Sensitive Serology Measurements in the Saliva of Individuals with COVID-19 Symptoms Using a Multiplexed Immunoassay
Bibliographic record
Abstract
BACKGROUND: There are numerous benefits to performing salivary serology measurements for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen for coronavirus disease 2019 (COVID-19). Here, we used a sensitive multiplex serology assay to quantitate salivary IgG against 4 SARS-CoV-2 antigens: nucleocapsid, receptor-binding domain, spike, and N-terminal domain. METHODS: We used single samples from 90 individuals with COVID-19 diagnosis collected at 0 to 42 days postsymptom onset (PSO) and from 15 uninfected control subjects. The infected individuals were segmented in 4 groups (0-7 days, 8-14 days, 15-21 days, and >21 days) based on days PSO, and values were compared to controls. RESULTS: Compared to controls, infected individuals showed higher levels of antibodies against all antigens starting from 8 days PSO. When applying cut-offs with at least 93.3% specificity at every time interval segment, nucleocapsid protein serology had the best sensitivity at 0 to 7 days PSO (60% sensitivity [35.75% to 80.18%], ROC area under the curve [AUC] = 0.73, P = 0.034). Receptor-binding domain serology had the best sensitivity at 8 to 14 days PSO (83.33% sensitivity [66.44%-92.66%], ROC AUC = 0.90, P < 0.0001), and all assays except for N-terminal domain had 92% sensitivity (75.03%-98.58%) at >14 days PSO. CONCLUSIONS: This study shows that our multiplexed immunoassay can distinguish infected from uninfected individuals and reliably (93.3% specificity) detect seroconversion (in 60% of infected individuals) as early as the first week PSO, using easy-to-collect saliva samples.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".