Skeletal muscle sensory afferents affect cardiac regulation in humans
Bibliographic record
Abstract
Elevated cardiac output (Q) during isometric contractions may be due to neural signals arising from the motor cortex or from the contracting muscle. This study tested the hypothesis that skeletal muscle sensory inputs affect cardiac function and that this effect depends on baroreceptor loading. Three‐minute segments of heart rate (HR; ECG), mean arterial pressure (MAP), and Q (Finometer) were obtained during supine rest, ‐35 mmHg lower body negative pressure (LBNP), and LBNP + electrical stimulation (ES) of the forearm (n=7 males). ES was applied at sub‐motor (group I and II afferents) and non‐fatiguing supra‐motor threshold (group III and IV fibers) levels. Parasympathetic indicators were assessed by heart rate variability (HRV) analysis. ES had no effect on HR, MAP, Q or HRV during supine rest. LBNP increased HR and decreased pulse pressure (P<0.05). Q during LBNP was reduced further with sub‐motor ES versus LBNP (‐0.142 L/min; P<0.05). Compared with LBNP alone, Q was increased during LBNP + supra‐motor ES (+0.132 L/min; P<0.05). ES during LBNP did not change HR, MAP, or HRV indicators. These results suggest that Type I and II afferents depress, and Type III and IV afferents augment Q during LBNP. As indices of HRV and parasympathetic outflow were not affected, these data suggest that muscle afferent stimulation affects Q through a sympathetic neural mechanism. Supported by the Heart and Stroke Foundation of Ontario.
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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) | 0.002 | 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".