Homing and Engraftment of Hematopoietic Stem Cells Following Transplantation:
A Pre-clinical Perspective
Bibliographic record
Abstract
Hematopoietic stem cell (HSC) transplantation (HSCT) is used to treat various hematologic disorders. Use of genetically modified mouse models of hematopoietic cell transplantation have been critical in our fundamental understanding of HSC biology and developing approaches for human patients. Pre-clinical studies in animal models provide insight into the journey of transplanted HSCs from infusion to engraftment in bone marrow (BM) niches. Various signaling molecules and growth factors secreted by HSCs and the niche microenvironment play critical roles in homing and engraftment of the transplanted cells. The sustained equilibrium of these chemical and biologic factors ensures that engrafted HSCs generate healthy and durable hematopoiesis. Transplanted healthy HSCs compete with residual host cells to repopulate stem cell niches in the marrow. Stem cell niches, in particular, can be altered by the effects of previous treatments, aging, and paracrine effects of leukemic cells that create inhospitable bone marrow niches that are unfavorable for healthy hematopoiesis. More work to understand how stem cell niches can be restored to favour normal hematopoieis may be key to reducing leukemic relapses following transplant.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".