Novel interferon-based pre-transplantation conditioning in the treatment of a congenital metabolic disorder
Bibliographic record
Abstract
Hematopoietic stem cell (HSC) gene therapy is a potentially curative treatment modality for monogenic hematological diseases and storage disorders. It is necessary, however, to establish pre-bone marrow (BM) transplant conditioning regimens that minimize DNA damage and toxicity. Type I interferon (IFN) signaling activates quiescent HSCs and enables them to be sensitive to 5-fluorouracil (FU)-mediated cytotoxicity, thus implying a molecular basis for improving HSC transplant outcomes. Here we show that type I IFN preconditioning, without irradiation or DNA alkylating agents, significantly enhanced the HSC engraftment efficiency in wild-type (WT) recipient mice. The importance of active type I IFN signaling in HSC recipients was further demonstrated using mice lacking IFN regulatory factor 2 (IRF2), a transcriptional suppressor of type I IFN signaling. In both WT and Irf2(-/-) recipients, active type I IFN signaling greatly enhanced the sensitivity to 5-FU or low-dose irradiation of HSCs. Importantly, IFN-based pre-BM transplant conditioning was also applicable to the treatment of Sly syndrome, a congenital storage disorder with β-glucuronidase deficiency, in which it restored enzyme expression at the HSC level and reciprocally reduced pathological glycosaminoglycan storage. Our findings suggest type I IFN-based preconditioning, combined with HSC transplantation, as a novel nongenotoxic treatment of some congenital 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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".