Synergistic inhibition of Influenza replication cycle with Echinacea purpurea and Sambucus nigra
Bibliographic record
Abstract
Introduction Hemagglutinin and neuraminidase enzyme present therapeutic targets for the prevention and treatment of influenza. Echinacea purpurea extracts have been shown to inhibit hemagglutinin activity and viral infectivity [1]. In this study we examined the possibility that Sambucus nigra juice could additionally affect other parameters of influenza replication cycle and consequently enhance antiviral activity. Methods An ethanol extract (65% V/V) from freshly harvested E purpurea herb and roots ( EF ), the juice from Sambucus nigra (SAM) fructus and a fixed combination thereof ( Echinaforce® Hotdrink, EF+SAM ), were all assayed for interaction with viral hemagglutinin and neuraminidase activities, and inhibition of influenza virus (H1N1) replication in MDCK cells as described in [1]. Results Lower concentrations of EF+SAM (0.0005% EF/0,02%SAM) were required to inhibit 50% of H1N1 replication than the single extracts with 0.006% for EF and 0.41% for SAM indicating super-additive effects of the EF+SAM combination. We observed varying kinetics, with early blockade of infection by Echinacea, while Sambucus' activity developed over time: EF activity was attributed to hemagglutinin blockade, indicating an early stage interference with infection. In contrast, SAM had no measurable activity on receptor interaction (HA activity) but inhibited activity of neuraminidase by 40% and more strongly than EF at the same concentration of 0.04%. Conclusion E purpurea ethanol extract and juice of Sambucus nigra have complementary points of activity to inhibit the influenza replication cycle. In combination the extracts inhibit both hemagglutinin and neuraminidase activities, i.e. virus entry and release of progeny virions, demonstrating synergistic effects in inhibition of influenza virus. Reference: [1] Pleschka S, Stein M, Schoop R, Hudson JB. Virology Journal 2009;6:197.
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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) | 0.002 | 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".