Ellagic acid protects against neuron damage in ischemic stroke through regulating the ratio of Bcl-2/Bax expression
Bibliographic record
Abstract
An oxygen-glucose deprivation and reoxygenation model in primary cultured rat cortical neurons was developed for this study to investigate the effects of ellagic acid (EA), a low-molecular-weight polyphenol, on neuron cells and their function, and to evaluate whether EA can be safely utilized by humans as a functional food or therapeutic agent. Administration of EA significantly decreased the volume of cerebrum infarction and the neurological deficit scores of the rats; EA treatment also increased the number of Bcl-2-positive cells and the ratio of Bcl-2-positive to Bax-positive neurons in the semidarkness zone near the brain ischemic focus in the photothrombotic cerebral ischemia model. Treatment of EA resulted in increased neuron viability, cell nuclear integrity, and the ratio of Bcl-2/Bax expression in the primary cultured neuron model; EA treatment also lead to a decrease in the number of apoptotic cells. Our results therefore suggest a specific mechanism for the beneficial effects of EA, providing new insights into how it provides neuroprotection. To the best of our knowledge, these results represent new insights on the mechanisms of the brain cell protective activity of EA. Thus, EA may be used in functional foods or medicines to help treat nerve dysfunction, neurodegenerative disease, and aging.
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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 | 0.001 | 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".