Double-Negative T Cell Levels Correlate with Chronic Graft-versus-Host Disease Severity
Bibliographic record
Abstract
Chronic graft-versus-host disease (cGVHD) is a major complication, affecting 50% to 80% of long-term survivors of allogeneic hematopoietic stem cell transplantation. Current cGVHD therapies are neither specific nor curative, and patients are typically maintained for several months to years under immunosuppressive regimens that are associated with important side effects and increased susceptibility to life-threatening infections. As a result, continued investigation into the pathology of the disease and the search for novel diagnostic and therapeutic strategies to treat cGVHD remains a high priority. We report that the cellular dynamics of various immune cell subsets are related to cGVHD onset and severity in a cohort of allogeneic hematopoietic stem cell transplantation recipients. We document a decrease in the proportion of CD45RO + CD4 − CD8 − (double-negative [DN]) T cells at the onset of cGVHD, a time at which serum levels of B cell activating factor and B cells are increased. We also find that DN T cell levels are correlated with cGVHD severity. Our present findings are in line with the view that activated DN T cells exhibit their immunoregulatory potential by eliminating B cells in vivo. Taken together, these findings suggest that maintaining elevated DN T cell numbers before the onset of cGVHD may prevent pathological B cell responses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".