Dynamic response characteristics of local muscle blood flow regulatory mechanisms in human forearm exercise
Bibliographic record
Abstract
We sought to understand the nature of control mechanisms involved in the adaptation of exercising muscle hyperemia. Seven subjects performed rhythmic dynamic forearm exercise under two exercise conditions: small step 1 [step increase from rest to 40% peak forearm vascular conductance (FVC), in ml.min(-1).100 mmHg-1] for 5 min followed by small step 2 (further increase to 80% peak FVC for 5 min), and large step (step increase from rest to 80% peak FVC for 5 min). FVC data were fit with a two- (small step 1) and three-component (small step 2, large step) exponential as appropriate. For the rapid phase I response, FVC dynamic response characteristics (time delay, time constant) were not affected by the magnitude of the work intensity increase when the transition began from rest, but were slower in the 40-80% transition. Rest-80% gain was greater than either rest-40% or 40-80% transitions but represented the same proportion of the phase I + phase II gain across all transitions (57 vs. 56 vs. 57%, respectively, P = 0.975). For the slower phase II response, dynamic response characteristics were not affected by the magnitude of the work intensity increase when initiated from rest. The time constant was not altered when the transition began from exercise vs. rest. We conclude that 1) dynamic response characteristics of exercise hyperemia control mechanisms are not affected by the magnitude of work rate increase when forearm exercise is initiated from rest, 2) phase I but not phase II dynamic response characteristics are sensitive to baseline exercise intensity, and 3) the mechanisms contributing to phase I result in the same relative response magnitude, regardless of the size of the step increase in exercise intensity or the baseline from which it is initiated.
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 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 | 0.001 |
| 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".