupine positionForearmMedicineCardiologyBlood flowInternal medicineBlood pressureHemodynamicsContraction (grammar)Reactive hyperemiaExercise physiologyAnesthesiaAnatomy

Abstract

fetched live from OpenAlex

The effect of augmented sympathetic outflow on forearm vascular conductance after single handgrip contractions of graded intensity was examined to determine whether sympatholysis occurs early in exercise (n = 7). While supine, subjects performed contractions that were 1 s in duration and 15, 30, and 60% of maximal voluntary contraction (MVC) in intensity. The contractions were repeated during control and lower body negative pressure (LBNP) (-40 mmHg) sessions. Forearm blood flow (FBF; Doppler ultrasound) and mean arterial pressure were measured continuously for 30 s before and 60 s after the single contractions. Vascular conductance (VC) was calculated. Total postcontraction blood flow increased in an exercise intensity-dependent manner. Compared with control, LBNP caused a reduction in baseline and postexercise FBF (P < 0.05), VC (P < 0.01), as well as total excess flow (P < 0.01). Specifically, during LBNP, baseline FBF and VC were reduced by 29 and 34% of control, respectively (P < 0.05). After the 15% MVC contraction, peak VC during LBNP was reduced by a magnitude similar to that during baseline (i.e., ~30%), but it was only reduced by 15% during both the 30 and 60% MVC trials (P < 0.01). It was concluded that the stimuli for exercise hyperemia during moderate and heavy, but not mild, handgrip exercise intensities, diminish the vasoconstrictor effects of LBNP. Furthermore, these data demonstrate that this sympatholysis occurs early in exercise.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.443

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 head0.057
GPT teacher head0.292
Teacher spread0.236 · 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

Citations31
Published2002
Admission routes1
Has abstractyes

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