Overexpressed Exogenous IL-4 And IL-10 Paradoxically Regulate Allogenic T-Cell and Cardiac Myocytes Apoptosis Through FAS/FASL Pathway
Bibliographic record
Abstract
BACKGROUND: The authors' previous study has shown that liposome-mediated ex vivo intracoronary interleukin (IL)-4 and IL-10 combined gene therapy suppressed the allo-immune responses and prolonged the cardiac allograft survival by 15 folds. However, the mechanism for promoting long-term allograft survival remains unknown. METHODS: This study tested the hypothesis that this combined cytokine gene targeting may promote alloreactive T-cell apoptosis or prevent apoptosis of cardiac allograft myocytes through Fas/Fas ligand (FasL) pathway. A rabbit functional cervical heterotopic heart transplantation model was used, and plasmid human recombinant IL-4 and IL-10 gene complexed with cationic liposome (GAP/DLRIE) was delivered into cardiac allografts by intracoronary infusion ex vivo. RESULTS: This liposome-mediated IL-4 and IL-10 combined gene therapy significantly increased apoptotic T cells detected by TUNEL staining. The caspase-8 or caspase-3 expressing T cells were also significantly increased. The Fas+ apoptotic T cells dominated in the population of apoptotic CD4+ T cells, but FasL+ CD4+ T-cell population was less effected in the combined gene therapy group. The effect of combined gene therapy on the infiltrative Fas+ CD8+ T-cell population is much less than that on Fas+ CD4+ cells, and there was almost no effect on the FasL+ CD8+ T-cell population. Furthermore, localized IL-4 and IL-10 combined gene therapy protected cardiac allograft myocytes by down-regulating its FasL expression, but not Fas. CONCLUSIONS: These results suggest that this combined gene targeting strategy which induced localized overexpression of exogenous IL-4 and IL-10 may promote alloreactive T-cell apoptosis and prevent myocytes apoptosis through Fas/FasL cell surface interaction, therefore inducing cardiac allograft tolerance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".