Suppression of Human Coronavirus 229E Infection in Lung Fibroblast Cells via RNA Interference
Bibliographic record
Abstract
Despite extensive efforts to repurpose approved drugs, discover new small molecules, and develop vaccines, COVID-19 pandemic is still claiming victims around the world. The current arsenal of antiviral compounds did not perform well in the past viral infections (e.g., SARS), which casts a shadow of doubt for use against the new SARS-CoV-2. Vaccines should offer the ultimate protection; however, there is limited information about the longevity of the generated immunity and the protection against possible mutations. This study uses Human Coronavirus 229E as a model coronavirus to test the hypothesis that effective delivery of virus-specific siRNAs to infected cells will result in lower viral load and reduced cell death. Two different categories of nucleic acid delivery systems, Peptide/Lipid-Associated Nucleic Acids (PLANAs) and lipophilic polymers, were investigated for their toxicity in human lung fibroblast cells and their ability to deliver specific siRNAs targeting Spike and Envelope proteins in order to prevent cell death in infected cells. Selected siRNAs were effectively delivered to human lung fibroblast cells with negligible toxicity. Cell death due to viral infection was significantly reduced with individual and combinatorial silencing of selected viral proteins. The combinatorial silencing of Spike and Envelope proteins restored the cell viability completely and eliminated plaques in the investigated system. Our cell culture data indicate promising results for the RNAi based approach as an alternative antiviral treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".