Rapamycin promotes tolerance through increased expression of CD4+CD25+Foxp3+ regulatory T cells and impaired development of CD8+CD44+CD62L+ memory T cells. (145.25)
Bibliographic record
Abstract
Abstract Long-term success of transplantation is limited by the need for immunosuppression; thus, tolerance induction is an important therapeutic goal. A 16 day treatment with Rapamycin led to indefinite graft survival (>100 days) of fully mismatched cardiac allografts (BALB/cJ → C3H/HeJ) whereas untreated hearts were rejected at day 9 + 2 days. Specific tolerance was confirmed by the observation that skin grafts from donor mice were accepted, whereas grafts from a 3rd party were rejected. T-lymphocytes from tolerant mice exhibited lower proliferation and cytotoxicity towards donor but not to 3rd party antigens. CD8+CD44+ memory T cells were reduced in tolerant mice but had increased expression of L-selectin (CD62L). CD4+CD25+Foxp3+ regulatory T cells (Tregs) were increased in tolerant mice. We next examined 23 Treg-related genes by multiplex PCR, and found an increase in expression of Foxp3, Tgfb1, Lag3 and Fgl2 and decreased expression of IFNγ and granzyme B in tolerant hearts, but not in rejecting hearts. Plasma levels of FGL2 determined by ELISA remained elevated in tolerant mice. Here we demonstrate that Rapamycin-induced specific tolerance to fully mismatched cardiac allografts is associated with increased numbers of Tregs and impaired development of memory T cells. Expression of Treg effector molecules may serve as putative biomarkers of tolerance in patients undergoing 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".