Ultrasensitive assay for saliva-based SARS-CoV-2 antigen detection
Bibliographic record
Abstract
Abstract Widespread SARS-CoV-2 testing is highly valuable for identifying asymptomatic/pre-symptomatic individuals to slow community disease transmission. However, there remains a technological gap for highly reliable, easy, and quick SARS-CoV-2 diagnostic tests that are suitable for frequent mass testing. Compared to the conventional nasopharyngeal (NP) swab-based tests, saliva-based methods are attractive due to easier and safer sampling protocols. Despite its merits in rapid turn-around-time and high throughput compared to traditional PCR-based technologies, the widespread use of saliva-based SARS-CoV-2 rapid antigen tests is hindered by limited analytical sensitivity of current methods. Here, we report the first ultrasensitive, saliva-based SARS-CoV-2 antigen assay with an analytical sensitivity of < 0.32 pg/ml, corresponding to 4 viral RNA copies/µl, which is comparable to that of PCR-based tests. Using the novel electrochemiluminescence (ECL)-based S-PLEX immunoassay, we measured the SARS-CoV-2 nucleocapsid (N) antigen concentration in 105 saliva samples obtained from non-COVID-19 and COVID-19 patients. Our assay displayed absolute specificity and high sensitivity (90.2%), where it correctly identified samples with viral loads up to 35 CT cycles by saliva-based PCR. Paired NP swab-based PCR results were also obtained for 86 cases for comparison. Our assay showed high concordance with saliva-based and NP swab-based PCR in samples with negative (< 0.32 pg/ml) and strongly positive (> 2 pg/ml) N antigen concentrations. Our study unveiled the ultrasensitivity and specificity of the saliva-based S-PLEX assay, demonstrating its clinical value as a high throughput, complementary alternative to PCR-based techniques. The novel technique is especially valuable in cases where compliance to frequent swabbing may be problematic (e.g. schools, nursing homes, etc.).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".