A Small-Molecule-Inducible Nrf2-Mediated Antioxidant Response Provides Effective Prophylaxis against Cerebral Ischemia<i>In Vivo</i>
Bibliographic record
Abstract
The transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) coordinates expression of genes required for free radical scavenging, detoxification of xenobiotics, and maintenance of redox potential. Previously, activation of this pleiotropic response was neuroprotective in cell culture models that simulate components of stroke damage. However, the role of Nrf2 in limiting stroke damage in vivo remained unclear. We report that Nrf2 activation protects the brain from cerebral ischemia in vivo. Acute (1-3 d) intracerebroventricular or intraperitoneal pretreatment with tert-butylhydroquinone (tBHQ), an Nrf2 activity inducer, reduced cortical damage and sensorimotor deficit at 24 h and even 1 month after ischemia-reperfusion in rats. Cortical glutathione levels robustly increased with tBHQ administration to rats and Nrf2-expressing mice, but not Nrf2(-/-) mice. Basal and inducible activities of antioxidant/detoxification enzymes in Nrf2(-/-) mice were reduced when compared with Nrf2(+/+) controls. Interestingly, larger infarcts were observed in Nrf2(-/-) mice at 7 d after stroke, but not at 24 h, suggesting that Nrf2 may play a role in shaping the penumbra well after the onset of ischemia. Neuronal death caused by a "penumbral" model of stroke, using intracortical endothelin-1 microinjection, was attenuated by tBHQ administration to Nrf2(+/+), but not to Nrf2(-/-) mice, confirming the Nrf2-specific action of tBHQ in vivo. We conclude that Nrf2 plays a role in modulating ischemic injury in vivo. Accordingly, Nrf2 activation by small molecule inducers may be a practical preventative treatment for stroke-prone patients.
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) | 0.000 | 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) | 0.002 | 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".