SPR and SERS sensors for serological assays of COVID-19 antibodies
Bibliographic record
Abstract
The need to develop clinical tests and rapid sensors for SARS-CoV-2 became evident early in the pandemic to monitor active infections and evaluate seroprevalence. While nucleic acid and antigen tests serve to detect active infections, antibody tests are an essential tool later in the pandemic to monitor past infections and to provide an indication of the immune response of an individual to COVID-19 and to vaccination. To address the need for antibody tests, we have developed surface plasmon resonance (SPR) sensors to detect antibodies expressed towards the nucleocapsid (N) protein and to the spike (S) protein and its receptor binding domain (RBD). We then applied the SPR sensors to determine the maturation of the affinity of the antibodies in the 24-week period post infection, and following vaccination. We developed an in vitro surrogate neutralization assay where the spike protein (the native and a few variants) was immobilized to the SPR sensors to evaluate if convalescent sera inhibited the interaction of spike with ACE-2. SERS assays were also developed to screen individuals that were infected from SARS-CoV-2 naïve individuals and for the multiplexed detection of antibody isotype prevalence at different times post infection.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".