Ex Vivo Expansion of Human and Murine Hematopoietic Stem Cells
Bibliographic record
Abstract
The last decade has seen major advances in our knowledge of the molecular control of hematopoiesis, widespread access to cytokines, and the development of practical assays for quantitating highly primitive hematopoietic cells. This progress has now made feasible the predictable manipulation of hematopoietic stem cells (HSC) and progenitors for a variety of experimental and clinical applications. Nevertheless, our understanding of events that induce and/ or block the differentiation of primitive hematopoietic cells is still very limited. Therefore, it is not surprising that procedures for expanding HSC populations ex vivo are based largely on a small set of empirical observations. The incentive to improve this situation is provided by many clinical situations in which the number of stem cells available for particular types of transplants is inadequate, or where HSC amplification may be useful as part of a purging strategy to reduce the potential burden of malignant cells in an autograft. Cell division with retention of stem cell integrity could also facilitate the generation in vitro of many specific types of differentiated cells (e.g., dendritic cells) and is a requirement for retroviral-mediated gene therapy. Progress in each of these rapidly evolving areas has recently been reviewed in greater depth elsewhere (1-5).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".