CD25 Blockade Delays Regulatory T Cell Reconstitution and Does Not Prevent Graft-versus-Host Disease After Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
regulatory T cells (Tregs) was recognized. Tregs can abrogate the onset of GVHD. The relation between Tregs and a graft-versus-malignancy effect is not fully understood. An international, multicenter, double-blind clinical trial randomized 210 adult or pediatric patients to receive 5 weekly doses of daclizumab at 0.3 mg/kg (n = 69) or 1.2 mg/kg (n = 76) or placebo (n = 65) after unrelated marrow transplantation for treatment of hematologic malignancies or severe aplastic anemia. The risk of acute GVHD did not differ among the groups (P = .68). Long-term follow-up of clinical outcomes and correlative analysis of peripheral blood T cell phenotype suggested that the patients treated with daclizumab had an increased risk of chronic GVHD (hazard ratio [HR], 1.49; 95% confidence interval [CI], 1.0 to 2.3; P = .08) and a decreased risk of relapse (HR, 0.57; 95% CI, 0.3 to 1.0; P = .05), but similar survival (HR, 0.89; 95% CI, 0.6 to 1.3; P = .53). T cells from a subset of patients (n = 107) were analyzed by flow cytometry. Compared with placebo, treatment with daclizumab decreased the proportion of Tregs among CD4 T cells at days 11-35 and increased the proportion of central memory cells among CD4 T cells at 1 year. Prophylactic administration of daclizumab does not prevent acute GVHD, but may increase the risk of chronic GVHD and decrease the risk of relapse. By delaying Treg reconstitution and promoting immunologic memory, anti-CD25 therapy may augment alloreactivity and antitumor immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".