Epigallocatechin-3-Gallate Attenuates Impairment of Learning and Memory in Chronic Unpredictable Mild Stress-Treated Rats by Restoring Hippocampal Autophagic Flux
Bibliographic record
Abstract
Epigallocatechin gallate (EGCG) is a major polyphenol in green tea with beneficial effects on the impairment in learning and memory. Autophagy is a cellular process that protects neurons from stressful conditions. The present study was designed to investigate whether EGCG can rescue chronic unpredictable mild stress (CUMS)-induced cognitive impairment in rats and whether its protective effect involves improvement of autophagic flux. As expected, our results showed that CUMS significantly impaired memory performance and inhibited autophagic flux as indicated by elevated LC3-II and p62 protein levels. At the same time, we observed an increased neuronal loss and activated mammalian target of rapamycin (mTOR)/p70 ribosomal protein S6 kinase (p70S6k) signaling in the CA1 regions. Interestingly, chronic treatment with EGCG (25 mg/kg, i.p.) significantly improved those behavioral alterations, attenuated histopathological abnormalities in hippocampal CA1 regions, reduced amyloid beta1-42 (Aβ1-42) levels, and restored autophagic flux. However, blocking autophagic flux with chloroquine, an inhibitor of autophagic flux, reversed these effects of EGCG. Taken together, these findings suggest that the impaired autophagy in CA1 regions of CUMS rats may contribute to learning and memory impairment. Therefore, we conclude that EGCG attenuation of CUMS-induced learning and memory impairment may be through rescuing autophagic flux.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".