Protective effects of diosmetin extracted from <i>Galium verum</i> L. on the thymus of U14-bearing mice
Bibliographic record
Abstract
Diosmetin (DGVL) extracted from the traditional Chinese herb Galium verum L. has been found to have anticancer activity. In this study, the effects of DGVL on the thymus of U14-bearing mice were investigated. Using flow cytometry, peripheral blood lymphocytes were characterized based on the expression of surface markers for T helper cells (CD4(+)) and T suppressor cells (CD8(+)). Serum levels of tumor necrosis factor α (TNF-α), interleukin-2 (IL-2), IL-10, and transforming growth factor β1 (TGF-β1) and a cell proliferation assay were determined with an enzyme-linked immunosorbent assay. The expression of Fas and Fas ligand (FasL) on the thymus was determined by Western blotting. Our results showed that DGVL inhibited tumor growth and significantly increased the thymus weight compared with the control. Also, DGVL elevated serum levels of IL-2 and significantly reduced levels of TNF-α, TGF-β1, and IL-10 in a dose-dependent manner. Histological study and terminal dUTP nick end labeling staining results showed that DGVL protected thymus tissue against the onslaught of tumor growth by inhibiting thymus lymphocyte apoptosis. The cell proliferation assay revealed that DGVL might promote more thymus lymphocytes towards proliferation. Furthermore, the ratio of CD4(+)/CD8(+) T lymphocytes was significantly increased from 0.69 to 2.29 by treatment with DGVL. Immunoblotting analyses revealed that the expression of Fas and FasL on the thymus was lower in mice in the DGVL treatment group than in the control mice. In conclusion, DGVL can inhibit tumor growth and protect tumor-induced apoptosis of the thymus, and the mechanism is closely associated with reduced cell death in the thymus and a Fas-FasL-dependent pathway.
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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) | 0.001 | 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".