2-Methoxyestradiol Attenuates Autophagy Activation After Global Ischemia
Bibliographic record
Abstract
BACKGROUND: Hypoxia inducible factor 1 (HIF-1) is a key transcriptional factor activated during cerebral ischemia, which regulates a great number of downstream genes, including those associated with cell death. In the present study, we aimed to test the hypothesis that post-ischemic HIF-1α up-regulation might promote autophagy activation; thereby, HIF-1α inhibitor 2ME2 might prevent neurons from ischemic injury through inhibiting autophagy. METHODS: Global ischemia was induced using the four-vessel occlusion model (4-VO) in Sprague-Dawley rats (male, 250-280g). 2-Methoxyestradiol (2ME2, 5mg/kg, i.p.) was administrated to down-regulate HIF-1α expression. Post-ischemic beclin-1 and LC3 protein expression was determined at different time points through Western blot assay. Neuronal injury was determined by cresyl violet staining and TUNEL staining in coronal histological sections. RESULTS: The expression of beclin-1 and the ratio of LC3-II/LC3-I increased significantly at 12 and 24 h after ischemia. 2ME2 could remarkably inhibit the up-regulation of beclin-1 and the increase of LC3-II/LC3-I ratio during reperfusion. Moreover, 2ME2 and 3-MA exhibited powerful protective effects against ischemic/reperfusion induced neuronal injury. CONCLUSIONS: This study confirmed that autophagy participated in post-ischemic neuronal injury. 2ME2, a HIF-1α inhibitor, might significantly decrease autophagy activation after cerebral ischemia and relieve post-ischemic neuronal injury. Our findings demonstrate that autophagy could be a potential target for neuronal protection after cerebral ischemia.
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 | 0.000 |
| 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".