RETRACTED: Silencing SARS‐CoV Spike protein expression in cultured cells by RNA interference
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
The severe acute respiratory syndrome (SARS) has been one of the most epidemic diseases threatening human health all over the world. Based on clinical studies, SARS-CoV (the SARS-associated coronavirus), a novel coronavirus, is reported as the pathogen responsible for the disease. To date, no effective and specific therapeutic method can be used to treat patients suffering from SARS-CoV infection. RNA interference (RNAi) is a process by which the introduced small interfering RNA (siRNA) could cause the degradation of mRNA with identical sequence specificity. The RNAi methodology has been used as a tool to silence genes in cultured cells and in animals. Recently, this technique was employed in anti-virus infections in human immunodeficiency virus and hepatitis C/B virus. In this study, RNAi technology has been applied to explore the possibility for prevention of SARS-CoV infection. We constructed specific siRNAs targeting the S gene in SARS-CoV. We demonstrated that the siRNAs could effectively and specifically inhibit gene expression of Spike protein in SARS-CoV-infected cells. Our study provided evidence that RNAi could be a tool for inhibition of SARS-CoV.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".