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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".