Cellulosic copper nanoparticles and a hydrogen peroxide–based disinfectant trigger rapid inactivation of pseudoviral particles expressing the Spike protein of SARS-CoV-2, SARS-CoV, and MERS-CoV
Bibliographic record
Abstract
Severe acute respiratory syndrome (SARS) is a viral respiratory infection caused by human coronaviruses that include SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV). Although their primary mode of transmission is through contaminated respiratory droplets from infected carriers, the deposition of expelled virus particles onto surfaces and fomites could contribute to viral transmission. Here, we use replication-deficient murine leukemia virus (MLV) pseudoviral particles expressing SARS-CoV-2, SARS-CoV, or MERS-CoV Spike (S) protein on their surface. These surrogates of native coronavirus counterparts serve as a model to analyze the S-mediated entry into target cells. Carboxymethyl cellulose (CMC) nanofibers that are combined with copper (Cu) exhibit strong antimicrobial properties. S-pseudovirions that are exposed to CMC-Cu nanoparticles (30 s) display a dramatic reduction in their ability to infect target Vero E6 cells, with ∼97% less infectivity as compared to untreated pseudovirions. In contrast, addition of the Cu chelator tetrathiomolybdate protects S-pseudovirions from CMC-Cu-mediated inactivation. When S-pseudovirions were treated with a hydrogen peroxide-based disinfectant (denoted SaberTM) used at 1:250 dilution, their infectivity was dramatically reduced by ∼98%. However, the combined use of SaberTM and CMC-Cu is the most effective approach to restrict infectivity of SARS-CoV-2-S, SARS-CoV-S, and MERS-CoV-S pseudovirions in Vero E6 cell assays. Together, these results show that cellulosic Cu nanoparticles enhance the effectiveness of diluted SaberTM sanitizer, setting up an improved strategy to lower the risk of surface- and fomite-mediated transmission of enveloped respiratory viruses.
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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 | 0.000 | 0.000 |
| 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 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".