Crosstalk between RNA viruses and DNA sensors: Role of the cGAS‐STING signalling pathway
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite only comprising half of all known viral species, RNA viruses are disproportionately responsible for many of the worst epidemics in human history, including outbreaks of influenza, poliomyelitis, Ebola, and most recently, the coronavirus disease-2019 (COVID-19) pandemic. The propensity for RNA viruses to replicate in cytosolic compartments has led to an evolutionary arms race and the emergence of cytosolic sensors to recognise and initiate the host innate immune response. Although significant progress has been made in identifying and characterising cytosolic RNA sensors as anti-viral innate immune factors, the potential role for cytosolic DNA sensors in RNA viral infection is only recently being appreciated. Among these, the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway has attracted increasing attention. The cGAS-STING signalling pathway has emerged as a key innate immune signalling axis that is implicated in diverse human diseases from infectious diseases to neurodegeneration and cancer. Here we review the existing literature on RNA viruses and their reciprocal interactions with the cGAS-STING pathway and share insights into RNA virus diversity by touching on the similarities and differences of RNA viral strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it