Revolutionizing antiviral therapy: harnessing nanotechnology to unlock the power of phytoconstituents
Bibliographic record
Abstract
INTRODUCTION: Viral diseases such as influenza, severe acute respiratory syndrome (SARS) caused due to coronaviruses (CoVs), Ebola, and acquired immunodeficiency syndrome (AIDS) caused due to human immunodeficiency virus (HIV) are still some of the major global causes of morbidity and mortality. Traditional antiviral therapies face limitations because of resistance development and toxicity. As a result, plant-derived medicines are gaining more attention for their therapeutic potential, owing to their lower toxicity and reduced likelihood of resistance development. AREAS COVERED: This review critically examines the antiviral properties of phytoconstituents like coumarins, steroids, and polysaccharides against various viruses. It discusses their integration with nanotechnology delivery systems to overcome bioavailability issues and highlights the need for translational studies to corroborate in vitro results. EXPERT OPINION: data, clinical research, standardization efforts, and regulatory clarity are needed. This review may serve as a foundational resource for researchers aiming to develop innovative antiviral therapies based on natural compounds and nanotechnology-based delivery systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".