The Evolutionary Arms Race between Virus and NK Cells: Diversity Enables Population-Level Virus Control
Bibliographic record
Abstract
Viruses and natural killer (NK) cells have a long co-evolutionary history, evidenced by patterns of specific NK gene frequencies in those susceptible or resistant to infections. The killer immunoglobulin-like receptors (KIR) and their human leukocyte antigen (HLA) ligands together form the most polymorphic receptor-ligand partnership in the human genome and govern the process of NK cell education. The KIR and HLA genes segregate independently, thus creating an array of reactive potentials within and between the NK cell repertoires of individuals. In this review, we discuss the interplay between NK cell education and adaptation with virus infection, with a special focus on three viruses for which the NK cell response is often studied: human immunodeficiency virus (HIV), hepatitis C virus (HCV) and human cytomegalovirus (HCMV). Through this lens, we highlight the complex co-evolution of viruses and NK cells, and their impact on viral control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".