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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".