Early posttransplantation donor-derived invariant natural killer T-cell recovery predicts the occurrence of acute graft-versus-host disease and overall survival
Bibliographic record
Abstract
Invariant natural killer T (iNKT) cells can experimentally dissociate GVL from graft-versus-host-disease (GVHD). Their role in human conventional allogeneic hematopoietic stem cell transplantation (HSCT) is unknown. Here, we analyzed the post-HSCT recovery of iNKT cells in 71 adult allografted patients. Results were compared with conventional T- and NK-cell recovery and correlated to the occurrence of GVHD, relapse, and survival. We observed that posttransplantation iNKT cells, likely of donor origin, recovered independently of T and NK cells in the first 90 days after HSCT and reached greater levels in recipient younger than 45 years (P = .003) and after a reduced-intensity conditioning regimen (P = .03). Low posttransplantation iNKT/T ratios (ie, < 10(-3)) were an independent factor associated with the occurrence of acute GVHD (aGVHD; P = .001). Inversely, reaching iNKT/T ratios > 10(-3) before day 90 was associated with reduced nonrelapse mortality (P = .009) without increased risk of relapse and appeared as an independent predictive factor of an improved overall survival (P = .028). Furthermore, an iNKT/T ratio on day 15 > 0.58 × 10(-3) was associated with a 94% risk reduction of aGVHD. These findings provide a proof of concept that early postallogeneic HSCT iNKT cell recovery can predict the occurrence of aGVHD and an improved overall survival.
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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".