Progenitor cell mobilization after exercise is related to systemic levels of <scp>G</scp>‐<scp>CSF</scp> and muscle damage
Bibliographic record
Abstract
Different types of exercise are characterized by the ability to induce specific physiological stimuli that might be able to induce the mobilization of progenitor cells. The aim of the current study was to investigate the mobilization of hematopoietic progenitor cells (HPCs) and endothelial progenitor cells (EPCs) in response to endurance, resistance, and eccentric endurance exercise and their relation to markers of muscle damage and inflammation. Healthy male subjects performed acute bouts of either endurance exercise, resistance exercise, or eccentric endurance exercise. Numbers of progenitor cells and several markers of muscle damage and inflammation were determined. Although the endurance exercise was followed by an immediate and short increase of both HPCs and EPCs, the eccentric exercise evoked a long lasting increase up to 24 h for HPCs and 48 h for EPCs (P < 0.05). After resistance exercise, an increase of HPCs was only found 3 h after exercise (P < 0.05). A correlation was found between mobilized progenitor cells and systemic levels of granulocyte colony-stimulating factor (G-CSF) levels (r = 0.54 and r = 0.51, P < 0.05) as well as for HPCs and creatine kinase levels (r = 0.57, P < 0.05). These results suggest that mobilization of progenitor cells is related to the type of exercise and possibly mediated by G-CSF and muscle damage.
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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) | 0.001 | 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".