When human guanylate-binding proteins meet viral infections
Bibliographic record
Abstract
Innate immunity is the first line of host defense against viral infection. After invading into the cells, pathogen-associated-molecular-patterns derived from viruses are recognized by pattern recognition receptors to activate the downstream signaling pathways to induce the production of type I interferons (IFN-I) and inflammatory cytokines, which play critical functions in the host antiviral innate immune responses. Guanylate-binding proteins (GBPs) are IFN-inducible antiviral effectors belonging to the guanosine triphosphatases family. In addition to exerting direct antiviral functions against certain viruses, a few GBPs also exhibit regulatory roles on the host antiviral innate immunity. However, our understanding of the underlying molecular mechanisms of GBPs' roles in viral infection and host antiviral innate immune signaling is still very limited. Therefore, here we present an updated overview of the functions of GBPs during viral infection and in antiviral innate immunity, and highlight discrepancies in reported findings and current challenges for future studies, which will advance our understanding of the functions of GBPs and provide a scientific and theoretical basis for the regulation of antiviral innate immunity.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".