Aerobic exercise in humans mobilizes HSCs in an intensity-dependent manner
Bibliographic record
Abstract
Hematopoietic stem and progenitor cells are necessary to maintain, repair, and reconstitute the hematopoietic blood cell system. Mobilization of these cells from bone marrow to blood can be greatly increased under certain conditions, one such being exercise. The purpose of this study was to identify the importance of exercise intensity in hematopoietic mobilization, to better understand the mobilization kinetics postexercise, and to determine if exercise is capable of mobilizing several specific populations of hematopoietic cells that have clinical relevance in a transplant setting. Healthy individuals were exercised on a cycle ergometer at 70% of their peak work rate (WR peak ) until volitional fatigue and at 30% of their WR peak work matched to the 70% WR peak bout. Blood was collected before, immediately post, and 10, 30, and 60 min postexercise. Total blood cells, hematocrit, and mononuclear cells isolated by density gradient centrifugation were counted. Specific populations of hematopoietic stem cells were analyzed by flow cytometry. Mononuclear cells, CD34 + , CD34 + /CD38 − , CD34 + /CD110 + , CD3 − /CD16 + /CD56 + , CD11c + /CD123 − , and CD11c − /CD123 + cells per millilter of blood increased postexercise. Overall, the 70% WR peak exercise group showed greater mobilization immediately postexercise, while there was no observable increase in mobilization in the work matched 30% WR peak exercise group. Mobilization of specific populations of hematopoietic cells mirrored changes in the general mobilization of mononuclear cells, suggesting that exercise serves as a nonspecific mobilization stimulus. Evidently, higher intensity exercise is capable of mobilizing hematopoietic cells to a large extent and immediately postexercise is an ideal time point for their collection. NEW & NOTEWORTHY Here we demonstrate for the first time that mobilization of hematopoietic stem cells (HSCs) through exercise is intensity dependent, with the greatest mobilization occurring immediately after high-intensity exercise. As well, we show that exercise is a general stimulus for mobilization: increases in specific HSC populations are reliant on general mononuclear cell mobilization. Finally, we demonstrate no differences in mobilization between groups with different aerobic fitness.
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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".