Identification of regulatory natural killer cells in pediatric chronic graft-versus-host disease patients
Bibliographic record
Abstract
Abstract Chronic graft-versus-host disease (cGVHD) is the leading cause of late morbidity and mortality in pediatric patients, which is caused by dynamic interferences of multiple types of immune cells, including natural killer (NK) cells. While earlier studies have indicated the potential regulatory function of NK cells restraining pathogenic cell types, the identity of unique NK cell subsets remains unknown. We performed single-cell RNA sequencing on NK cells isolated from the PBMC of patients who developed cGVHD (3 patients) or not (4 patients). Through computational analyses, we identified distinct NK cell populations and found multiple subsets of NK cells expressing unique transcriptomes in patients who received hematopoietic stem cell transplantation (HSCT) compared to healthy individuals, with one of the NK cell subsets exclusively present in post-HSCT patients. Significantly different gene expression patterns were observed between the two groups of patients, including cytotoxicity-related genes elevated in cGVHD patients. By applying diverse statistical analyses, we were able to characterize these NK cell subsets, indicating their potential roles in the development or prevention of cGVHD. Our study defines novel subsets of protective NK cells during cGVHD. These findings are of high translational relevance and can contribute to the treatment of cGVHD patients.
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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 | 0.000 | 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".