Impaired natural killer cell counts and cytolytic activity in patients with severe COVID-19
Bibliographic record
Abstract
The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-driven coronavirus disease 2019 (COVID-19) has caused unprecedented human death and has seriously threatened the global economy. Early data suggest a surge in proinflammatory cytokines in patients with severe COVID-19, which has been associated with poor outcomes. We recently postulated that the inflammatory response in patients with severe COVID-19 disease is not inhibited by natural killer (NK) cells, resulting in a "cytokine storm." Here, we assessed the NK-cell functional activity and the associated cytokines and soluble mediators in hospitalized COVID-19 patients. Significantly impaired NK-cell counts and cytolytic activity were observed in COVID-19 patients when compared with healthy controls. Also, cytokines like interleukin 12 (IL12), IL15, and IL21 that are important for NK-cell activity were not detected systematically. Serum concentrations of soluble CD25 (sCD25)/soluble IL2 receptor α (sIL2-Rα) were significantly elevated and were inversely correlated with the percentage of NK cells. Impaired NK-cell cytolytic activity together with other laboratory trends including elevated sCD25 were consistent with a hyperinflammatory state in keeping with macrophage-activation syndrome. Our findings suggest that impaired counts and cytolytic activity of NK cells are important characteristics of severe COVID-19 and can potentially facilitate strategies for immunomodulatory therapies.
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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) | 0.001 | 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 | 0.001 |
| 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".