A systematic review on the effects of Echinacea supplementation on cytokine levels: Is there a role in COVID-19?
Bibliographic record
Abstract
COVID-19 is the respiratory illness caused by the novel coronavirus, SARS-CoV-2. Cytokine storm appears to be a factor in COVID-19 mortality. Echinacea species have been used historically for immune modulation. A previous rapid review suggested that Echinacea supplementation may decrease the levels of pro-inflammatory cytokines involved in cytokine storm. The objective of the present systematic review was to identify all research that has assessed changes in levels of cytokines relevant to cytokine storm in response to administration of Echinacea supplementation. The following databases were searched: Medline (Ovid), AMED (Ovid), CINAHL (EBSCO), EMBASE (Ovid). Title and abstract screening, full text screening, and data extraction were completed in duplicate using a piloted extraction template. Risk of bias assessment was completed. Qualitative analysis was used to assess for trends in cytokine level changes. The search identified 279 unique publications. After full text screening, 105 studies met criteria for inclusion including 13 human studies, 24 animal studies, and 71 in vitro or ex vivo studies. The data suggest that Echinacea supplementation may be associated with a decrease in the pro-inflammatory cytokines IL-6, IL-8, and TNF, as well as an increase in the anti-inflammatory cytokine IL-10. The risk of bias in the included studies was generally high. While there is currently no substantive research on the therapeutic effects of Echinacea in the management of either cytokine storm or COVID-19, the present evidence related to the herb's impact on cytokine levels suggests that further research may be warranted in the form of a clinical trial involving patients with COVID-19.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".