CPC-containing oral rinses inactivate SARS-CoV-2 variants and are active in the presence of human saliva
Bibliographic record
Abstract
Introduction. The importance of human saliva in aerosol-based transmission of SARS-CoV-2 is now widely recognized. However, little is known about the efficacy of virucidal mouthwash formulations against emergent SARS-CoV-2 variants of concern and in the presence of saliva. Hypothesis. Mouthwashes containing virucidal actives will have similar inactivation effects against multiple SARS-CoV-2 variants of concern and will retain efficacy in the presence of human saliva. Aim. To examine in vitro efficacy of mouthwash formulations to inactivate SARS-CoV-2 variants. Methodology. Inactivation of SARS-CoV-2 variants by mouthwash formulations in the presence or absence of human saliva was assayed using ASTM International Standard E1052-20 methodology. Results. Appropriately formulated mouthwashes containing 0.07 % cetylpyridinium chloride but not 0.2 % chlorhexidine completely inactivated SARS-CoV-2 (USA-WA1/2020, Alpha, Beta, Gamma, Delta) up to the limit of detection in suspension assays. Tests using USA-WA1/2020 indicates that efficacy is maintained in the presence of human saliva. Conclusions. Together these data suggest cetylpyridinium chloride-based mouthwashes are effective at inactivating SARS-CoV-2 variants. This indicates potential to reduce viral load in the oral cavity and mitigate transmission via salivary aerosols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".