Catch me if you can: viral nucleic acids to host sensors
Bibliographic record
Abstract
is a cat-and-mouse story in which Frank Abagnale Jr. successfully conned his way into several high-profile jobs while evading capture by FBI agent Carl Hanratty. Similarly, after entering host cells, viruses interact with or hijack host cellular machinery to replicate their genetical materials and assemble themselves for the next round of infection. Analogous to an FBI agent, host cells have numerous molecular "detectives" that recognize viral nucleic acids (NAs). These include RIG-I, MDA5, LGP2, TLR3, TLR7, TLR8, DHX36, DICER1, PKR, OAS1, ZAP, and NLRP1/6 for viral RNA, as well as cGAS, TLR9, AIM2, IFI16, IFIX, Ku70, MRE11, RNA polymerase III, hnRNPA2B1, LRRFIP1, DAI, DHX9 and DDX41 for viral DNA. However, much like the brilliant Frank Abagnale Jr., viruses have developed various strategies to evade host cellular surveillance-for example, by sequestering or modifying viral NAs and inhibiting or degrading host sensors. In this review, we will summarize the host sensors identified so far, discuss the latest understandings of the various strategies employed by viruses, and highlight the challenges associated with drug development to target virus or host factors. Considering recent global health challenges such as the COVID-19 pandemic and undergoing measles outbreak, understanding virus-host interactions at the molecular and cellular levels remains essential for the development of novel therapeutic strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".