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Record W2604183633 · doi:10.5772/66403

Pathogenic Angiogenic Mechanisms in Alzheimer's Disease

2017· book-chapter· en· W2604183633 on OpenAlexaff
Chaahat S. B. Singh, Cheryl G. Pfeifer, Wilfred A. Jefferies

Bibliographic record

VenueInTech eBooks · 2017
Typebook-chapter
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsVancouver Coastal HealthCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsAngiogenesisMedicineDiseasePathologicalBlood–brain barrierBlood vesselNeuroscienceCerebral blood flowPathologyBlood flowBiologyInternal medicineCentral nervous system

Abstract

fetched live from OpenAlex

Vascular dysfunction is a crucial pathological hallmark of Alzheimer's disease (AD). Studies have reported that beta amyloid (Aβ) causes increased blood vessel growth in the brains of AD mouse models, a phenomenon that is also seen in AD patients. This has given way to an alternative angiogenesis hypothesis according to which, increased leakiness in the blood vessels disrupts the blood‐brain barrier (BBB) and allows unwanted blood products to enter the brain causing progression of disease pathology, promoting amyloid clumping and aggregation along with impaired cerebral blood flow. Furthermore, the expression of melanotransferrin in AD model and patients may contribute to angiogenesis. The objective of this chapter is to attempt to establish a link between the vascular damage and AD pathology. Curbing the vascular changes and resulting damage seen in the brains of AD model mice and improving their cognition by treating with FDA‐approved anti‐angiogenic drugs may expedite the translational potential of this research into clinical trials in human patients. This direction into targeting angiogenesis will facilitate new preventive and therapeutic interventions for AD and related vascular diseases.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.310
Teacher spread0.265 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations4
Published2017
Admission routes1
Has abstractyes

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