Antiproliferative activity of tea catechins associated with casein micelles, using HT29 colon cancer cells
Bibliographic record
Abstract
Numerous studies have shown that green tea polyphenols display anticancer activities in many organ sites by using different experimental models in rodents and in cultured cell lines in vitro. The present study tested the ability of casein micelles to deliver biologically active concentrations of polyphenols to HT-29 colon cancer cells. Epigallocatechin gallate (EGCG), the major catechin found in green tea, was used as the model molecule, as it has been shown to have antiproliferative activity on colon cancer cells. In the present work, we hypothesized that due to the binding of caseins with EGCG, casein micelles may be an ideal platform for the delivery of this bioactive molecule and that the binding would not affect the bioaccessibility of EGCG. The cytotoxicity and proliferation behavior of HT-29 colon cancer cells when exposed to free EGCG was compared with that of nanoencapsulated EGCG in casein micelles of skim milk. Epigallocatechin gallate-casein complexes were able to decrease the proliferation of HT-29 cancer cells, demonstrating that bioavailability may not be reduced by the nanoencapsulation. As casein micelles may act as protective carriers for EGCG in foods, it was concluded that nanoencapsulation of tea catechins in casein micelles may not diminish their antiproliferative activity on colon cancer cells compared with free tea catechins.
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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".