The early contribution of cerebrovascular factors to the pathogenesis of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer's disease (AD) and cerebrovascular disorders are the leading causes of dementia in our ageing population. Given that the progression of neuropathological changes in the brains of AD patients initiates several years, and even decades, before the diagnosis of dementia, a great effort has been made to identify potentially modifiable factors that contribute to the pathogenesis of sporadic late-onset AD. Among these factors, cerebrovascular disease and microvascular alterations seem to bilaterally interact with the underlying AD pathology, affecting the progression of cognitive deficits. In addition, cerebrovascular dysfunction has emerged as an early event in AD, encompassing changes in virtually all cell types of the neurovascular unit, including bone marrow-derived cells, astrocytes, pericytes, vascular smooth muscle cells and endothelial cells. In this review, we discuss recent studies implicating cerebrovascular factors in the pathogenesis of AD. We also discuss how the impairment of mechanisms of brain regeneration, such as neurogenesis and angiogenesis, might be related to the vascular dysfunction. Finally, we briefly discuss several therapeutic options targeting the vascular system, which might represent an interesting strategy for preventing or delaying the onset of dementia 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".