Successful resolution of inflammation and increased regulatory T cells in sirolimus‐treated post‐transplant allograft hepatitis
Bibliographic record
Abstract
Ekong UD, Mathew J, Melin‐Aldana H, Wang D, Alonso EM. Successful resolution of inflammation and increased regulatory T cells in sirolimus‐treated post‐transplant allograft hepatitis. Pediatr Transplantation 2012: 16: 165–175. © 2012 John Wiley & Sons A/S. Abstract: This retrospective case series reviews our center’s experience with sirolimus and a CNI as alternative therapy for the treatment of PTAH. It also characterizes regulatory T cells (Tregs) in PTAH. LT recipients with PTAH who had received or were receiving treatment with sirolimus were retrospectively identified (n = 12). Liver enzymes, immunohistochemistry, and histology were compared in all 12 patients. Immunophenotyping for Tregs in peripheral blood mononuclear cells was performed on LT recipients with PTAH on conventional therapy with CNI, azathioprine ± prednisone (CT) (n = 11), recipients with PTAH on sirolimus, CNI ± prednisone (n = 8), recipients without PTAH (n = 25), and pre‐transplant patients (n = 5). Severity of necro‐inflammatory changes markedly improved with sirolimus. Treg frequency and number were significantly lower in recipients with PTAH on CT compared to (i) those on sirolimus (p = 0.002 and p = 0.01, respectively), and (ii) recipients without PTAH (p = 0.07 and p = 0.009, respectively). Treg frequency was significantly higher in recipients with PTAH on sirolimus compared to recipients without PTAH under CNI therapy (p = 0.027). Sirolimus in addition to a CNI is successful in reversing inflammation in LT recipients with PTAH. This is associated with significantly higher circulating Tregs.
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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 | 0.001 |
| 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".