Deficiency of Nrf2 exacerbates white matter damage and microglia/macrophage levels in a mouse model of vascular cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Chronic cerebral hypoperfusion causes damage to the brain's white matter underpinning vascular cognitive impairment. Inflammation and oxidative stress have been proposed as key pathophysiological mechanisms of which the transcription factor Nrf2 is a master regulator. We hypothesised that white matter pathology, microgliosis, blood-brain barrier breakdown and behavioural deficits induced by chronic hypoperfusion would be exacerbated in mice deficient in the transcription factor Nrf2. METHODS: Mice deficient in Nrf2 (male heterozygote or homozygous for Nrf2 knockout) or wild-type littermates on a C57Bl6/J background underwent bilateral carotid artery stenosis (BCAS) to induce chronic cerebral hypoperfusion or sham surgery and survived for a further 6 weeks. White matter pathology was assessed with MAG immunohistochemistry as a marker of altered axon-glial integrity; alterations to astrocytes and microglia/macrophages were assessed with GFAP and Iba1 immunohistochemistry, and blood-brain barrier breakdown was assessed with IgG immunohistochemistry. Behavioural alterations were assessed using 8-arm radial arm maze, and alterations to Nrf2-related and inflammatory-related genes were assessed with qRT-PCR. RESULTS: mice and further exacerbated by the complete absence of Nrf2. Chronic hypoperfusion induced white matter astrogliosis and induced an impairment in behaviour assessed with radial arm maze; however, these measures were not affected by Nrf2 deficiency. Although Nrf2-related antioxidant gene expression was not altered by chronic cerebral hypoperfusion, there was evidence for elevated pro-inflammatory related gene expression following chronic hypoperfusion that was not affected by Nrf2 deficiency. CONCLUSIONS: The results demonstrate that the absence of Nrf2 exacerbates white matter pathology and microgliosis following cerebral hypoperfusion but does not affect behavioural impairment.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".