Improvement in human decay accelerating factor transgenic porcine kidney xenograft rejection with intravenous administration of gas914, a polymeric form of ??gal1
Bibliographic record
Abstract
BACKGROUND: The present study was undertaken to determine whether intravenous administration of GAS914, a polymeric form of alphaGal, would minimize porcine kidney xenograft rejection in baboons. Human decay accelerating factor renal xenografts were transplanted into 16 baboon recipients. METHODS: Baseline immunosuppression for all groups included cyclosporine A, cyclophosphamide, SDZ-RAD, and methylprednisolone. Group 1 received only baseline immunosuppression; group 2 animals received low-dose GAS914 with baseline immunosuppression; group 3 animals received high dose GAS914 with high-dose baseline immunosuppression; and animals from group 4 received high-dose GAS914 and low-dose baseline immunosuppression. RESULTS: None of the animals in this study developed hyperacute rejection. Intravenous administration of GAS914 significantly reduced xenoreactive antibodies as measured by antiporcine hemolytic assays and anti-Gal (immunoglobulin [Ig] G and IgM) antibody assays. Rejection was less severe in the GAS914-treated group. Only 25% (3 of 12) of GAS914-treated animals were killed as a result of rejection, whereas 75% (three of four) of non-GAS914-treated animals were killed because of terminal rejection (P<0.01). Protocol biopsies demonstrated that the degree of acute humoral xenograft rejection (AHXR) was reduced in the GAS914-treated animals compared with non-GAS914-treated animals. CONCLUSION: The intravenous administration of GAS914 reduces xenoreactive antibody levels and reduces the degree of porcine kidney xenograft rejection, but does not improve survival. AHXR and drug toxicity remain major barriers to the long-term success of xenotransplantation.
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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 | 0.000 |
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".