Recent advances in understanding viral evasion of type I interferon
Bibliographic record
Abstract
The type I interferon (IFN) system mediates a wide variety of antiviral effects and represents an important first barrier to virus infection. Consequently, viruses have developed an impressive diversity of tactics to circumvent IFN responses. Evasion strategies can involve preventing initial virus detection, via the disruption of the Toll-like receptors or the retinoic acid inducible gene I (RIG-I) -like receptors, or by avoiding the initial production of the ligands recognized by these receptors. An alternative approach is to preclude IFN production by disarming or degrading the transcription factors involved in the expression of IFN, such as interferon regulatory factor 3 (IRF3)/IRF7, nuclear factor-κB (NF-κB), or ATF-2/c-jun, or by inducing a general block on host cell transcription. Viruses also oppose IFN signalling, both by disturbing the type I IFN receptor and by impeding JAK/STAT signal transduction upon IFN receptor engagement. In addition, the global expression of IFN-stimulated genes (ISGs) can be obstructed via interference with epigenetic signalling, and specific ISGs can also be selectively targeted for inhibition. Finally, some viruses disrupt IFN responses by co-opting negative regulatory systems, whereas others use antiviral mechanisms to their own advantage. Here, we review recent developments in this field.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".