Neuroprotective effects of green and black teas and their catechin gallate esters against β‐amyloid‐induced toxicity
Bibliographic record
Abstract
Teas represent a large family of plants containing high amounts of polyphenols that may confer health benefits in various diseases. Recently, it has been hypothesized that tea consumption may also reduce the risk of age-related neurodegenerative pathologies. Considering the deleterious role of beta-amyloid (Abeta) in the aetiology of Alzheimer's disease (AD), we investigated green and black tea extracts and flavan-3-ols (present as monomers and dimers in green and black forms, respectively) against toxicity induced by Abeta-derived peptides using primary cultures of rat hippocampal cells as model. Both green and black tea extracts (5-25 microg/mL) displayed neuroprotective action against Abeta toxicity. These effects were shared by gallic acid (1-20 microm), epicatechin gallate (ECG; 1-20 microM) and epigallocatechin gallate (EGCG; 1-10 microM), the former being the most potent flavan-3-ol. In contrast, epicatechin and epigallocatechin were ineffective in the same range of concentrations. Moreover, only tea flavan-3-ol gallate esters (i.e. ECG, EGCG) and gallic acid inhibited apoptotic events induced by Abeta(25-35). Interestingly, EGCG and gallic acid inhibited Abeta aggregation and/or the formation of Abeta-derived diffusible neurotoxin ligands. Taken together, these results indicate that the catechin gallates (through the galloyl moiety) contribute to the neuroprotective effects of both green and black teas. Moreover, the protective effect of EGCG is likely to be associated, at least in part, with its inhibitory action on Abeta fibrils/oligomers formation. These data also support the hypothesis that not only green but also black teas may reduce age-related neurodegenerative diseases, such as 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) | 0.000 | 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".