Green Tea Epigallocatechin-3-Gallate (EGCG) Modulates Amyloid Precursor Protein Cleavage and Reduces Cerebral Amyloidosis in Alzheimer Transgenic Mice
Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder pathologically characterized by deposition of beta-amyloid (Abeta) peptides as senile plaques in the brain. Recent studies suggest that green tea flavonoids may be used for the prevention and treatment of a variety of neurodegenerative diseases. Here, we report that (-)-epigallocatechin-3-gallate (EGCG), the main polyphenolic constituent of green tea, reduces Abeta generation in both murine neuron-like cells (N2a) transfected with the human "Swedish" mutant amyloid precursor protein (APP) and in primary neurons derived from Swedish mutant APP-overexpressing mice (Tg APPsw line 2576). In concert with these observations, we find that EGCG markedly promotes cleavage of the alpha-C-terminal fragment of APP and elevates the N-terminal APP cleavage product, soluble APP-alpha. These cleavage events are associated with elevated alpha-secretase activity and enhanced hydrolysis of tumor necrosis factor alpha-converting enzyme, a primary candidate alpha-secretase. As a validation of these findings in vivo, we treated Tg APPsw transgenic mice overproducing Abeta with EGCG and found decreased Abeta levels and plaques associated with promotion of the nonamyloidogenic alpha-secretase proteolytic pathway. These data raise the possibility that EGCG dietary supplementation may provide effective prophylaxis for AD.
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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".