SPEEDS: A Portable Serological Testing Platform for Rapid Electrochemical Detection of SARS-CoV-2 Antibodies
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has resulted in a worldwide health crisis. Rapid diagnosis, new therapeutics and effective vaccines will all be required to stop the spread of COVID-19. Quantitative evaluation of serum antibody levels against the SARS-CoV-2 virus provides a means of monitoring a patient’s immune response to a natural viral infection or vaccination, as well as evidence of a prior infection. In this paper, a portable and low-cost electrochemical immunosensor is developed for the rapid and accurate quantification of SARS-CoV-2 serum antibodies. The immunosensor is capable of quantifying the concentrations of immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies against the SARS-CoV-2 spike protein in human serum. For IgG and IgM, it provides measurements in the range of 10.1 ng/mL − 60 µg/mL and 1.64 ng/mL − 50 µg/mL, respectively, and both antibodies can be assayed in 13 min. We also developed device stabilization and storage strategies to achieve stable performance of the immunosensor within 24-week storage at room temperature. We evaluated the performance of the immunosensor using COVID-19 patient serum samples collected at different time points after symptom onset. The rapid and sensitive detection of IgG and IgM provided by our immunosensor fulfills the need of rapid COVID-19 serology testing for both point-of-care diagnosis and population immunity screening.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".