Lentivirus IL-10 Gene Therapy Down-Regulates IL-17 and Attenuates Mouse Orthotopic Lung Allograft Rejection
Bibliographic record
Abstract
The purpose of the study was to examine the effect of lentivirus-mediated IL-10 gene therapy to target lung allograft rejection in a mouse orthotopic left lung transplantation model. IL-10 may regulate posttransplant immunity mediated by IL-17. Lentivirus-mediated trans-airway luciferase gene transfer to the donor lung resulted in persistent luciferase activity up to 6 months posttransplant in the isograft (B6 to B6); luciferase activity decreased in minor-mismatched allograft lungs (B10 to B6) in association with moderate rejection. Fully MHC-mismatched allograft transplantation (BALB/c to B6) resulted in severe rejection and complete loss of luciferase activity. In minor-mismatched allografts, IL-10-encoding lentivirus gene therapy reduced the acute rejection score compared with the lentivirus-luciferase control at posttransplant day 28 (3.0 ± 0.6 vs. 2.0 ± 0.6 (mean ± SD); p = 0.025; n = 6/group). IL-10 gene therapy also significantly reduced gene expression of IL-17, IL-23, and retinoic acid-related orphan receptor (ROR)-γt without affecting levels of IL-12 and interferon-γ (IFN-γ). Cells expressing IL-17 were dramatically reduced in the allograft lung. In conclusion, lentivirus-mediated IL-10 gene therapy significantly reduced expression of IL-17 and other associated genes in the transplanted allograft lung and attenuated posttransplant immune responses after orthotopic lung transplantation.
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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) | 0.001 | 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) | 0.001 | 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".