Complete Rescue of Cerebrovascular Function in Aged Alzheimer's Disease Transgenic Mice by Antioxidants and Pioglitazone, a Peroxisome Proliferator-Activated Receptor γ Agonist
Bibliographic record
Abstract
Accumulating evidence suggests that cerebrovascular dysfunction is an important factor in the pathogenesis of Alzheimer's disease (AD). Using aged ( approximately 16 months) amyloid precursor protein (APP) transgenic mice that exhibit increased production of the amyloid-beta (Abeta) peptide and severe cerebrovascular and memory deficits, we examined the capacity of in vivo treatments with the antioxidants N-acetyl-L-cysteine (NAC) and tempol, or the peroxisome proliferator-activated receptor gamma agonist pioglitazone to rescue cerebrovascular function and selected markers of AD neuropathology. Additionally, we tested the ability of pioglitazone to normalize the impaired increases in cerebral blood flow (CBF) and glucose uptake (CGU) induced by whisker stimulation, and to reverse spatial memory deficits in the Morris water maze. All compounds fully restored cerebrovascular reactivity of isolated cerebral arteries concomitantly with changes in proteins regulating oxidative stress, without reducing brain Abeta levels or Abeta plaque load. Pioglitazone, but not NAC, significantly attenuated astroglial activation and improved, albeit nonsignificantly, the reduced cortical cholinergic innervation. Furthermore, pioglitazone completely normalized the CBF and CGU responses to increased neuronal activity, but it failed to improve spatial memory. Our results are the first to demonstrate that late pharmacological intervention with pioglitazone not only overcomes cerebrovascular dysfunction and altered neurometabolic coupling in aged APP mice, but also counteracts cerebral oxidative stress, glial activation, and, partly, cholinergic denervation. Although early or combined therapy may be warranted to improve cognition, these findings unequivocally point to pioglitazone as a most promising strategy for restoring cerebrovascular function and counteracting several AD markers detrimental to neuronal function.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".