The Effects of Resveratrol, Caffeine, β‐Carotene, and Epigallocatechin Gallate (EGCG) on Amyloid‐β25--35 Aggregation in Synthetic Brain Membranes
Bibliographic record
Abstract
Abstract Scope: Alzheimer's disease is a neurodegenerative condition marked by the formation and aggregation of amyloid‐β (Aβ) peptides. There exists, to this day, no cure or effective prevention for the disease; however, there is evidence that a healthy diet and certain food products can slow down first occurrence and progression. To investigate if food ingredients can interact with peptide aggregates, synthetic membranes that contained aggregates consisting of cross‐β sheets of the membrane active fragment A are prepared. Methods and results: The impact of resveratrol, found in grapes, caffeine, the main active ingredient in coffee, β‐carotene, found in orange fruits and vegetables, and epigallocatechin gallate (EGCG), a component of green tea, on the size and volume fraction of Aβ aggregates is studied using optical and fluorescence microscopy, X‐ray diffraction, UV–vis spectroscopy, and molecular dynamics simulations. All compounds are membrane active and spontaneously partitioned in the synthetic brain membranes. While resveratrol and caffeine lead to membrane thickening and reduced membrane fluidity, β‐carotene and EGCG preserve or increase fluidity. Conclusion: Resveratrol and caffeine do not reduce the volume fraction of peptide aggregates while β‐carotene significantly reduces plaque size. Interestingly, EGCG dissolves peptide aggregates and significantly decreases the corresponding cross‐β and β‐sheet signals.
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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) | 0.000 | 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".