Triple mode of action of the fresh plant tincture Echinaforce®
Bibliographic record
Abstract
More than 200 viruses cause influenza-like infections (ILI) presenting with nasal complaints, sore throat, cough and sometimes with fever [1]. Most ILI are treated with over-the-counter drugs to reduce the viral spread (antiviral) and the infection-based inflammation (anti-inflammatory), which finally elicits the cold symptoms [2]. Echinaforce® (ECF) is used for the prevention and the acute treatment of URI's and we wanted to elucidate how the efficacy could be explained. Using in-vitro test systems we identified a threefold action for the extract. Already at lowest concentrations ECF fully inhibited the replication of different cold viruses (influenza, respiratory syncytial (RSV) and herpes simlex virus) and inhibited – once the infection had established – the production of various inflammatory mediators (Interleukin IL-6, IL-8 or TNF-a). Moreover we could identify specific anti-bacterial effects against a variety of bacteria like Haemophilus influenzae , Streptococcus pyogenes and Legionella pneumophilia . These pathogens often are associated with secondary infections like pneumonia or bronchitis. Finally, ECF blocked the inflammatory reaction, caused by bacteria, as demonstrated by reduced levels of IL-6 or IL-8. In our experiments we showed that ECF exhibits multiple bioactivities, which could explain the effects as seen in clinical studies. The acute treatment with ECF further might deliver a positive effect to prevent secondary infections, often occurring at a later stage during viral infection. In conclusion, ECF represents an interesting option for the prevention and the treatment of URI, displaying multileveled activities in the management of upper respiratory tract infections. [ References: 1. Monto AS. Epidemiology of viral respiratory infections. Americal Journal of Medicine. 2002;112(6A):4–12. 2. Johnston SL. Problems and prospects of developing effective therapy for common cold viruses. Trends in Microbiology. 1997;5(2);58–63.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".