Echinacea purpurea Extract Affects the Immune System, Global Metabolome, and Gut Microbiome in Wistar Rats
Bibliographic record
Abstract
Echinacea purpurea extract, a traditional herbal food additive with dual-purpose of medicine and edible material, has been widely used for the treatment and prevention of various infectious diseases, especially for children, old aged and immunocompromised patients. Although there were numerous reports suggested E. purpurea possessed immunostimulatory and antibacterial effects in vitro, the mechanisms underlying remained to be elucidated. This study employed immunologic factors analysis, GC-TOFMS based metabolomics and 16S-rRNA-sequencing microbiome profiling technologies to explore the effects of E. purpurea on young rats, a physiological insufficient immunity status, by compared with pidotimod treatment on young rats and adult animals. E. purpurea treatment significantly increased IL-2, decreased IL-6 and affect immunoglobulins in the spleen of young rats, indicating its promotion of cellular immunity. Both the immunologic factors and the global metabolome of E. purpurea treated young rats were close to the status of mature individuals. Results of 16S-rRNA-sequencing of ileum content together with co-metabolism metabolites demonstrated that E. purpurea changed gut microbiota structure characteristically as a reducing Firmicutes phylum, especially Lactobacillus, and a rising Actinobacteria phylum including Bifidobacterium. The results were concluded that E. purpurea could potentially promote the maturation of immune and metabolism of immature rats, and also affect gut flora structure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".