Bibliographic record
Abstract
In recent years, our understanding of the role of natural killer (NK) cells in the response to viral infection has grown rapidly. Not only do we realize viruses have many immune-evasion strategies to escape NK cell responses, but that stimulation of NK cell subsets during an antiviral response occurs through receptors seemingly geared directly at viral products and that NK cells can provide a memory response to viral pathogens. Tremendous knowledge has been gained in this area through the study of herpes viruses, but appreciation for the significance of NK cells in the response to other types of viral infections is growing. The function of NK cells in defense against poxviruses has emerged over several decades beginning with the early seminal studies showing the role of NK cells and the NK gene complex in susceptibility of mouse strains to ectromelia, a poxvirus pathogen of mice. More recently, greater understanding has emerged of the molecular details of the response. Given that human diseases caused by poxviruses can be as lethal as smallpox or as benign as Molluscum contagiosum, and that vaccinia virus, the prototypic member of the pox family, persists as a mainstay of vaccine design and has potential as an oncolytic virus for tumor therapy, further research in this area remains important. This review focuses on recent advances in understanding the role of NK cells in the immune response to poxviruses, the receptors involved in activation of NK cells during poxvirus infection, and the viral evasion strategies poxviruses employ to avoid the NK response.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".