Rapid assessment of SARS-CoV-2–evolved variants using virus-like particles
Bibliographic record
Abstract
A tool to probe SARS-CoV-2 biology To develop therapies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and emerging variants, it is important to understand the viral biology and the effect of mutations. However, this is challenging because live virus can only be studied in a few laboratories that meet stringent safety standards. Syed et al . describe a virus-like particle (VLP) that comprises the four SARS-CoV-2 structural proteins, but instead of packaging viral RNA, it packages messenger RNA (mRNA) that expresses a reporter protein (see the Perspective by Johnson and Menachery). The amount of reporter expressed in receiver cells depends on the efficiency of packaging and assembly in the producer cells and the efficiency of entry into receiver cells. Mutations in the nucleocapsid protein that are found in more transmissible variants increase mRNA packaging and expression. The VLPs provide a platform for studying the effect of mutations in the structural proteins and for screening therapeutics. —VV
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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 | 0.001 | 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 | 0.001 | 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".