<scp>NK</scp> cells exacerbate the pathology of influenza virus infection in mice
Bibliographic record
Abstract
NK cells offer a first line of defense against viruses and are considered beneficial to the host during infection. Nevertheless, little is understood regarding the phenotype and function of NK cells in the lung during influenza virus infection. We found that the frequency of NK cells in mouse lung increased during influenza infection, with the majority of a mature phenotype. Cell surface CD107a and intracellular IFN-γ were detected in cells expressing multiple NK-cell receptors in infected lung, suggesting that NK cells were activated during infection. The activating receptor NKp46 was predominantly negative on such cells, possibly as a result of encountering influenza HA. Depletion of NK cells in vivo with anti-asialo GM1 or anti-NK1.1 reduced mortality from influenza infection and surviving mice recovered their body weight. Pathology induced by NK cells was only observed with high, not medium or low-dose influenza infection, indicating that the severity of infection influences NK-cell-mediated pathology. Furthermore, adoptive transfer of NK cells from influenza-infected lung, but not uninfected lung, resulted in more rapid weight loss and increased mortality of influenza-infected mice. Our results indicate that during severe influenza infection of the lung, NK cells have a deleterious impact on the host, promoting mortality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".