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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".