A Novel SARS-CoV-2-Derived Infectious Vector System
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19. The development of antiviral drugs for COVID-19 has been hampered by the requirement of a biosafety level 3 (BSL3) laboratory for experiments related to SARS-CoV-2, and by the lack of easy and precise methods for quantification of infection. Here, we developed a SARS-CoV-2 viral vector composed of all four SARS-CoV-2 structural proteins constitutively expressed in lentivirally transduced cells, combined with an RNA replicon deleted for SARS-CoV-2 structural protein genes S, M, and E, and expressing a luciferase–GFP fusion protein. We show that, after concentrating viral stocks by ultracentrifugation, the SARS-CoV-2 viral vector is able to infect two human cell lines expressing receptors ACE2 and TMPRSS2. Both luciferase activity and GFP fluorescence were detected, and transduction was remdesivir-sensitive. We also show that this vector is inhibited by three type I interferon (IFN-I) subtypes. Although improvements are needed to increase infectious titers, this vector system may prove useful for antiviral drug screening and SARS-CoV-2-related investigations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".