Evaluating porcine RBC and platelet α‐galactosyl expression
Bibliographic record
Abstract
BACKGROUND: Naturally occurring human xenoreactive antibodies bind and agglutinate porcine RBCs. STUDY DESIGN AND METHODS: To determine if xenoantigen expression on RBCs of individual pigs of different breeds and blood groups is variable, and if it correlates with platelet (PLT) expression, we measured adsorption of affinity-purified antibodies to alpha-galactosyl (alphaGal) by RBCs or PLTs from 22 pigs representing four breeds. Hemagglutination of RBCs from these pigs was also performed with pools of human group OAB, A, B, and AB sera, as well as with anti-alphaGal-depleted pooled OAB human serum. RESULTS: There was significant variation in alphaGal expression on RBCs and PLTs among pigs. PLT alphaGal expression did not correlate with RBC alphaGal. RBCs from all pigs were agglutinated by pooled group O, AB, A, or B sera, whereas titers were reduced by 87 percent with anti-alphaGal-depleted serum and by 82 percent with AB sera from two volunteers. Agglutination titers were higher against RBCs from the five highest RBC alphaGal expressers compared with those from the five lowest RBC alphaGal expressers (92 +/- 12 vs. >160, p < 0.05, where 160 was the maximum dilution tested). CONCLUSION: Hemagglutination is a feasible alternative for rapid identification of pigs with RBCs carrying less alphaGal.
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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".