Non-Invasive Detection of Viral Antibodies Using Oral Flocked Swabs
Bibliographic record
Abstract
Abstract Salivary antibodies are useful in surveillance and vaccination studies. However, low antibody levels and degradation by endonucleases are problematic. Oral flocked swabs are a potential non-invasive alternative to blood for detecting viral antibodies. Serum and saliva collected from 50 healthy volunteers were stored at −80°C; dried swabs at room temperature. Seroprevalence for Cytomegalovirus (CMV), Varicella-Zoster virus (VZV), Epstein-Barr virus (EBV), Measles and Mumps IgG antibodies were determined using commercial ELISAs and processed on an automated platform. For each antibody, swabs correlated well with saliva. For CMV IgG, VZV IgG, and EBV EBNA-1 IgG and VCA IgG, the swab sensitivities compared to serum were 95.8%, 96%, 92.1% and 95.5% respectively. For Measles IgG, swab sensitivity was 84.5%. Mumps IgG displayed poor sensitivity for oral swabs (60.5%) and saliva (68.2%). Specificities for IgG antibodies were 100% for CMV, EBV and Mumps. Specificities for VZV and Measles could not be determined due to seropositive volunteers. As oral flocked swabs correlate well with serum, are easy to self-collect and stable at room temperature further research is warranted. Highlights Oral flocked swabs are an easy, self-collection method for measuring viral antibodies. Viral IgG is stable on dried oral flocked swabs for at least two years. Oral swabs are highly sensitive for CMV, VZV, and EBV IgG. Oral swabs are potentially useful for surveillance and clinical microbiology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".