Protective effects of a green tea polyphenol, epigallocatechin-3-gallate, against sevoflurane-induced neuronal apoptosis involve regulation of CREB/BDNF/TrkB and PI3K/Akt/mTOR signalling pathways in neonatal mice
Bibliographic record
Abstract
Epigallocatechin-3-gallate (EGCG), a polyphenol in green tea, is an effective antioxidant and possesses neuroprotective effects. Brain-derived neurotrophic factor (BDNF) and cyclic AMP response element-binding protein (CREB) are crucial for neurogenesis and synaptic plasticity. In this study, we aimed to assess the protective effects of EGCG against sevoflurane-induced neurotoxicity in neonatal mice. Distinct groups of C57BL/6 mice were given EGCG (25, 50, or 75 mg/kg body weight) from postnatal day 3 (P3) to P21 and were subjected to sevoflurane (3%; 6 h) exposure on P7. EGCG significantly inhibited sevoflurane-induced neuroapoptosis as determined by Fluoro-Jade B staining and terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL). Increased levels of cleaved caspase-3, downregulated Bad and Bax, and significantly enhanced Bcl-2, Bcl-xL, xIAP, c-IAP-1, and survivin expression were observed. EGCG induced activation of the PI3K/Akt pathway as evidenced by increased Akt, phospho-Akt, GSK-3β, phospho-GSK-3β, and mTORc1 levels. Sevoflurane-mediated downregulation of cAMP/CREB and BDNF/TrkB signalling was inhibited by EGCG. Reverse transcription PCR analysis revealed enhanced BDNF and TrkB mRNA levels upon EGCG administration. Improved performance of mice in Morris water maze tests suggested enhanced learning and memory. The study indicates that EGCG was able to effectively inhibit sevoflurane-induced neurodegeneration and improve learning and memory retention of mice via activation of CREB/BDNF/TrkB-PI3K/Akt signalling.
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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) | 0.001 | 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 | 0.001 |
| 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".