Novel application of lentiviral vectors towards treatment of graft-versus-host disease
Bibliographic record
Abstract
Allogeneic transplantation of hematopoietic stem cells and lymphocytes is a curative treatment for malignant and non-malignant disease. However, the primary complication limiting the safety of transplantation is graft-versus-host disease (GvHD), which is mediated by donor T cells. Strategies for pre- and post-transplant manipulation of graft cells are not yet optimal for balancing GvHD severity with beneficial Graft-versus-Leukemia (GvL) effects. Emerging cell fate control gene therapy (CFCGT)-based strategies, such as 'suicide' gene therapy for donor T cell regulation, can supplement existing transplantation approaches by providing a safety element to reduce GvHD. Past uses of CFCGT in the clinic have provided proof-of-principle that GvHD can be controlled by such a strategy. However, there exists a need for improved transgene delivery and suicide control systems. Recently, lentiviral vectors (LVs) have emerged as effective gene delivery vehicles for the clinic. Combining lentiviral gene delivery with newer generations of 'suicide' systems that possess improved enzyme/prodrug specificities, activities, and reduced immunogenicity, could provide the necessary degree of control required to more successfully manage GvHD. Improving the safety of transplantation through successful CFCGT will serve to expand the potential donor pool and the spectrum of disorders that can be treated by this therapeutic schema.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".