Memory and cerebrovascular deficits recovered following angiotensin IV intervention in a mouse model of Alzheimer's disease
Bibliographic record
Abstract
Angiotensin II type 1 receptor antagonists like losartan have been found to lower the incidence and progression to Alzheimer's disease (AD), as well as rescue cognitive and cerebrovascular deficits in AD mouse models. We previously found that co-administration of an angiotensin IV (AngIV) receptor (AT4R) antagonist prevented losartan's benefits, identifying AT4Rs as a possible target to counter AD pathogenesis. Therein, we investigated whether directly targeting AT4Rs could counter AD pathogenesis in a well-characterized mouse model of AD. Wild-type and human amyloid precursor protein (APP) transgenic (J20 line) mice (4.5 months old) received vehicle or AngIV (~1.3 nmol/day, 1 month) intracerebroventricularly via osmotic minipumps. AngIV restored short-term memory, spatial learning and memory in APP mice. AngIV normalized hippocampal AT4R levels, increased hippocampal subgranular zone cellular proliferation and dendritic arborization, and reduced oxidative stress. AngIV rescued whisker-evoked neurovascular coupling, endothelial- and smooth muscle cell-mediated cerebral vasodilatory responses, and cerebrovascular nitric oxide bioavailability. AngIV did not alter blood pressure, neuroinflammation or amyloid-β (Aβ) pathology. These preclinical findings identify AT4R as a promising target to counter Aβ-related cognitive and cerebrovascular deficits in 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) | 0.001 | 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 | 0.001 |
| 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".