Diverse NKT cells regulate early inflammation and neurological outcomes after cardiac arrest and resuscitation
Bibliographic record
Abstract
Neurological injury drives most deaths and morbidity among patients hospitalized for out-of-hospital cardiac arrest (OHCA). Despite its clinical importance, there are no effective pharmacological therapies targeting post–cardiac arrest (CA) neurological injury. Here, we analyzed circulating immune cells from a large cohort of patients with OHCA, finding that lymphopenia independently associated with poor neurological outcomes. Single-cell RNA sequencing of immune cells showed that T cells with features of both innate T cells and natural killer (NK) cells were increased in patients with favorable neurological outcomes. We more specifically identified an early increase in circulating diverse NKT (dNKT) cells in a separate cohort of patients with OHCA who had good neurological outcomes. These cells harbored a diverse T cell receptor repertoire but were consistently specific for sulfatide antigen. In mice, we found that sulfatide-specific dNKT cells trafficked to the brain after CA and resuscitation. In the brains of mice lacking NKT cells ( Cd1d −/− ), we observed increased inflammatory chemokine and cytokine expression and accumulation of macrophages when compared with wild-type mice. Cd1d −/− mice also had increased neuronal injury, neurological dysfunction, and worse mortality after CA. To therapeutically enhance dNKT cell activity, we treated mice with sulfatide lipid after CA, showing that it improved neurological function. Together, these data show that sulfatide-specific dNKT cells are associated with good neurological outcomes after clinical OHCA and are neuroprotective in mice after CA. Strategies to enhance the number or function of dNKT cells may thus represent a treatment approach for CA.
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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".