Comparing methods collecting mucosal secretions and detecting SARS-CoV-2 spike IgA in three laboratories across three countries
Bibliographic record
Abstract
BACKGROUND: Mucosal IgA is key in preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Several mucosal vaccines are in development, and consistent methodologies assessing mucosal IgA are crucial for evaluation across clinical trials. METHODS: We compared SARS-CoV-2 ancestral spike-specific IgA and secretory IgA (SIgA) in nasal secretions and saliva from 20 adults enrolled at Danderyd Hospital, Stockholm, Sweden, and 23 adults enrolled at the Icahn School of Medicine at Mount Sinai, New York, USA. Nasal secretions were collected by Nasosorption® and nasal swabs, and saliva by passive drooling, Salivette®, and saliva swabs. Antibody levels were measured in all samples using an electrochemiluminescence assay (ECL) and two enzyme-linked immunosorbent assays (ELISAs). FINDINGS: Spike-specific IgA and SIgA levels measured by ECL correlated well with those measured by ELISA across nasal and saliva samples (range 0.42-0.94, p < 0.01), except for saliva collected by saliva swabs yielding lower IgA concentrations and weaker correlations (range - 0.21-0.27). Spike-specific IgA levels also correlated well across collection methods (range 0.7-0.9, p < 0.0001), with a weaker correlation between saliva collected by passive drooling and saliva swab (r = 0.55, p < 0.001). Although antibody levels correlated well between nasal secretions and saliva collected by passive drooling or Salivette® (range 0.64-0.86, p < 0.01), the overall levels were > 3-fold higher in nasal secretions compared to saliva (p < 0.01). INTERPRETATION: This multi-center study demonstrates an overall good comparability between spike-specific IgA and SIgA across assays and collection methods, except for saliva swabs. Our findings suggest that nasal secretions may be preferable due to higher spike-specific IgA levels compared to in saliva.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".