Transplantation of human hematopoietic repopulating cells: mechanisms of regeneration and differentiation using human???mouse xenografts
Bibliographic record
Abstract
PURPOSE OF REVIEW: Some of the common clinical applications of hematopoietic stem cell (HSC) transplantation are used for treatment of leukemic malignancies, bone marrow/hematopoietic failure, and immunodeficiencies. Several inherent limitations of this procedure have restricted its use, including failed recipient bone marrow recovery and tumor relapse. Experimentally, ex-vivo manipulations and in-vivo xenotransplant assays used as clinical surrogates of HSC transplantation aim to improve quality and number of donor cells by uncovering the cellular and molecular mechanisms controlling HSC proliferation and differentiation upon transplantation. RECENT FINDINGS: One of the major research goals in hematopoietic stem cell transplant biology is the manipulation of factors affecting self-renewal, mobilization, and homing/retention of donor cells in the recipient. Transgenic systems in the mouse are frequently used as tools to dissect the individual pathways that influence these properties. Recently, identification of factors and chemicals that target HSCs or their 'niche', a supportive microenvironment for these stem cells in the bone marrow, has become a new avenue of interest to develop a novel therapeutic approach for enhancing HSC repopulation ability and engraftment efficiency. SUMMARY: This review focuses on recent progress in HSC ex-vivo manipulation and transplant biology, and the potential improvements arising from identification of molecular players and cellular components of the microenvironment niche.
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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 | 0.003 |
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".