Tolerance induction in rats, using a combination of anti-CD154 and donor splenocytes, given once on the day of transplantation1
Bibliographic record
Abstract
BACKGROUND: Donor-specific tolerance induction remains an attractive objective that generates much research in the field of transplantation. Unfortunately, most of the protocols available involve pregraft conditioning, making these treatments incompatible with clinical applications. METHODS: LEW.1A rats were grafted with histoincompatible LEW.1W hearts. On the day of transplantation, recipients were treated with anti-CD40L combined with donor splenocytes. The hearts were evaluated for graft survival; cellular infiltrate and intragraft cytokines were determined using real-time reverse transcriptase-polymerase chain reaction. Tolerance induction was assessed by skin grafting and adoptive transfers. RESULTS: The combination of a single injection of anti-CD40L and donor splenocytes, given on the day of surgery, allowed 40% of cardiac allografts to survive long-term (mean survival time=66.3 day). The cellular composition or the extent of graft infiltrate was not modified but was associated with a massive decrease of proinflammatory cytokines expression within the graft. Long-term survivors accepted donor-matched skin grafts, and leukocytes harvested from these animals transferred tolerance into irradiated freshly grafted recipients. CONCLUSION: A combination of costimulation blockade and donor cells, given once at the time of transplantation, is sufficient to induce allograft tolerance in rats.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".