Feasibility of Using Autologous Transplantation to Evaluate Hematopoietic Stem Cell-Based Gene Therapy Strategies in Transgenic Mouse Models of Human Disease
Bibliographic record
Abstract
Histoincompatibility between murine donors and recipients of bone marrow (BM) transplants reduces engraftment, and this compromises assessment of hematopoietic stem cells (HSCs) in certain transgenic mice. To study HSCs in the S+S-Antilles mouse model of human sickle cell disease (SCD), we developed an autotransplant protocol. Initial experiments showed no differences between S+S-Antilles mice and normal C57BL/6 (+/+) mice in their radiosensitivity or baseline hematopoietic progenitor numbers. The kinetics of red blood cell (RBC) replacement post-transplant in +/+ recipients of mixtures of transgenic and +/+ BM cells also showed no competitive advantage of the +/+ cells. BM cells were then aspirated from mice 4 days after 5-fluorouracil treatment, transduced with a green fluorescent protein (GFP)-encoding retrovirus, and transplanted into the same recipients that, just before transplant, were irradiated with 800 cGy. We subsequently detected high levels of GFP(+) RBCs (21-79%) and white blood cells (WBCs; 35-88%) in the blood for 11 months and showed that transduced HSCs regenerated in the primary mice also repopulated secondary mice. These findings provide a generally applicable protocol for performing autotransplants in mice and forecast the potential utility of this approach in assessing HSC-based gene therapy protocols in transgenic mouse models of many human diseases.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".