Small molecule regulation of normal and leukemic stem cells
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hematopoietic stem and progenitor cell (HSPC) transplantation is frequently used in the treatment of hematological diseases. The outcome of the procedure is strongly influenced by the quantity of injected cells, especially if low cell numbers are infused as frequently encountered with cord blood transplants. Ex-vivo expansion of cord blood HSPCs would increase cell numbers, thus accelerating engraftment and reducing infectious complications and transplant-related mortality. In addition, expansion would maximize accessibility to better HLA-matched units, further improving patients' outcome. Similarly, in-vitro maintenance or expansion of leukemic stem cells (LSCs) would enable research into the much awaited targeted therapies that spare normal hematopoietic stem cells (HSCs). Here, we review recent findings on small molecules (excluding biologicals) regulating the activity of normal and leukemic stem cells and provide insights into basic science and clinical implications. RECENT FINDINGS: High-throughput screening of small molecules active on primary hematopoietic cells has led to the identification of two potent series of chemical compounds, best exemplified by StemRegenin1 and UM171, that both expand HSPCs. Current data suggest that the aryl hydrocarbon receptor antagonist StemRegenin1 is most active on primitive normal hematopoietic progenitors and LSCs and that UM171 expands long-term normal HSCs. SUMMARY: Small molecules are clinically useful and powerful tools for expanding HSPCs. They are also of potential value for dissecting the still elusive regulatory networks that govern self-renewal of human HSCs.
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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) | 0.001 | 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) | 0.003 | 0.002 |
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".