Extracellular dsRNA: Its Function and Mechanism of Cellular Uptake
Bibliographic record
Abstract
Double-stranded RNA (dsRNA) is arguably the most potent viral trigger of innate immune signaling. Its activity has been recognized for over 5 decades, first as a toxin, then as a central component of the interferon system, as an efficient activator of antiviral responses and an immunomodulator for therapeutic applications. Nucleic acid sensing is the main basis for antiviral defense systems throughout the diverse forms of life from bacteria to plants and animals. Pattern recognition receptors of the host defense system not only sense viral dsRNA as a pathogen-associated molecular pattern in infected cells, but also recognize circulating endogenous dsRNA, a nonmicrobial signal, as a danger-associated molecular pattern, often leading to autoimmunity. Despite the effects of extracellular viral and host dsRNA associated with infection and autoimmunity, respectively, the understanding of cellular mechanisms for its recognition and uptake has only been appreciated in recent years. This review presents an overview of this unique form of nucleic acid, addressing its roles in infection, autoimmunity, and host sensing mechanisms. The goal of this review is to highlight the novel findings with a focus on extracellular recognition and uptake by the cell.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".