AT2R’s (Angiotensin II Type 2 Receptor’s) Role in Cognitive and Cerebrovascular Deficits in a Mouse Model of Alzheimer Disease
Bibliographic record
Abstract
Antihypertensive medications targeting the renin-angiotensin system have lowered the incidence and progression of Alzheimer disease. Understanding how these medications function could lead to novel therapeutic strategies. AT4Rs (angiotensin IV receptors) have been associated with angiotensin receptor blockers' cognitive, cerebrovascular, and neuroinflammatory rescue in Alzheimer disease models. Yet, whether AT4Rs act alone or with AT2Rs remains unknown. Here, we investigated whether AT2Rs contribute to losartan's benefits and whether chronic AT2R activation could mimic angiotensin receptor blocker benefits in transgenic mice overexpressing familial Alzheimer disease mutations of the human APP (amyloid precursor protein). Losartan-treated mice (10 mg/kg per day, drinking water, 7 months) received intracerebroventricular (1 month) administration of vehicle or AT2R antagonist PD123319 (1.6 nmol/day). PD123319 countered losartan's benefits on spatial learning and memory, neurovascular coupling, and hampered those on oxidative stress and nitric oxide bioavailability. PD123319 did not oppose losartan's benefits on short-term memory and vasodilatory function and had no benefit on neuroinflammation or Aβ (amyloid β) pathology. Mice receiving either vehicle or selective AT2R agonist compound 21 (intracerebroventricular: 1 nmol/day, 1 month or drinking water: 10 mg/kg per day, 7 months), showed no improvement in memory, vasodilatory function, or nitric oxide bioavailability. Compound 21 treatment normalized neurovascular coupling, reduced astrogliosis independent of persisting microgliosis, and exacerbated oxidative stress in APP mice. Compound 21 reduced dense core Aβ plaques, but not diffuse plaques or Aβ species. Our findings suggest that targeting AT2Rs is not an ideal strategy for restoring Aβ-related cognitive and cerebrovascular deficits.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".