Extracellular Vesicles Loaded with Long Antisense RNAs Repress Severe Acute Respiratory Syndrome Coronavirus 2 Infection
Bibliographic record
Abstract
Long antisense RNAs (asRNAs) have been observed to repress HIV and other virus expression in a manner that is refractory to viral evolution. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus disease 2019 (COVID-19) disease, has a distinct ability to evolve resistance around antibody targeting, as was evident from the emergence of various SARS-CoV-2 spike antibody variants. Importantly, the effectiveness of current antivirals is waning due to the rapid emergence of new variants of concern, more recently the omicron variant. One means of avoiding the emergence of viral resistance is by using long asRNA to target SARS-CoV-2. Similar work has proven successful with HIV targeting by long asRNA. In this study, we describe a long asRNA targeting SARS-CoV-2 RNA-dependent RNA polymerase gene and the ability to deliver this RNA in extracellular vesicles (EVs) to repress virus expression. The observations presented in this study suggest that EV-delivered asRNAs are one means to targeting SARS-CoV-2 infection, which is both effective and broadly applicable as a means to control viral expression in the absence of mutation. This is the first demonstration of the use of engineered EVs to deliver long asRNA payloads for antiviral therapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".