Inhibition of p53 attenuates ischemic stress-induced activation of astrocytes
Bibliographic record
Abstract
In cerebral ischemia, studies of cell death have focused primarily on neurons, but recent work indicates that ischemia also causes damage to astrocytes. Activation of astrocytes is a typical brain response to stress stimuli and is evidenced by changes in cellular function and morphology, as well as upregulation of glial fibrillary acidic protein. The tumor-suppressor transcription factor p53 has recently been implicated as a mediator of ischemia-induced neuronal death, but very little is known about its role in the activation or the death of astrocytes. The present study investigated the role of p53 in astrocyte and neuronal toxicity using in-vitro and in-vivo ischemic stroke models. We showed that p53 is activated in ischemic brains and in oxygen-glucose deprivation (OGD)-induced cell death in neurons and astrocytes. Inhibition of p53 activity using either pifithrin-α or small interference RNA interference reduced OGD-induced cell death and pifithrin-α reversed OGD-induced impairment of glutamate uptake in astrocytes, suggesting that p53 might play a key role in mediating neurotoxicity and gliotoxicity in ischemic brain injury. This study shows that p53 is activated in astrocytes during ischemia and that inhibition of the activity of this molecule prevents not only OGD-induced neuronal and astrocytic death but also astrocyte activation and impaired glutamate uptake. These findings suggest that p53 may be a valuable therapeutic target in ischemic brain injury.
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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 | 0.001 |
| 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".