Nucleic Acid-Sensing Pathways During SARS-CoV-2 Infection: Expectations versus Reality
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
The coronavirus disease 2019 (COVID-19) pandemic has affected millions of people and crippled economies worldwide. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for this pandemic has triggered avid research on its pathobiology to better understand the pathophysiology of COVID-19. In the absence of approved antiviral therapeutic strategies or vaccine platforms capable of effectively targeting this global threat, the hunt for effective therapeutics has led to many candidates being actively evaluated for their efficacy in controlling or preventing COVID-19. In this review, we gathered current evidence on the innate nucleic acid-sensing pathways expected to be elicited by SARS-CoV-2 and the immune evasion mechanisms they have developed to promote viral replication and infection. Within the nucleic acid-sensing pathways, SARS-CoV-2 infection and evasion mechanisms trigger the activation of NOD-signaling and NLRP3 pathways leading to the production of inflammatory cytokines, IL-1β and IL-6, while muting or blocking cGAS-STING and interferon type I and III pathways, resulting in decreased production of antiviral interferons and delayed innate response. Therefore, blocking the inflammatory arm and boosting the interferon production arm of nucleic acid-sensing pathways could facilitate early control of viral replication and dissemination, prevent disease progression, and cytokine storm development. We also discuss the rationale behind therapeutic modalities targeting these sensing pathways and their implications in the treatment of COVID-19.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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