NK Cell‐mediated Destruction of Influenza A Virus‐infected Peripheral but not Central Neurones
Bibliographic record
Abstract
Peripheral neurones have the potential to transmit infectious agents to the central nervous system (CNS). This raises the possibility of existing host defence mechanisms that may prevent such spread. Natural killer (NK) cells can target infected cells, and by this ability serve to limit spread of infection prior to the development of adaptive immune responses. To address directly if NK cells can target infected peripheral neurones, we examined the expression of NK cell-activating ligands and susceptibility to NK cell-mediated cytolytic effects in ex vivo cultures of mouse peripheral dorsal root ganglia (DRG) neurones prior to and after infection with a neurotropic strain of influenza A virus, WSN/33. In infected DRG cultures, retinoic acid early inducible gene-1 (RAE-1) transcripts were induced and exposure to interleukin (IL)-2-activated NK cells resulted in a total destruction of neurites. Studies on cultures from interferon (IFN)-alpha/betaR-deficient mice suggest that the infection engages an IFN-alpha/beta-dependent signalling pathway to induce RAE-1 transcripts. In contrast, induction of RAE-1 transcripts or NK cell-mediated neurite destructions was not observed in central hippocampal neurones. This reveals distinct properties between peripheral DRG and central hippocampal neurones with respect to the ability to signal for immune destruction following infection.
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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".