Induction of Alloimmune Tolerance in Heart Transplantation Through Gene Silencing of TLR Adaptors
Bibliographic record
Abstract
Toll-like receptors (TLRs) activate biochemical pathways that evoke activation of innate immunity, which leads to dendritic cell (DC) maturation and initiation of adaptive immune responses that provoke allograft rejection. We aimed to prolong allograft survival by selectively inhibiting expression of the common adaptors of TLR signaling, namely MyD88 and TRIF, using siRNA. In vitro we demonstrated that blocking expression of MyD88 and TRIF led to reduced DC maturation. In vivo treatment of recipients with MyD88 and TRIF siRNA significantly prolonged allograft survival in the BALB/c > C57BL6 cardiac transplant model. Moreover, the combination of MyD88 and TRIF siRNA along with a low dose of rapamycin further extended the allograft survival (88.8 ± 7.1 days). Tissue histopathology demonstrated an overall reduction in lymphocyte interstitium infiltration, vascular obstruction and hemorrhage in mice treated with MyD88 and TRIF siRNA vector plus rapamycin. Furthermore, treatment was associated with an increase in the numbers of CD4(+) CD25(+) FoxP3(+) regulatory T cells and Th2 deviation. To our knowledge, this study is the first demonstration of prolonging the survival of allogeneic heart grafts through gene silencing of TLR signaling adaptors, highlighting the therapeutic potential of siRNA in clinical 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".