Two Detailed Plaque Assay Protocols for the Quantification of Infectious SARS‐CoV‐2
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) has been identified as the causal agent of COronaVIrus Disease‐19 (COVID‐19), an atypical pneumonia‐like syndrome that emerged in December 2019. While SARS‐CoV‐2 titers can be measured by detection of viral nucleic acid, this method is unable to quantitate infectious virions. Measurement of infectious SARS‐CoV‐2 can be achieved by tissue culture infectious dose−50 (TCID 50 ), which detects the presence or absence of cytopathic effect in cells infected with serial dilutions of a virus specimen. However, this method only provides a qualitative infectious virus titer. Plaque assays are a quantitative method of measuring infectious SARS‐CoV‐2 by quantifying the plaques formed in cell culture upon infection with serial dilutions of a virus specimen. As such, plaque assays remain the gold standard in quantifying concentrations of replication‐competent lytic virions. Here, we describe two detailed plaque assay protocols to quantify infectious SARS‐CoV‐2 using different overlay and staining methods. Both methods have several advantages and disadvantages, which can be considered when choosing the procedure best suited for each laboratory. These assays can be used for several research purposes, including titration of virus stocks produced from infected cell supernatant and, with further optimization, quantification of SARS‐CoV‐2 in specimens collected from infected animals. © 2019 The Authors. Basic Protocol : SARS‐CoV‐2 plaque assay using a solid double overlay method Alternate Protocol : SARS‐CoV‐2 plaque assay using a liquid overlay and fixation‐staining method
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.038 |
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".