Detecting Rejection after Mouse Islet Transplantation Utilizing Islet Protein-Stimulated ELISPOT
Bibliographic record
Abstract
Improved posttransplant monitoring and on-time detection of rejection could improve islet transplantation outcome. The present study explored the possibility of detecting harmful events after mouse islet transplantation measuring the immune responsiveness against islet extracts. Mouse islet transplantations were performed using various donor/recipient combinations, exploring autoimmune (NOD/SCID to NOD, n = 6) and alloimmune events (C57BL/6 to BALB/c, n = 20), a combination of both (C57BL/6 to NOD, n = 8), the absence of both (BALB/c to BALB/c, n = 21), or naive, nontransplanted control mice (n = 14). The immune reactivity was measured by ELISPOT, looking at the ex vivo release of IFN-γ from splenocytes stimulated by islet donor extracts (sonicated islets). The immune reactivity was not altered in the syngeneic and autoimmune models, demonstrating similar levels as nontransplanted controls (p = 0.46 and p = 0.6). Conversely, the occurrence of an allogeneic rejection alone or in combination to autoimmunity was associated to an increase in the level of immune reactivity (p = 0.023 and p = 0.003 vs. respective controls). The observed increase was transient and lost in the postrejection period or after treatment with CTLA4-Ig. Overall, allogeneic rejection was associated to a transient increase in the reactivity of splenocytes against islet proteins. Such a strategy has the potential to improve islet graft monitoring in human and should be further explored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".