Genetic Modification of Murine Hematopoietic Stem Cells by Retroviruses
Bibliographic record
Abstract
Among the currently available methods for gene transfer, recombinant murine retroviruses remain the best established method for achieving stable integration of a transgene with high efficiency. Pioneering work by a number of groups has demonstrated the feasibility of using this method for gene transfer to primitive, multipotential long-term repopulating hematopoietic stem cells (HSC) (1-4). In the case of the hematopoietic system, it is required that the introduced gene integrates into the genome of HSC in order to be expressed in multiple lineages over an extended period of time. However, HSC are found at low frequency, and are normally in a quiescent or slow cycling state. Both factors represent challenges to successful retroviral gene transfer. The former places a premium on high titer, and the latter dictates methods to trigger HSC cycling during the infection, since stable integration of murine retroviruses requires cell division of the target cell and breakdown of the nuclear membrane (5,6). In general, titers greater than 1 × 10(5) U/mL allow some degree of gene transfer for HSC, but 1 × 10(6) or higher are a reasonable goal for achieving useful efficiencies of at least 20%. For activation of HSC, most protocols invoke a combination of in vivo and in vitro stimulation. The former is most easily and routinely achieved by administration of cytotoxic agents like 5-fluorouracil (5-FU) 4 d prior to bone-marrow harvest. This procedure removes a large proportion of actively cycling, more differentiated cells, thus achieving a.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".