Losartan improves cerebrovascular function in a mouse model of Alzheimer's disease with combined overproduction of amyloid-β and transforming growth factor-β1
Bibliographic record
Abstract
Alterations of the renin-angiotensin system have been implicated in the pathogenesis of Alzheimer's disease. We tested the efficacy of losartan (10 mg/kg/day for three months), a selective angiotensin II type 1 receptor antagonist, in alleviating cerebrovascular and cognitive deficits in double-transgenic mice (six months at endpoint) that overexpress a mutated form of the human amyloid precursor protein (APP Swe,Ind ) and a constitutively active form of the transforming growth factor-β1, thereafter named A/T mice. Losartan rescued cerebrovascular reactivity, particularly the dilatory responses, but failed to attenuate astroglial activation and to normalize the neurovascular uncoupling response to sensory stimulation. The cognitive deficits of A/T mice were not restored by losartan nor were the increased brain levels of soluble and insoluble Aβ 1–40 and Aβ 1–42 peptides normalized. Our results are the first to demonstrate the capacity of losartan to improve cerebrovascular reactivity in an Alzheimer's disease mouse model of combined Aβ-induced vascular oxidative stress and transforming growth factor-β1-mediated vascular fibrosis. These data suggest that losartan may be promising for restoring cerebrovascular function in patients with vascular diseases at risk for vascular dementia or Alzheimer's disease. However, a combined therapy may be warranted for rescuing both vascular and cognitive deficits in a multifaceted pathology like Alzheimer's disease.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".