Polyphenols, Autophagy and Neurodegenerative Diseases: A Review
Bibliographic record
Abstract
Polyphenols are secondary metabolites from plant origin and are shown to possess a wide range of therapeutic benefits. They are also reported as regulators of autophagy, inflammation and neurodegeneration. The autophagy pathway is vital in degrading outdated organelles, proteins and other cellular wastes. The dysregulation of autophagy causes proteinopathies, mitochondrial dysfunction and neuroinflammation thereby contributing to neurodegeneration. Evidence reveals that polyphenols improve autophagy by clearing misfolded proteins in the neurons, suppress neuroinflammation and oxidative stress and also protect from neurodegeneration. This review is an attempt to summarize the mechanism of action of polyphenols in modulating autophagy and their involvement in pathways such as mTOR, AMPK, SIRT-1 and ERK. It is evident that polyphenols cause an increase in the levels of autophagic proteins such as beclin-1, microtubule-associated protein light chain (LC3 I and II), sirtuin 1 (SIRT1), etc. Although it is apparent that polyphenols regulate autophagy, the exact interaction of polyphenols with autophagy markers is not known. These data require further research and will be beneficial in supporting polyphenol supplementation as a potential alternative treatment for regulating autophagy in neurodegenerative diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".