Excessive autophagy contributes to neuron death in cerebral ischemia
Bibliographic record
Abstract
Aims To determine the extent to which autophagy contributes to neuronal death in cerebral hypoxia and ischemia. Methods We performed immunocytochemistry, western blot, cell viability assay, and electron microscopy to analyze autophagy activities in vitro and in vivo. Results In both primary cortical neurons and SH‐SY5Y cells exposed to oxygen and glucose deprivation (OGD) for 6 h (hours) and reperfusion (RP) for 24, 48 and 72 h respectively, an increase of autophagy was observed as determined by the increased ratio of LC3‐II to LC3‐I and Beclin‐1 (BECN1) expression. Using Fluoro‐Jade C and monodansylcadaverine (MDC) double‐staining, and electron microscopy we found the increment in autophagy after OGD/RP was accompanied by increased autophagic cell death, and this increased cell death was inhibited by the specific autophagy inhibitor, 3‐Methyladenine (3‐MA). The presence of large autolysosomes and numerous autophagosomes in cortical neurons were confirmed by electron microscopy. Autophagy activities were increased dramatically in the ischemic brains 3 to 7 days post‐injury from a rat model of neonatal cerebral hypoxia/ischemia as shown by increased punctate LC3 staining and Beclin‐1 expression. Conclusion Excessive activation of autophagy contributes to neuronal death in cerebral ischemia. This study was supported by CIHR, MHRC, MICH and Canadian Stroke Network.
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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) | 0.000 | 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 | 0.000 |
| 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".