How Does Lifestyle Affect Hematopoiesis and the Bone Marrow Microenvironment?
Bibliographic record
Abstract
Lifestyle factors are modifiable behavioral factors that have a significant impact on health and longevity. Diet-induced obesity and physical activity/exercise are two prevalent lifestyle factors that have strong relationships to overall health. The mechanisms linking obesity to negative health outcomes and the mechanisms linking increased participation in physical activity/exercise to positive health outcomes are beginning to be elucidated. Chronic inflammation, due in part to overproduction of myeloid cells from hematopoietic stem cells (HSCs) in the bone marrow, is an established mechanism responsible for the negative health effects of obesity. Recent work has shown that exercise training can reverse the aberrant myelopoiesis present in obesity in part by restoring the bone marrow microenvironment. Specifically, exercise training reduces marrow adipose tissue, increases HSC retention factor expression, and reduces pro-inflammatory cytokine levels in the bone marrow. Other, novel mechanistic factors responsible for these exercise-induced effects, including intercellular communication using extracellular vesicles (EVs), is beginning to be explored. This review will summarize the recent literature describing the effects of exercise on hematopoiesis in individuals with obesity and introduce the potential contribution of EVs to this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".