Activating receptors promote NK cell expansion for maintenance, IL-10 production, and CD8 T cell regulation during viral infection
Bibliographic record
Abstract
Natural killer (NK) cells have the potential to deliver both direct antimicrobial effects and regulate adaptive immune responses, but NK cell yields have been reported to vary greatly during different viral infections. Activating receptors, including the Ly49H molecule recognizing mouse cytomegalovirus (MCMV), can stimulate NK cell expansion. To define Ly49H's role in supporting NK cell proliferation and maintenance under conditions of uncontrolled viral infection, experiments were performed in Ly49h(-/-), perforin 1 (Prf1)(-/-), and wild-type (wt) B6 mice. NK cell numbers were similar in uninfected mice, but relative to responses in MCMV-infected wt mice, NK cell yields declined in the absence of Ly49h and increased in the absence of Prf1, with high rates of proliferation and Ly49H expression on nearly all cells. The expansion was abolished in mice deficient for both Ly49h and Prf1 (Ly49h(-/-)Prf1(-/-)), and negative consequences for survival were revealed. The Ly49H-dependent protection mechanism delivered in the absence of Prf1 was a result of interleukin 10 production, by the sustained NK cells, to regulate the magnitude of CD8 T cell responses. Thus, the studies demonstrate a previously unappreciated critical role for activating receptors in keeping NK cells present during viral infection to regulate adaptive immune responses.
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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 | 0.001 |
| 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".