LF 15–0195, a novel immunosuppressive agent prevents rejection and induces operational tolerance in a mouse cardiac allograft model
Bibliographic record
Abstract
BACKGROUND: LF 15-0195 (LF) is a new analogue of 15-deoxyspergualin (DSG) that is less toxic and more potent than DSG. The present study was undertaken to determine (1). the dose response of LF monotherapy, (2). its ability to induce tolerance, and (3) its interaction with cyclosporine (CsA), FK 506 (FK), and rapamycin (RAPA). METHODS: Varying doses of LF were administered to determine dose-dependent effects on graft survival in a C57BL/6 to BALB/c heterotopic heart allograft mouse model. Transplanting-donor and third-party skin grafts into long-term survivors were used to assess the tolerance status. CsA, FK, and RAPA were combined with LF to determine their interactive effects on graft survival. RESULTS: The efficacy and toxicity of LF was dose dependent. High-dose LF monotherapy (>2 mg/kg) induced donor-specific operational tolerance, but it was associated with high mortality. Simultaneous administration of high-dose calcineurin inhibitors (CsA FK) prevented tolerance induced by LF. In contrast, a short course of LF combined with a subtherapeutic dose of CsA FK achieved indefinite survival of C57/BL6 cardiac allografts. RAPA and LF had a synergistic effect in induction of tolerance. CONCLUSIONS: The efficacy and toxicity of LF were dose dependent. A short course of LF significantly reduced the requirement of CsA or FK to prevent rejection. RAPA and LF had synergy in induction of tolerance. These data indicate that LF may be a promising agent that warrants further studies in nonhuman primate models of 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) | 0.001 | 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".