A gold nanoparticle-protein G electrochemical affinity biosensor for the detection of SARS-CoV-2 antibodies: a surface modification approach
Bibliographic record
Abstract
As COVID-19 waves continue to spread worldwide, demand for a portable, inexpensive and convenient biosensor to determine community immune/infection status is increasing. Here we describe an impedance-based affinity biosensor using Interdigitated Electrode (IDE) arrays to detect antibodies to SARS-CoV-2 in serum. We created the biosensor by functionalizing the IDEs' surface with abaculaovirus-expressed and purified Spike (S) protein to bind anti-SARS CoV-2antibodies. Gold nanoparticles (GNP) fused to protein G were used to probe for bound antibodies. An ELISA assay using horseradish peroxidase-protein G to probe for bound IgG confirmed that the purified S protein bound a commercial source of anti-SARS-CoV-2 antibodies specifically and bound anti-SARS-CoV-2 antibodies in COVID-19 positive serum. Then we demonstrated that our biosensor could detect anti-SARS-CoV-2 antibodies with 72% sensitivity in 2 h. Using GNP-protein G, the affinity biosensor had increased impedance changes with COVID-19positive serum and minimal or decreased impedance changes with negative serum. This demonstrated that our biosensor could discriminate between COVID-19 positive and negative sera, which were further improved using poly(vinyl alcohol)as a blocking agent.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".