Chronic cerebral hypoperfusion: a key mechanism leading to vascular cognitive impairment and dementia. Closing the translational gap between rodent models and human vascular cognitive impairment and dementia
Bibliographic record
Abstract
Increasing evidence suggests that vascular risk factors contribute to neurodegeneration, cognitive impairment and dementia. While there is considerable overlap between features of vascular cognitive impairment and dementia (VCID) and Alzheimer's disease (AD), it appears that cerebral hypoperfusion is the common underlying pathophysiological mechanism which is a major contributor to cognitive decline and degenerative processes leading to dementia. Sustained cerebral hypoperfusion is suggested to be the cause of white matter attenuation, a key feature common to both AD and dementia associated with cerebral small vessel disease (SVD). White matter changes increase the risk for stroke, dementia and disability. A major gap has been the lack of mechanistic insights into the evolution and progress of VCID. However, this gap is closing with the recent refinement of rodent models which replicate chronic cerebral hypoperfusion. In this review, we discuss the relevance and advantages of these models in elucidating the pathogenesis of VCID and explore the interplay between hypoperfusion and the deposition of amyloid β (Aβ) protein, as it relates to AD. We use examples of our recent investigations to illustrate the utility of the model in preclinical testing of candidate drugs and lifestyle factors. We propose that the use of such models is necessary for tackling the urgently needed translational gap from preclinical models to clinical treatments.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".