Euonymus alatus (Thunb.) Siebold leaf extract enhanced immunostimulatory effects in a cyclophosphamide-induced immunosuppressed rat model
Bibliographic record
Abstract
Background: (Thunb.) Siebold (EA) is a medicinal plant used in some Asian countries to treat various diseases, including cancer, hyperglycemia, diabetes, urticaria, dysmenorrhea, and arthritis. Owing to the wide range of pharmacological applications of EA, various roles of EA are being studied. Objective: We evaluated the immune-enhancing effect of EA treatment in a cyclophosphamide (Cy)-induced immunosuppressed rat model. Design: studies, analysis of weekly body weight, spleen weight, immune cell count, cytokine levels, and spleen histological findings was performed following EA administration in Cy-induced immunocompromised rats. Results: EA significantly increased cell viability and phospho-nuclear factor-kappa B and phospho-extracellular signal-regulated kinase protein levels in the macrophages. EA significantly increased NK cell activity in splenocytes compared with the control group. In Cy-induced immunosuppressed rats, EA administration increased spleen tissue weight and the contents of leukocytes, lymphocytes, granulocytes, intermediate cells, and plasma cytokines (tumor necrosis factor-α and interferon-γ). In addition, improvement in the damaged spleen tissue was observed. Conclusions: These findings confirm that EA exerts an immune-enhancing effect, thereby suggesting its potential as an immunostimulatory agent or functional food.
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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 | 0.000 | 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) | 0.001 | 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".