Maximal and Submaximal Handgrip Exercise Stimulates Thrombocytosis
Bibliographic record
Abstract
Introduction Blood platelets sequester, store, and release a majority of blood‐borne brain‐derived neurotrophic factor (BDNF), which is a major orchestrator of exercise‐induced brain plasticity. Exercise involving large muscle mass transiently increases platelet and serum BDNF levels in an intensity‐dependent manner. During exercise, augmented sympathetic outflow causes splenic contractions, which increases platelet levels in the blood (exercise‐induced thrombocytosis). Interestingly, very brief (60 s) submaximal activation of small muscle mass induces splenic constriction and a 2% elevation in platelet levels. If small muscle exercise is sufficient enough of a stimulus to increase platelet levels, then by extension, it could stand as a viable strategy for increasing BDNF availability; however, the platelet response to dynamic handgrip exercise (HGEX) is currently unknown. Purpose To examine the response of platelets following both short‐duration maximal and prolonged submaximal intensity dynamic HGEX. Methods Healthy males (n = 6; 21.7 ±2.5 yrs old) have been recruited. Exercise protocols were performed on separate days. The high‐intensity exercise was a critical power test (CP), consisting of maximal squeezing for 10 minutes. Submaximal exercise (SE) consisted of HGEX performed at 15% below critical power for 30 minutes. Both protocols used a 2:2 contraction:relaxation duty cycle. Platelets were derived from a complete blood count analysis performed by a haematology lab. Results Platelets increased by 8% from rest to CP (n = 6; 217.9 vs. 236.4 x10 9 /L; p < .05) and 6% from rest to SE (n = 3; 193.3 vs. 206 x10 9 /L, p = .06). Conclusion Preliminary data demonstrate that platelets increase in a similar fashion to HGEX, regardless of exercise intensity.
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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) | 0.003 | 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".