The long‐term survival of baboon‐to‐monkey kidney and liver xenografts*
Bibliographic record
Abstract
The present study was undertaken to develop an optimum immunosuppressive regimen in baboon-to-monkey life-supporting kidney xenografts. Baseline therapy for all groups include cyclosporine (CsA) and steroids. We compared adding (1) cyclophosphamide (CyP) at dose of 20 mg/kg/day given on post-operative day (POD) 0, 2, 5 and 7; (2) mycophenolic mofetil (MMF) at a dose of 40 mg/kg/day by daily gavage; or (3) CyP + rapamycin (Rap). The latter group was divided into high and low dose subgroups. Untreated xenografts were rejected on POD 6, CsA alone treated xenografts survived for 35 days and CsA + CyP treated xenografts survived for 45 days. Adding MMF significantly prolonged mean survival to 111 +/- 53 days, but the xenografts eventually developed rejection. Combination therapy including CsA, CyP and Rap reliably prevented xenogenic rejection and achieved a mean survival of 290 +/- 30 days. However, high dose CyP + Rap led to high incidence of post-transplant lymphoproliferation disorders (PTLD), while the incidence of PTLD was significantly less in the low dose subgroup (P < 0.01). Four animals in this subgroup survived for more than 300 days with normal renal function and histology. In addition, two liver recipients treated with CsA + CyP survived for 91 and 1,076 days. We conclude that long-term survival of kidney or liver xenografts can be achieved in a non-human concordant xenograft model using currently available immunosuppressive agents.
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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 | 0.000 |
| 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".