Broad antiviral effects of Echinacea purpurea against SARS-CoV-2 variants of concern and potential mechanism of action
Bibliographic record
Abstract
Introduction SARS-CoV-2 variants of concern (VOCs) represent an alarming threat as they may escape vaccination effectiveness. Broad-spectrum antivirals could complement and further enhance preventive benefits achieved through SARS-CoV-2 vaccination campaigns. Aim Testing the antiviral activity of Echinacea purpurea against VOCs and exploring underlying modes-of-action. Method A hydroethanolic extract of freshly harvested E. purpurea herb and roots (Echinaforce ® , EF extract) was tested to inhibit infection of VOCs B1.1.7 (alpha), B.1.351.1 (beta), P.1 (gamma), B1.617.2 (delta), AV.1 (Scottish) and B1.525 (eta). Molecular dynamics (MD) were used to study interaction of EF phytochemical markers with known pharmacological viral and host cell targets. Results EF broadly inhibited propagation of all tested SARS-CoV-2 VOCs at EC 50 ≤ 12.0 µg/ml. Treatment of epithelial cells with 20 µg/ml EF prevented sequential infection with SARS-CoV-2 (Hu-1). MD analyses showed for alkylamides, caftaric acid and feruoyl-tartaric constant binding affinity to spike proteins of all VOCs and to TMPRSS-2, a serine protease required for virus endocytosis. Conclusion EF extract exhibits virucidal activity against all tested SARS-CoV-2 VOCs and protects epithelial cells from infection. Publication History Article published online: 13 June 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".