Standardization of a flow cytometry SARS-CoV-2 serologic test
Bibliographic record
Abstract
Abstract The SARS-CoV-2 virus is the causing agent of the coronavirus disease 2019 (COVID-19) pandemic which is responsible for millions of deaths worldwide. The development of the humoral response to the virus has been the subject of intensive research and development. A flow cytometry-based assay using native full-length SARS-CoV-2 Spike protein expressed in 293T cells was recently proposed as a complementary seropositivity determination assay. The aim of our study was to further develop the flow cytometry assay for potential use as a confirmatory test and to standardize its parameters and results for reliable inter-laboratory use. We have optimized the protocol, established the Receiving Operating Characteristic (ROC) curve and tested reproducibility using pre-COVID plasma samples and convalescent, SARS-CoV-2 individual plasma samples. The flow-based assay was simplified and standardized by cultivating the 293T cells in suspension and expressing results in Mean Equivalent Soluble Fluorochrome (MESF) using an internal antibody positive control. The ROC curve was determined with an area under the curve (AUC) of 0.996 and the assay specificity and sensitivity were established at 100% and 97.7% respectively. Reproducibility was good as determined on multiple cytometers, on different days, and with data acquisition as far as 72h post-staining. The optimized and standardized assay could be used as a high throughput confirmation confirmatory assay in flow cytometry laboratories involved in serological testing.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".