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The early contribution of cerebrovascular factors to the pathogenesis of Alzheimer’s disease

2012· review· en· W2169832046 on OpenAlexafffund
Pedro M. Pimentel‐Coelho, Serge Rivest

Bibliographic record

VenueEuropean Journal of Neuroscience · 2012
Typereview
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsCentre hospitalier de l'Université LavalUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsDementiaPathogenesisNeuroscienceNeurogenesisDiseaseMedicineVascular dementiaAngiogenesisPopulationPathologyBioinformaticsPsychologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.120
GPT teacher head0.311
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations87
Published2012
Admission routes2
Has abstractyes

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