Development and Validation of a Multiplex, Bead-based Assay to Detect Antibodies Directed Against SARS-CoV-2 Proteins
Bibliographic record
Abstract
BACKGROUND: Transplant recipients who develop COVID-19 may be at increased risk for morbidity and mortality. Determining the status of antibodies against SARS-CoV-2 in both candidates and recipients will be important to understand the epidemiology and clinical course of COVID-19 in this population. While there are multiple tests to detect antibodies to SARS-CoV-2, their performance is variable. Tests vary according to their platforms and the antigenic targets which make interpretation of the results challenging. Furthermore, for some assays, sensitivity and specificity are less than optimal. Additionally, currently available serological tests do not exclude the possibility that positive responses are due to cross reactive antibodies to community coronaviruses rather than SARS-CoV-2. METHODS: This study describes the development and validation of a high-throughput multiplex antibody detection assay. RESULTS: The multiplex assay has the capacity to identify, simultaneously, patient responses to 5 SARS-CoV-2 proteins, namely, the full spike protein, 3 individual domains of the spike protein (S1, S2, and receptor binding domain), and the nucleocapsid protein. The antibody response to the above proteins are SARS-CoV-2-specific, as antibodies against 4 common coronaviruses do not cross-react. CONCLUSIONS: 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. Test 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".