Cross-validation of ELISA and a portable surface plasmon resonance instrument for IgG antibody serology with SARS-CoV-2 positive individuals
Bibliographic record
Abstract
We report on the development of surface plasmon resonance (SPR) sensors and matching ELISAs for the detection of nucleocapsid and spike antibodies specific against the novel coronavirus 2019 (SARS-CoV-2) in human serum, plasma and dried blood spots (DBS). When exposed to SARS-CoV-2 or a vaccine against SARS-CoV-2, the immune system responds by expressing antibodies at levels that can be detected and monitored to identify the fraction of the population potentially immunized against SARS-CoV-2 and support efforts to deploy a vaccine strategically. A SPR sensor coated with a peptide monolayer and functionalized with various sources of SARS-CoV-2 recombinant proteins expressed in different cell lines detected human anti-SARS-CoV-2 IgG antibodies in clinical samples. Nucleocapsid expressed in different cell lines did not significantly change the sensitivity of the assays, whereas the use of a CHO cell line to express spike ectodomain led to excellent performance. This bioassay was performed on a portable SPR instrument capable of measuring 4 biological samples within 30 minutes of sample/sensor contact and the chip could be regenerated at least 9 times. Multi-site validation was then performed with in-house and commercial ELISA, which revealed excellent cross-correlations with Pearson's coefficients exceeding 0.85 in all cases, for measurements in DBS and plasma. This strategy paves the way to point-of-care and rapid testing for antibodies in the context of viral infection and vaccine efficacy monitoring.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".