TFAP2A inhibits microRNA-126 expression at the transcriptional level and aggravates ischemic neuronal injury
Bibliographic record
Abstract
Neuronal injury induced by cerebral ischemia poses a serious health risk globally, and there is no effective clinical therapy. This study was performed to investigate the role of transcription factor AP-2 alpha (TFAP2A) in cerebral ischemia, and the underlying mechanisms, using an in-vitro model (PC-12 cells) of oxygen-glucose deprivation (OGD), and an in-vivo model (rat) of transient global cerebral ischemia (tGCI). The results for CCK-8 and Hoechst staining showed that silencing of TFAP2A enhanced the viability and decreased the rate of apoptosis of PC12 cells subjected to OGD. ChIP assays were performed to evaluate the binding of TFAP2A to the promoter region of microRNA (miR)-126, and we found that TFAP2A inhibits the expression of miR-126. Further mechanistic investigation revealed that miR-126 targets polo like kinase 2 (PLK2), and that overexpression of PLK2 activates the IκBα-NF-κB signaling pathway and suppresses the growth of PC12 cells subjected to OGD. For our in-vivo assay, we used TTC staining to analyze the infarction area in the brain tissues of rats, and Nissl staining to evaluate the number of surviving brain neurons. The pathological conditions associated with neuronal injury in rat brain tissues were assessed by staining the tissues with hematoxylin-eosin. Our results indicate that TFAP2A downregulates miR-126, and thereby upregulates PLK2 and activates the IκBα-NF-κB pathway, which increased neuronal injury following cerebral ischemia.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".