Nanostructures for prevention, diagnosis, and treatment of viral respiratory infections: from influenza virus to SARS-CoV-2 variants
Bibliographic record
Abstract
Viruses are a major cause of mortality and socio-economic downfall despite the plethora of biopharmaceuticals designed for their eradication. Conventional antiviral therapies are often ineffective. Live-attenuated vaccines can pose a safety risk due to the possibility of pathogen reversion, whereas inactivated viral vaccines and subunit vaccines do not generate robust and sustained immune responses. Recent studies have demonstrated the potential of strategies that combine nanotechnology concepts with the diagnosis, prevention, and treatment of viral infectious diseases. The present review provides a comprehensive introduction to the different strains of viruses involved in respiratory diseases and presents an overview of recent advances in the diagnosis and treatment of viral infections based on nanotechnology concepts and applications. Discussions in diagnostic/therapeutic nanotechnology-based approaches will be focused on H1N1 influenza, respiratory syncytial virus, human parainfluenza virus type 3 infections, as well as COVID-19 infections caused by the SARS-CoV-2 virus Delta variant and new emerging Omicron variant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".