Whole‐body heat stress and exercise stimulate the appearance of platelet microvesicles in plasma with limited influence of vascular shear stress
Bibliographic record
Abstract
Abstract Intense, large muscle mass exercise increases circulating microvesicles, but our understanding of microvesicle dynamics and mechanisms inducing their release remains limited. However, increased vascular shear stress is generally thought to be involved. Here, we manipulated exercise‐independent and exercise‐dependent shear stress using systemic heat stress with localized single‐leg cooling (low shear) followed by single‐leg knee extensor exercise with the cooled or heated leg (Study 1, n = 8) and whole‐body passive heat stress followed by cycling (Study 2, n = 8). We quantified femoral artery shear rates ( SR s) and arterial and venous platelet microvesicles ( PMV – CD 41 + ) and endothelial microvesicles ( EMV – CD 62E + ). In Study 1, mild passive heat stress while one leg remained cooled did not affect [microvesicle] ( P ≥ 0.05). Single‐leg knee extensor exercise increased active leg SR s by ~12‐fold and increased arterial and venous [ PMV s] by two‐ to threefold, even in the nonexercising contralateral leg ( P < 0.05). In Study 2, moderate whole‐body passive heat stress increased arterial [ PMV ] compared with baseline (mean± SE , from 19.9 ± 1.5 to 35.5 ± 5.4 PMV . μ L −1. 10 3 , P < 0.05), and cycling with heat stress increased [ PMV ] further in the venous circulation (from 27.5 ± 2.2 at baseline to 57.5 ± 7.2 PMV . μ L −1. 10 3 during cycling with heat stress, P < 0.05), with a tendency for increased appearance of PMV across exercising limbs. Taken together, these findings demonstrate that whole‐body heat stress may increase arterial [ PMV ], and intense exercise engaging either large or small muscle mass promote PMV formation locally and systemically, with no influence upon [ EMV ]. Local shear stress, however, does not appear to be the major stimulus modulating PMV formation in healthy humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".