Potential of Naturally-occurring Compounds for the Development of Dietary Supplements with Antiviral Activity
Bibliographic record
Abstract
Viral infections are a major concern for public health systems. The possibility of outbreaks and pandemics can be devastating at a global scale, as seen with the recent coronavirus disease (COVID-19) pandemic. Therefore, it is imperative to control the spread of viruses, especially highly-virulent strains, for which the administration of vaccines continues to be the best strategy; however, these may be not available for certain viruses, such as the human immunodeficiency virus (HIV) and herpes simplex virus, or they can quickly lose efficacy towards highly-mutable viruses, such as the influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In such cases, treatments that can attenuate the infection and hamper the multiplication of the virus need to be widely accessible. Drugs are available to treat several virus infections, although they may impose considerable side effects and drug resistance can occur from continuous administration. In this case, alternative treatments should be procured. Polyphenols, alkaloids, terpenoids, and other natural compounds have demonstrated antiviral activity, acting through multiple mechanisms, and showing inhibition of proteins and enzymes that are essential during the life cycle of viruses. This chapter examines recent findings on the antiviral effects of bioactive compounds found in nature, highlighting the characteristics that can make them potential ingredients for the development of antiviral dietary supplements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.013 | 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".