Bibliographic record
Abstract
In the last couple of decades, we have experienced increased use of nutraceuticals worldwide with a demand for organic foods, which has been elevated to an extent probably unmatched with other periods of our civilization. One of the nutraceuticals that gained attention is epigallocatechin gallate (EGCG), a polyphenol in green tea. It has been suggested that diseases of the central nervous system can benefit from consuming some antioxidants, despite current results showing little evidence for their use in preventing and treating these diseases. ECGC may be beneficial in delaying the neurodegeneration of the substantia nigra regardless of the origin of Parkinson's disease (PD). This review covers the effect of EGCG on vitro and animal models of PD, the potential mechanisms of neuroprotection involved and summaries recent clinical trials in human PD. This review also aims to provide an investigative analysis of the current knowledge in this field and to identify putative crucial issues. Environmental factors such as dietary habits, drug use and social interaction are all factors that influence the evolution of neurodegenerative diseases. Therefore, the use of nutraceuticals requires further investigation.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.006 | 0.003 |
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".