Tolerance induction in rats, using a combination of anti-CD154 and donor splenocytes, given once on the day of transplantation1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 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 it