Detection of SARS-CoV-2 Neutralizing Antibodies using High-Throughput Fluorescent Imaging of Pseudovirus Infection
Bibliographic record
Abstract
As the COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve, it has become evident that the presence of neutralizing antibodies against the virus may provide protection against future infection. Thus, as the creation and translation of effective COVID-19 vaccines continues at an unprecedented speed, the development of fast and effective methods to measure neutralizing antibodies against SARS-CoV-2 will become increasingly important to determine long-term protection against infection for both previously infected and immunized individuals. This paper describes a high-throughput protocol using vesicular stomatitis virus (VSV) pseudotyped with the SARS-CoV-2 spike protein to measure the presence of neutralizing antibodies in convalescent serum from patients who have recently recovered from COVID-19. The use of a replicating pseudotyped virus eliminates the necessity for a containment level 3 facility required for SARS-CoV-2 handling, making this protocol accessible to virtually any containment level 2 lab. The use of a 96-well format allows for many samples to be run at the same time with a short turnaround time of 24 h.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".