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Record W2095613667 · doi:10.1186/alzrt230

Adjusting the compass: new insights into the role of angiogenesis in Alzheimer’s disease

2013· review· en· W2095613667 on OpenAlexafffund
Wilfred A. Jefferies, Katherine A. Price, Kaan E. Biron, Franz Fenninger, Cheryl G. Pfeifer, Dara L. Dickstein

Bibliographic record

VenueAlzheimer s Research & Therapy · 2013
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsAngiogenesisPathogenesisNeuroscienceBlood–brain barrierDiseaseCognitive declineVascular permeabilityDementiaPathologicalMedicineAlzheimer's diseaseBiologyPathologyCentral nervous systemCancer research

Abstract

fetched live from OpenAlex

Growing evidence suggests that vascular perturbation plays a critical role in the pathogenesis of Alzheimer's disease (AD). It appears to be a common feature in addition to the classic pathological hallmarks of amyloid beta (Aβ) plaques and neurofibrillary. Moreover, the accumulation of Aβ in the cerebral vasculature is closely associated with cognitive decline, and disruption of the blood-brain barrier (BBB) has been shown to coincide with the onset of cognitive impairment. Although it was originally hypothesized that the accumulation of Aβ and the subsequent disruption of the BBB were due to the impaired clearance of Aβ from the brain, a body of data now suggests an alternative hypothesis for vascular dysfunction in AD that amyloidogenesis promotes extensive neoangiogenesis leading to increased vascular permeability and subsequent hypervascularization. In this review, we discuss the role Aβ plays in angiogenesis of the neurovasculature and BBB and how it may contribute to the pathogenesis of AD. These studies suggest that interventions that directly or indirectly affect angiogenesis could have beneficial effects on amyloid and other pathways 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.198
GPT teacher head0.434
Teacher spread0.236 · 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.

Study designNot applicable
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

Citations89
Published2013
Admission routes2
Has abstractyes

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