Pioglitazone Improves Reversal Learning and Exerts Mixed Cerebrovascular Effects in a Mouse Model of Alzheimer’s Disease with Combined Amyloid-β and Cerebrovascular Pathology
Bibliographic record
Abstract
Animal models of Alzheimer's disease (AD) are invaluable in dissecting the pathogenic mechanisms and assessing the efficacy of potential new therapies. Here, we used the peroxisome proliferator-activated receptor gamma agonist pioglitazone in an attempt to rescue the pathogenic phenotype in adult (12 months) and aged (>18 months) bitransgenic A/T mice that overexpress a mutated human amyloid precursor protein (APPSwe,Ind) and a constitutively active form of transforming growth factor-β1 (TGF-β1). A/T mice recapitulate the AD-related cognitive deficits, amyloid beta (Aβ) and cerebrovascular pathologies, as well as the altered metabolic and vascular coupling responses to increased neuronal activity. Pioglitazone normalized neurometabolic and neurovascular coupling responses to sensory stimulation, and reduced cortical astroglial and hippocampal microglial activation in both age groups. Spatial learning and memory deficits in the Morris water maze were not rescued by pioglitazone, but reversal learning was improved in the adult cohort notwithstanding a progressing Aβ pathology. While pioglitazone preserved the constitutive nitric oxide synthesis in the vessel wall, it unexpectedly failed to restore cerebrovascular reactivity in A/T mice and even exacerbated the dilatory deficits. These data demonstrate pioglitazone's efficacy on selective AD hallmarks in a complex AD mouse model of comorbid amyloidosis and cerebrovascular pathology. They further suggest a potential benefit of pioglitazone in managing neuroinflammation, cerebral perfusion and glucose metabolism in AD patients devoid of cerebrovascular pathology.
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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".