The functional impairment of natural killer cells during influenza virus infection
Bibliographic record
Abstract
Natural killer (NK) cells have a critical role in clearing influenza virus, which primarily infects the lung epithelial cells. However, the ability of influenza virus to infect and manipulate NK cells has not been studied. In this context, we hypothesized that influenza virus can target NK cells leading to a functional impairment in their ability to mediate cytotoxicity and cytokine/chemokine generations. Here, we show influenza virus, PR8, can enter and infect NK cells. This infection did not alter the expression levels of activating, inhibitory or developmental receptors of NK cells. However, infection of NK cells by PR8 reduced the cytotoxicity to tumor cells that represent 'induced-self' and 'missing-self'. PR8-infection also significantly downregulated the NCR1, NKG2D, Nkpr1c, Ly49D and CD244 receptors-mediated generation of pro-inflammatory cytokines and chemokines. Mutations in the non-structural protein 1 (NS1) of influenza virus further augmented the functional impairment of NK cells. Our observations show the presence of a new, but yet to be explored, mechanism by which the influenza virus can evade immune detection.
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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".