Capillary electrophoresis study of interaction between omega-3 fatty acids and epigallocatechin gallate
Bibliographic record
Abstract
Cancer, a global disease affecting millions of people, is characterized by uncontrolled cell division due to mutations within the cell. The resulting cell mass continues to rapidly grow, resulting in tumours. Epigallocatechin gallate is a polyphenolic catechin found in green tea leaves that has been shown to have antioxidant and anti-inflammatory properties. Recently, it has also been shown to have anti-cancerous effects on various breast cancer cell lines. The effects include inhibiting cancer cell line growth, down-regulating estrogen receptor function, inducing apoptosis, inhibiting tumour promotion, and exhibiting antioxidant properties. This shows that epigallocatechin gallate is a potential cancer therapeutic agent. Although these are promising in vitro results, epigallocatechin gallate does not readily cross the cellular phospholipid bilayer in vivo. It is hypothesized that epigallocatechin gallate could be carried through the plasma membrane and into the cell when attached to a lipid molecule. The molecule that is currently being tested as a carrier molecule is the omega-3 fatty acid docosahexaenoic acid. The analytical technique affinity capillary electrophoresis has been used to determine that epigallocatechin gallate and docosahexaenoic acid have a reasonably strong interaction of 4.0 (± 1.2) x 104 at physiological pH.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".