Selective unresponsiveness to beta cell autoantigens after induction immunosuppression in pancreas transplantation with anti-interleukin-2 receptor antibody <i>versus</i> anti-thymocyte globulin
Bibliographic record
Abstract
Pancreas transplantation in type 1 diabetes patients could result in (re)activation of allo- and autoreactive T lymphocytes. Anti-thymocyte globulin (ATG) induction treatment is a successful, but broadly reactive anti-lymphocyte therapy used in pancreas and islet transplantation. A more selective alternative is daclizumab, a monoclonal antibody directed against the interleukin-2 receptor (CD25) on activated lymphocytes. We tested the hypothesis that daclizumab is more selective and has less immunological side effects than ATG. Thirty-nine simultaneous pancreas-kidney transplantation patients with type 1 diabetes were randomized for induction therapy with ATG or daclizumab. Auto- and recall immunity was measured cross-sectionally by lymphocyte stimulation tests with a series of auto- and recall antigens in 35 successfully transplanted patients. T cell autoimmunity to islets was low in both groups, except for a marginal but significantly higher reactivity against glutamic acid decarboxylase (GAD)65 in daclizumab-treated patients. The memory responses to recall antigens were significantly higher in the daclizumab-treated group compared to ATG-treated patients, specifically against purified protein derivative (PPD) (anti-bacterial immunity), Haemophilus influenzae virus matrix protein-1 (anti-viral immunity) and p53 [anti-tumour (auto)immunity]. These data imply that daclizumab is more specifically affecting diabetes-related immune responses than ATG. The autoimmunity is affected effectively after daclizumab induction, while memory responses towards bacterial, viral and tumour antigens are preserved.
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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 | 0.001 |
| 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".