The impact of a rub and rinse regimen on removal of human coronaviruses from contemporary contact lens materials
Bibliographic record
Abstract
PURPOSE: To assess the influence of contemporary contact lens (CL) materials on human coronavirus attachment and the influence of a rub and rinse step to remove these viruses. METHODS: The binding rates of HCoV-229E and HCoV-OC43 to eight soft CL materials and four rigid gas permeable materials were analyzed. The impact of a rub and rinse step to remove these viruses from all materials was examined. The efficacy of Biotrue (Bausch & Lomb), OPTI-FREE Puremoist (Alcon), Clear Care (Alcon) and cleadew (Ophtecs) to remove virus contamination from two representative soft lens materials (etafilcon A and lotrafilcon B) was also determined. RESULTS: infectious viral particles were recovered from each CL material. Although some materials were more prone to coronavirus adhesion, contamination of both viral types was reduced to below the limit of quantification (LQ) from all materials using a simple saline rinse step. Exposure to Clear Care and cleadew reduced the number of infectious viral particles from both etafilcon A and lotrafilcon B to below the LQ, while for Biotrue and OPTI-FREE Puremoist, infectious viral particles were reduced to below the LQ only when additional rub and rinse steps were included. CONCLUSION: Human coronavirus contamination can be easily removed from CL surfaces. Although CL care products containing hydrogen peroxide and povidone-iodine efficiently removed virus contamination from CL surfaces without the need for a rub and rinse step, a full regimen including rub and rinse steps is crucial when using CL care products based on non-oxidative systems.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".