Brain large artery dilatation increases the risk for Alzheimer’s disease pathology
Bibliographic record
Abstract
ABSTRACT Alzheimer’s disease (AD) and related dementia cases are increasing globally, emphasizing the urgent need to clarify disease mechanisms for translational application in diagnoses and treatment. Vascular alterations represent a major pathological feature of AD, and beyond the well-established roles of small vessel disease and large artery atherosclerosis, our group has previously demonstrated that brain large artery dilatation is associated with elevated risk of dementia and Alzheimer pathology. The most severe manifestation of this non-atherosclerotic arterial phenotype is dolichoectasia, an enlargement of large blood vessels (Gutierrez et al., 2019; Melgarejo et al., 2024). Despite consistent epidemiological evidence across populations, the mechanistic link between arterial dilatation and AD remains poorly understood. To address this gap, we induced dolichoectasia in App NL-G-F mice, a model of amyloid pathology, by injecting elastase into the cisterna magna. After three months, brains were examined using biochemical and immunohistochemical methods. Elastase-treated mice exhibited a significant increase in amyloid plaques in the hippocampus ( p = 0.021 ) and cortex ( p = 0.029 ) compared with vehicle-treated controls. Neuronal loss was evident in the CA1 region of the hippocampus ( p = 0.036 ), with a trend towards neurodegeneration in CA3 (p = 0.055). We also observed elevated p62 in the hippocampus and cortex ( p = 0.009 and p = 0.001 , respectively), suggesting impaired protein or autophagic-lysosomal clearance. Although no overt increase in neuroinflammation or astrogliosis was detected at this time point, matrix metalloproteinase-9 (MMP-9) levels were trending towards elevated levels (p = 0.058). Combined, these findings indicate successful elastase-induced brain arterial dilatation accelerates AD-related pathology in App NL-G-F mice, providing mechanistic evidence that large artery dilatation may contribute directly to Alzheimer’s disease progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 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".