Evaluation of silver nanoparticles for the prevention of SARS-CoV-2 infection in health workers: <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Abstract SARS-CoV-2 infection in hospital areas is of a particular concern, since the close interaction between health care personnel and patients diagnosed with COVID-19, which allows virus to be easily spread between them and subsequently to their families and communities. Preventing SARS-CoV-2 infection among healthcare personnel is essential to reduce the frequency of infections and outbreaks during the pandemic considering that they work in high-risk areas. In this research, silver nanoparticles (AgNPs) were tested in vitro and shown to have an inhibitory effect on SARS-CoV-2 infection in cultured cells. Subsequently, we assess the effects of mouthwash and nose rinse with ARGOVIT ® silver nanoparticles (AgNPs), in the prevention of SARS-CoV-2 contagion in health workers consider as high-risk group of acquiring the infection in the General Tijuana Hospital, Mexico, a hospital for the exclusive recruitment of patients diagnosed with COVID-19. We present a prospective randomized study of 231 participants that was carried out for 9 weeks (during the declaration of a pandemic). The “experimental” group was instructed to do mouthwash and nose rinse with the AgNPs solution; the “control” group was instructed to do mouthwashes and nose rinse in a conventional way. The incidence of SARS-CoV-2 infection was significantly lower in the “experimental” group (two participants of 114, 1.8%) compared to the “control” group (thirty-three participants of 117, 28.2%), with a 84.8% efficiency. We conclude that the mouth and nasal rinse with AgNPs helps in the prevention of SARS-CoV-2 infection in health personnel who are exposed to patients diagnosed with COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".