Inability of Viral Superantigens to Induce CD4-mediated Islet Transplant Rejection
Bibliographic record
Abstract
Superantigens have the ability to bypass the specific interactions of the MHC class II and T-cell receptor by binding outside of the peptide binding region and onto the Vβ chain. This ability allows superantigens to stimulate a wide array of T-cell populations, irrespective of T-cell receptor (TCR) specificity. Research on bacterial and viral superantigens have demonstrated various outcomes ranging from superantigen dependent cellular cytoxicity (SDCC), rheumatoid arthritis, to superantigen stimulated T-cell clonal deletion and anergy. Due to its ability to proliferate a large population of T-cells, this paper asks whether superantigens have a role in islet transplant rejection. We used transgenic Vβ6 TCR mice specific for male ‘H-Y’ antigen as recipients to islet transplants. Donors comprised mice expressing endogenous superantigen specific for the Vβ6 chain. Transplantation of these donor islets did not induce rejection. Recipients were also primed with ‘H-Y’ antigen to induce a CD4 effector memory T-cell population prior to islet transplantation. Even with primed recipients, donor islet transplants did not induce rejection by recipient transgenic mice.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".