Protective effects of epigallocatechin gallate (EGCG) derivatives on azoxymethane-induced colonic carcinogenesis in mice
Bibliographic record
Abstract
Epigallocatechin gallate (EGCG), the major polyphenol in green tea and a functional food ingredient/nutraceutical with health-promoting properties, was structurally modified by esterification with butyric and docosahexaenoic (DHA) acid in order to improve its lipophilicity and hence bioefficacy in vivo . The lipophilic derivatives of EGCG so-prepared were evaluated for their anticancer activity against azoxymethane (AOM)-induced colon carcinogenesis in mice. Formation of colonic aberrant crypt foci (ACF) was monitored as the biomarker of colorectal cancer (CRC). It was found that oral administration of EGCG derivatives led to reduced size of ACF in the mouse colon. EGCG–DHA esters were more effective than EGCG-butyrate in inhibiting the formation of ACF. The total number of large colonic ACF was remarkably decreased by treatment with EGCG derivatives, especially by the EGCG–DHA esters, which showed a 100% inhibition of large ACF formation. Two tumor-promoting enzymes, iNOS and COX-2 were also inhibited by EGCG derivatives to various extents at the expression level. The results suggest that the lipophilic ester derivatives of EGCG are effective in inhibiting colon carcinogenesis and may be good candidates for colon cancer prevention/treatment.
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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) | 0.001 | 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".