Tea (Camellia sinensis) infusions ameliorate cancer in 4TI metastatic breast cancer model
Bibliographic record
Abstract
BACKGROUND: Tea (Camellia sinensis) infusions are widely consumed beverages with numerous health benefits. However, physiological and molecular responses mediating these activities are poorly understood. METHOD: cells/ml) were challenged in vitro with various concentrations of green, black and purple tea infusions to asseses their cytoxicity and associated differentially expressed genes in the cells. Inhibitory activity was tested by using serial dilutions of respective tea infusions in a 96 well ELISA plate. RESULTS: = 13.12 μg/ml. Further analysis of the 4TI cancer cell line treated with tea using 454 pyrosequencing generated 425,696 reads with an input mean length of 286.54. Trimmed sequences were imported on a CLC genomic workbench v7.03 and annotated on a reference mouse genome (Mus musculus strain C57BL/6 J). Results revealed a differential expression of apoptosis related genes in the transcriptome. Casp8, Casp9, Casp3, Casp6, Casp8AP2, Aifm1, Aifm2 and Apopt1 genes were significantly upregulated indicating the process of apoptosis was initiated and executed. CONCLUSION: These findings on caspases offer valuable information on the mechanism of tea as an anticancer agent and will contribute to further research in future novel treatments.
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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 | 0.001 |
| 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".