Characterization of the effects of exercise training on hematopoietic stem cell quantity and function
Bibliographic record
Abstract
The effect of exercise training on hematopoietic stem cells (HSC) is largely unknown. The aim of the present investigation was to determine whether exercise training could expand the bone marrow HSC pool and influence various aspects of HSC function. Mice were either exercise trained (EX; 1 h/day, 3 days/wk, for 8 wk) or remained sedentary (SED). Bone marrow (BM) from SED or EX mice was extracted from different HSC niches for cell cycle analysis, HSC (lineage(-), Sca-1(+), c-Kit(+)) quantification, and differentiation along various hematopoietic lineages via flow cytometry. Serum was collected for evaluation of cytokines known to regulate HSC. To determine HSC function, BM from EX and SED mice was transplanted into primary and secondary recipients in a BM transplant assay. EX increased HSC quantity in the vascular BM niche 20% vs. SED (P < 0.05) and increased the proportion of whole BM cells in G(2)/M phase of cell cycle (P < 0.05). The number of spleen colonies was 48% greater (P < 0.05) in recipients transplanted with BM from EX. Serum IL-6 levels were decreased 38% in EX, and differentiation along the lineage trended to increase (16%, P = 0.053 and 16%, P = 0.061, respectively). Short- or long-term engraftment and homing in primary recipients were not altered in EX. HSC self-renewal as analyzed by hematopoietic regeneration in secondary recipients was also unaffected by EX. Here we demonstrate that HSC quantity is increased in the BM niche associated with more activated, differentiated HSC, and that this expansion does not improve or impair HSC function.
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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".