Development and validation of a multiplex bead based assay for the detection of antibodies directed against SARS-CoV-2 proteins
Bibliographic record
Abstract
Abstract Transplant recipients who develop COVID-19 may be at increased risk for morbidity and mortality. Determining antibody status against SARS-CoV-2 in candidates and recipients will be important to understand the epidemiology and clinical course of COVID-19 infection in this population. There are multiple antibody tests to detect antibodies to SARS-CoV-2, but their performance varies according to their platforms and the antigenic targets, making interpretation of the results challenging. Additionally, currently available serological tests do not exclude the possibility that positive responses are due to cross reactive antibodies to community coronaviruses. This study describes the development and validation of a high throughput multiplex bead based antibody detection assay with the capacity to identify, simultaneously, patient responses to five distinct SARS-CoV-2 proteins. The antibody response to these proteins are SARS-CoV-2 specific as antibodies against four community coronaviruses do not cross-react. Assay configuration is essentially identical to the single antigen bead assays used in the majority of histocompatibility laboratories around the world and could easily be implemented into routine screening of transplant candidates and recipients. This new assay provides a novel tool to interrogate the spectrum of immune responses to SAR-CoV-2 and is uniquely suitable for use in the transplant setting.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".