The effect of COVID-19 and sex differences on natural killer cell cytotoxicity
Bibliographic record
Abstract
COVID-19 has caused more than 7 million deaths worldwide, and according to the World Health Organization, it continues to result in more than 1000 reported deaths per month at the time of this writing. It is crucial to understand the immune response to COVID-19 since the virus continues to persist. Natural killer (NK) cells play a critical role in the immune defense against viral infections, including COVID-19. While it is well documented that infected patients have a reduction in lymphocytes and NK cells, gaps in knowledge exist regarding the function of NK cells. To study the function of NK cells in patients hospitalized with COVID-19, peripheral blood was obtained from patients admitted to the medical (non-ICU) wards at a large tertiary hospital. We demonstrated a decrease in the mature cytotoxic subset of NK cells within the peripheral blood of patients hospitalized with COVID-19. We also observed a notable reduction in the cytotoxic function of NK cells against tumor targets. We examined the mechanisms leading to NK cell killing. We found reductions in the intracellular levels of effector molecules, the degranulation of cytotoxic granules, and the extracellular concentrations of released effector molecules. We identified dysfunctional intracellular granule trafficking required to position the granules for degranulation, which would be consistent with the reduced release of effector molecules. We found clusters of inhibitory receptors were upregulated in subsets of NK cells, in keeping with inhibition of cytotoxicity. Additionally, males with COVID-19 showed NK cell defects compared to healthy males, while no significant differences were observed in females. Our findings highlight defects in cytolytic effector molecules, granule trafficking and release, and increased expression of inhibitory receptors on NK cells in patients hospitalized with COVID-19, in addition to a sex difference in cytolytic function, which contributes to defective NK cell function in COVID-19.
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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) | 0.003 | 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".