Carbohydrate antigen microarray analysis of serum IgG and IgM antibodies before and after adult porcine islet xenotransplantation in cynomolgus macaques
Bibliographic record
Abstract
Understanding the anti-carbohydrate antibody response toward epitopes expressed on porcine cells, tissues, and organs is critical to advancing xenotransplantation toward clinical application. In this study, we determined IgM and IgG antibody specificities and relative concentrations in five cynomolgus monkeys at baseline and at intervals following intraportal xenotransplantation of adult porcine islets. This study utilized a carbohydrate antigen microarray that comprised more than 400 glycoconjugates, including historically reported α-Gal and non-α-Gal carbohydrate antigens with various modifications. The elicited anti-carbohydrate antibody responses were predominantly IgM compared to IgG in 4 out of 5 monkeys. Patterns of elicited antibody responses greater than 1.5 difference (log2 base units; 2.8-fold on a linear scale) from pre-serum to post-serum sampling specific for carbohydrate antigens were heterogeneous and recipient-specific. Increases in the elicited antibody response to α-Gal, Sda, GM2 antigens, or Lexis X antigen were found in individual monkeys. The novel carbohydrate structures Galβ1-4GlcNAcβ1-3Galβ1 and N-linked glycans with Manα1-6(GlcNAcβ1-2Manα1-3)Manβ1-4GlcNAcβ structure were common targets of elicited IgM antibodies. These results provide important insights into the carbohydrate epitopes that elicit antibodies following pig-to-monkey islet xenotransplantation and reveal possible targets for gene editing.
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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".