A PROTEASOME INHIBITOR EFFECTIVELY PREVENTS MOUSE HEART ALLOGRAFT REJECTION1
Bibliographic record
Abstract
BACKGROUND: We have previously demonstrated in vitro that proteasome inhibitors could suppress proliferation and induce apoptosis of activated T cells. This finding suggests that such inhibitors could be used as a novel category of immunosuppressants in blocking allograft rejection. METHODS: The proteasome inhibitor dipeptide boronic acid (DPBA) was tested in vitro for its inhibitory effect on mouse T-cell proliferation and lymphokine secretion. DPBA was also used in vivo to treat mouse heterotopic heart allograft rejection. Possible side effects of this compound were examined according to blood chemistry of mice treated with DPBA. RESULTS: DPBA suppressed the T-cell proliferation and potently inhibited interleukin (IL)-2, IL-6, IL-10, IL-13, and IFN-gamma produced by anti-CD3-activated T cells. Given i.p. starting 1 day after transplantation at 0.66 mg/kg per day for 16 days, or at 1 mg/kg per day for 4 days followed by 0.5 mg/kg per day for 12 days, DPBA could prolong heart allograft survival to 35.5 days (mean survival time, MST) and to 36.2 days, respectively. The control group had MST of 7.3 days. When administrated 72 hr post operation at 1 mg/kg per day for 4 days, DPBA could prolong the graft survival to 19.8 days. During the course of these effective dosages, DPBA had no apparent toxicity in the liver, kidney, pancreas, or heart, according to analysis of blood chemistry. CONCLUSIONS: The proteasome inhibitor could repress allograft rejection in mice without apparent side-effects at the effective dosages. This finding has opened a new dimension in development of novel immunosuppressants for organ 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".