Nrf2 Suppresses Oxidative Stress and Inflammation in <i>App</i> Knock-In Alzheimer’s Disease Model Mice
Bibliographic record
Abstract
Nrf2 (NF-E2-related-factor 2) is a stress-responsive transcription factor that protects cells against oxidative stresses. To clarify whether Nrf2 prevents Alzheimer’s disease (AD), AD model AppNL-G-F/NL-G-F knock-in (AppNLGF) mice were studied in combination with genetic Nrf2 induction model Keap1FA/FA mice. While AppNLGF mice displayed shorter latency to escape than wild-type mice in the passive-avoidance task, the impairment was improved in AppNLGF::Keap1FA/FA mice. Matrix-assisted laser desorption ionization–mass spectrometry imaging revealed that reduced glutathione levels were elevated by Nrf2 induction in AppNLGF::Keap1FA/FA mouse brains compared to AppNLGF mouse brains. Genetic Nrf2 induction in AppNLGF mice markedly suppressed the elevation of the oxidative stress marker 8-OHdG and Iba1-positive microglial cell number. We also determined the plasmalogen-phosphatidylethanolamine (PlsPE) level as an AD biomarker. PlsPE containing polyunsaturated fatty acids was decreased in the AppNLGF mouse brain, but Nrf2 induction attenuated this decline. To evaluate whether pharmacological induction of Nrf2 elicits beneficial effects for AD treatment, we tested the natural compound 6-MSITC [6-(methylsulfinyl)hexyl isothiocyanate]. Administration of 6-MSITC improved the impaired cognition of AppNLGF mice in the passive-avoidance task. These results demonstrate that the induction of Nrf2 ameliorates cognitive impairment in the AD model mouse by suppressing oxidative stress and neuroinflammation, suggesting that Nrf2 is an important therapeutic target of AD.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".