THE ROLE OF ANTI-GAL??1-3GAL ANTIBODIES IN ACUTE VASCULAR REJECTION AND ACCOMMODATION OF XENOGRAFTS1
Bibliographic record
Abstract
BACKGROUND: A major impediment to the transplanting of porcine organs into humans is the susceptibility of porcine organs to acute vascular rejection, which can destroy a vascularized xenograft over a period of hours to days. Acute vascular rejection of porcine-to-primate xenografts is thought to be triggered by binding of xenoreactive antibodies to the graft. We tested whether antibodies, binding to Galalpha1-3Gal epitopes in porcine tissue, initiate this phenomenon. METHODS AND RESULTS: Specific depletion of anti-Galalpha1-3Gal antibodies from the blood of baboons, using extracorporeal perfusion of separated plasma through columns of Sepharose beads covalently linked to the antigenic trisaccharide, Galalpha1-3Galbeta1-4GlcAc, averted the development of acute vascular rejection in porcine organs transgenic for human decay-accelerating factor and CD59. More importantly, after immunodepletion was stopped and Gala1-3Gal antibodies were allowed to return, these same organs continued to function and remained pathologically normal and thus seemed to achieve a state of accommodation. CONCLUSION: These results demonstrate that anti-Galalpha1-3Gal antibodies cause acute vascular rejection and suggest that depletion of these antibodies leads to accommodation of the donor cardiac xenograft and could supply an important model for additional study.
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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 | 0.001 | 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 | 0.001 | 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".