Non‐alpha‐adrenergic effects on systemic vascular conductance during lower‐body negative pressure, static exercise and muscle metaboreflex activation
Bibliographic record
Abstract
AIM: This study tested the hypothesis that non-α-adrenergic mechanisms contribute to systemic vascular conductance (SVC) in a reflex-specific manner during the sympathoexcitatory manoeuvres. METHODS: Twelve healthy subjects underwent lower-body negative pressure (LBNP, -40 mmHg) as well as static handgrip exercise (HG, 20% of maximal force) followed by post-exercise forearm circulatory occlusion (PECO, 5 min each) with and without α-adrenergic blockade induced by phentolamine (PHE). Aortic blood flow, finger blood pressure and superficial femoral artery blood flow were measured to calculate cardiac output, SVC and leg vascular conductance (LVC) during the last minute of each intervention. RESULTS: Mean arterial pressure (MAP) decreased more during LBNP with PHE compared with saline (-7 ± 7 vs. -2 ± 5%, P = 0.016). PHE did not alter the MAP response to HG (+20 ± 12 and +24 ± 16%, respectively, for PHE and saline) but decreased the change in MAP during PECO (+12 ± 7 vs. +21 ± 14%, P = 0.005). The decrease in SVC and LVC with LBNP did not differ between saline and PHE trials (-13 ± 10 vs. -17 ± 10%, respectively, for SVC, P = 0.379). In contrast, the SVC response to HG increased from -9 ± 12 with saline to + 5 ± 15% with PHE (P = 0.002) and from -16 ± 15 with saline to +1 ± 16% with PHE during PECO (P = 0.003). LVC responses to HG or PECO were not different from saline with PHE. CONCLUSIONS: Non-α-adrenergic vasoconstriction was present during LBNP. The systemic vasoconstriction during static exercise and isolated muscle metaboreflex activation, in the absence of leg vasoconstriction, was explained by an α-adrenergic mechanism. Therefore, non-α-adrenergic vasoconstriction is more emphasized during baroreflex, but not metaboreflex-mediated sympathetic activation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".