Can Natural Killer Cells Be a Principal Player in Anti-SARS-CoV-2 Immunity?
Bibliographic record
Abstract
The COVID-19 pandemic caused by SARS-CoV-2 virus infection is resulting in enormous loss of lives and devastating the global economy. Elderly populations and those with co-morbidities are often unable to clear the infection. With several hundred thousand people dead worldwide and many more infected, it is essential to understand the immunological response that results in beneficial or detrimental disease outcomes in this infection. Natural Killer (NK) cells from the innate immune system are early cytotoxic responders that can eradicate virus-infected cells and, therefore, may help control the infection. Besides, NK cells are not only responsible for target lysis but are also immunomodulators that produce pro-inflammatory or anti-inflammatory cytokines such as IFN-γ or IL-10, respectively. Notably, COVID-19 patients consistently experience a loss in total NK cell counts with increased expression of the NKG2A receptors, and such phenotypes are highly correlated to the severity of the disease. Several clinical trials have been initiated to perfuse NK cells to COVID-19 patients. Since numerous reports consistently indicate that COVID-19 patients develop a 'cytokine storm' with increased levels of multiple pro-inflammatory cytokines, trials employing NK cells holding the pro-inflammatory property would be not ideal. Here, we contemplate the impact of NK cell loss on NK cell immunity and cytokine storm in SARS-CoV-2 infection, and propose adequate NK cell therapies for COVID-19 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".