Heterogeneity in hematopoietic stem cell populations
Bibliographic record
Abstract
PURPOSE OF REVIEW: Transplantation of hematopoietic cells is now a well established clinical procedure, although optimal outcomes are not always obtained. This reflects insufficient knowledge of the different subsets of primitive cells required to achieve a rapid and permanent recovery of mature blood cell production. Here we review recent findings that extend our understanding of these cells and their regulation, and implications for the ex-vivo expansion of these cells. RECENT FINDINGS: Separate subsets of platelet and neutrophil lineage-restricted human hematopoietic cells with rapid but transient repopulating activities have been identified, thus adding to previous evidence of short-term repopulating cells that generate both of these lineages. New studies also suggest intrinsically determined heterogeneity in differentiation potentialities that are sustained at the stem cell level, and have revealed new ways their self-renewal can be influenced. SUMMARY: Hematopoietic repopulation posttransplant is highly complex both in terms of the differing numbers and types of cells required for optimal hematopoietic recoveries and the factors that will determine the composition and behavior of a given inoculum. Successful ex-vivo expansion protocols will, thus, need to incorporate conditions that will produce adequate numbers of all cell types required with retention of their full functionality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".