Sensors of Infection: Viral Nucleic Acid PRRs in Fish
Bibliographic record
Abstract
Viruses produce nucleic acids during their replication, either during genomic replication or transcription. These nucleic acids are present in the cytoplasm or endosome of an infected cell, or in the extracellular space to be sensed by neighboring cells during lytic infections. Cells have mechanisms of sensing virus-generated nucleic acids; these nucleic acids act as flags to the cell, indicating an infection requiring defense mechanisms. The viral nucleic acids are called pathogen-associated molecular patterns (PAMPs) and the sensors that bind them are called pattern recognition receptors (PRRs). This review article focuses on the most recent findings regarding nucleic acids PRRs in fish, including: Toll-like receptors (TLRs), RIG-I-like receptors (RLRs), cytoplasmic DNA sensors (CDSs) and class A scavenger receptors (SR-As). It also discusses what is currently known of the downstream signaling molecules for each PRR family and the resulting antiviral response, either type I interferons (IFNs) or pro-inflammatory cytokine production. The review highlights what is known but also defines what still requires elucidation in this economically important animal. Understanding innate immune systems to virus infections will aid in the development of better antiviral therapies and vaccines for the future.
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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 | 0.002 | 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 | 0.001 |
| 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".