Mobilization and homogeneity of healthy hematopoietic stem cells during hematological malignancy 3191
Bibliographic record
Abstract
Abstract Description Hematopoietic stem cells (HSCs) are multipotent and capable of self-renewal, producing all immune cells. HSCs are heterogeneous, showing differences in blood lineage production and responses to stress, such as transient proliferation and migration. Inflammation can disrupt HSC function, but the mechanisms remain unclear; so understanding how inflammation affects HSCs could reveal important aspects of their diversity and behavior. We examined how acute sterile inflammation impacts the HSC biology using a pre-clinical mouse model of acute myeloid leukemia (AML) paired with flow cytometric analysis. Our results showed a rapid decline in marrow-resident HSCs after disease onset, with a corresponding expansion of phenotypically distinct splenic HSCs. These egressed HSCs shared surface markers with those lost from the marrow, suggesting a subset of HSCs that are particularly sensitive to inflammation and mobilization. As disease progressed, remaining marrow HSCs became more quiescent. Interestingly, transcriptomic analysis revealed that these residual HSCs were primed for balanced lineage output and showed increased TGFβ signaling, suggesting that a quiescent, non-biased HSC population has a selective advantage under inflammatory stress. These findings may provide insights into how inflammation compromises HSC function, offering potential explanations for impaired hematopoietic recovery following inflammatory insults and guiding future therapeutic strategies. Funding Sources 1T32GM146611-01A1, Mark Foundation Endeavor Topic Categories Hematopoiesis and Immune System Development (HEM)
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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 | 0.001 |
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".