Optimisation of Echinacea purpurea extraction and processing to yield high potency antiviral activity
Bibliographic record
Abstract
Introduction Antiviral and anti-inflammatory activities are important for the prevention and treatment of respiratory tract infections. These activities can be found in the herbal medicinal plant Echinacea purpurea (EP). However different commercial preparations of EP vary greatly in chemical composition and the manufacturing procedures used, and consequently differ significantly in their antiviral potencies. As a result, there is no standard procedure for the preparation of consistent high potency extracts. In this study we evaluated different types of EP extracts for relative antiviral activities. Antiviral activity Ethanol is an ideal solvent to isolate the antiviral principles from E. purpurea (1). We separately investigated ethanol extracts (65% V/V) of roots, herb (aerial part without flowers), flower heads and the petals. Minimal inhibitory concentrations (MIC) were measured quantitatively by means of standard plaque assays with influenza virus type A, H3N2 (1). No antiviral activity was found in the roots and the petals (MIC > 1 mg/ml); but extracts of the herb and of the flower heads were active (MIC < 26.5 µg/ml). In addition there was a substantial difference between the freshly processed herb providing tinctures with MIC < 2.3 µg/ml in comparison to dried herb tincture with MIC equal to 16.8 µg/ml. Anti-inflammatory activity Previous studies have demonstrated the anti-inflammatory potential of alkylamides that are enriched in roots of Echinacea species. Thus a combination of herb and root tinctures, prepared from freshly harvested plants, would provide the full spectrum of pharmacological activities for successful cold management. Conclusion Ethanol extracts from E. purpurea herb and roots have distinct antiviral and anti-inflammatory potential. In order to obtain optimal benefits of EP, it is desirable to use both herb and root components, derived from freshly harvested plants. References: [1] Vimalanathan S, et al. (2005). Pharm. Biol 12;43(9):740 – 745.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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".