Multiple Reduced-intensity Conditioning Regimens Facilitate Correction of Fabry Mice After Transplantation of Transduced Cells
Bibliographic record
Abstract
Hematopoietic cell transplantation can impact lysosomal storage disorders (LSDs) and will be enhanced by gene therapy. Transduced cells in LSDs often secrete the therapeutic hydrolase, which can be used by bystander cells. However, toxicity associated with myeloablative transplant preparative regimens limits many applications of this approach in gene therapy. We hypothesized that reduced-intensity (RI) conditioning regimens would allow stable engraftment of therapeutically transduced cells and allow correction of Fabry disease. We transplanted transduced cells into Fabry mice receiving eight different clinically relevant chemotherapy- and/or radiotherapy-based RI conditioning regimens generating modest and transient lymphoid/myeloid cell depletion. Two comprehensive transplantation Protocols were performed. Firstly, transplantation of 0.38 x 10(6) gene-modified stem/progenitor cells was nominally effective; none of the RI regimens led to stable alpha-galactosidase A (alpha-gal A) correction. Secondly, transduced cells were preselected for functional transgene expression and transplanted at a higher dose (0.72 x 10(6) cells). Each RI regimen yielded engraftment of functional transgene-positive cells through 180 days along with increased plasma alpha-gal A activity. Importantly, the RI regimens mediated broad organ enzyme correction and were not associated with immune responses against alpha-gal A. RI conditioning thus has an important role in gene therapy for LSDs; a variety of regimens can be effective in this context.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".