Zinc associated nanomaterials and their intervention in emerging respiratory viruses: Journey to the field of biomedicine and biomaterials
Bibliographic record
Abstract
Respiratory viruses represent a severe public health risk worldwide, and the research contribution to tackle the current pandemic caused by the SARS-CoV-2 is one of the main targets among the scientific community. In this regard, experts from different fields have gathered to confront this catastrophic pandemic. This review illustrates how nanotechnology intervention could be valuable in solving this difficult situation, and the state of the art of Zn-based nanostructures are discussed in detail. For virus detection, learning from the experience of other respiratory viruses such as influenza, the potential use of Zn nanomaterials as suitable sensing platforms to recognize the S1 spike protein in SARS-CoV-2 are shown. Furthermore, a discussion about the antiviral mechanisms reported for ZnO nanostructures is included, which can help develop surface disinfectants and protective coatings. At the same time, the properties of Zn-based materials as supplements for reducing viral activity and the recovery of infected patients are illustrated. Within the scope of noble adjuvants to improve the immune response, the ZnO NPs properties as immunomodulators are explained, and potential prototypes of nanoengineered particles with metallic cations (like Zn2+) are suggested. Therefore, using Zn-associated nanomaterials from detection to disinfection, supplementation, and immunomodulation opens a wide area of opportunities to combat these emerging respiratory viruses. Finally, the attractive properties of these nanomaterials can be extrapolated to new clinical challenges.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".