Technical knockout: understanding poxvirus pathogenesis by selectively deleting viral immunomodulatory genes
Bibliographic record
Abstract
The study of viral pathogens with genomes as large and complex as poxviruses represents a constant experimental challenge. Advances in recombinant DNA technologies have provided sophisticated methods to produce mutants defective in one or more viral genes, termed knockout (KO) viruses, thereby facilitating research into the impact of specific gene products on viral pathogenesis. Such strategies have rapidly advanced the systematic mining of many poxvirus genomes and enabled researchers to identify and characterize poxvirus genes whose functions represent the culmination of host and pathogen coevolution. Of particular interest are the multiple classes of virus-encoded immunomodulatory proteins that have evolved specifically to allow poxviruses to evade, obstruct or subvert critical elements within the host innate and acquired immune responses. Functional characterization of these viral genes by generating KO viruses and investigating the phenotypic changes that result is an important tool for understanding the molecular mechanisms underlying poxvirus replication and pathogenesis. Moreover, the insights gained have led to new developments in basic and clinical virology, provided a basis for the design of new vaccines and antivirals, and increased the potential application of poxviruses as investigative tools and sources of biotherapeutics for the treatment of human diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".