Using nanomaterials to address SARS-CoV-2 variants through development of vaccines and therapeutics
Bibliographic record
Abstract
Nanomaterials have played a significant role in effectively combating the global SARS-CoV-2 pandemic that began in December 2019 through the development of vaccines as well as antiviral therapies. These versatile, tunable materials can interact and deliver a broad range of biologically relevant molecules for preventing COVID-19 infection, generating immunity against COVID-19, and treating infected patients. Application of these nanomaterials and nanotechnologies can further be investigated in conjunction with disease models of COVID-19 and this holds immense potential for accelerating vaccine or therapeutic process development further encouraging the elimination of animal model use during preclinical stages. This review examines the existing literature on COVID-19 related nanomaterial applications, including perspective on nanotechnology-based vaccines and therapeutics, and discusses how these tools can be adapted to address new SARS-CoV-2 variants of concern. We also analyze the limitations of current nanomaterial approaches to managing COVID-19 and its variants alongside the challenges posed when implementing this technology. We end by providing avenues for future developments specific to disease modelling in this ever-evolving field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".