Rapid and Low-Cost Detection and Quantification of SARS-CoV-2 Antibody Titers of ICU Patients with Respiratory Deterioration Using a Handheld Thermo-Photonic Device
Bibliographic record
Abstract
While research suggests that COVID-19 vaccines are effective in producing anti-SARS-CoV-2 antibodies that reduce the risk of COVID-19 and its potentially severe complications, how long these antibodies persist after the infection/vaccination is unknown. Longitudinal studies and rapid and scalable platforms are needed for large-scale sero-diagnosis and vaccine evaluation. In this study, we examine the efficacy of our recently-developed handheld thermo-photonic device for rapid and low-cost assessment of the adaptive immune response of COVID+ and COVID− patients admitted to the intensive care unit (ICU) at a local hospital due to respiratory deterioration. Antibody testing included detection and quantification of IgG and IgM via thermo-photonic sensing of a commercially available COVID-19 IgG/IgM rapid test as well as standard measurements with quantitative enzyme-linked immunosorbent assays (qELISA). The results demonstrate that the thermo-photonic reader in conjunction with COVID-19 IgG/IgM test cassettes can detect and quantify IgG levels in COVID-19 antibody assays within the clinically relevant range and with a high correlation to those obtained from qELISA. We also found that the IgG antibody is more reliable for detecting individuals with an adaptive immune response to SARS-CoV-2 compared to the IgM antibody. The developed reader offers a low-cost, portable, and scalable solution for accessing the antibody titer of individuals against SARS-CoV-2 and can be used in local hospital settings.
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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 | 0.001 |
| 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".