Role of Nanoparticles in COVID-19 Management
Bibliographic record
Abstract
The COVID-19 epidemic has globally influenced every significant facet of our societies.SARSCov-2 can withstand severe environmental conditions for up to 72 hours, which may be a factor in the virus's quick dissemination.As a result, efficient containment measures like sanitization, therapy, and vaccination are essential.An alternative to stop the COVID-19 virus from spreading is nanotechnology, especially in high-risk settings like public spaces and healthcare facilities.Nanoparticles can be obtained from metals as well as from plants.The phytochemical metabolites embodying extracts function as reducing agents to form nanoparticles, and such plant-based nanoparticles have diverse applications in nanomedicines.Regardless of the biological makeup, physiology, or drug-resistant characteristics of various diseases, including viruses, nanotechnology-based solutions effectively block them.Although there are different licensed nanotechnology-based antiviral medications, this chapter emphasizes various nanoparticles and their antiviral role against SARS-Cov-2 (COVID-19).Nanoparticles exhibit antimicrobial properties to limit the bacteria and fungi that might contaminate healthcare-related facilities.Therefore, nanoparticles can eliminate the virus and lower the risk of secondary microbial infections in COVID-19 patients.And lastly, affordable, simpleto-synthesize antiviral nanomaterials may lessen COVID-19's impact on harsh environments and impoverished nations.This chapter is about the antiviral activity of nanoparticles with special emphasis on COVID-19.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".