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Record W1990439587 · doi:10.1038/srep01354

Cessation of Neoangiogenesis in Alzheimer's Disease Follows Amyloid-beta Immunization

2013· article· en· W1990439587 on OpenAlexafffund
Kaan E. Biron, Dara L. Dickstein, Rayshad Gopaul, Franz Fenninger, Wilfred A. Jefferies

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of British Columbia HospitalCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsHypervascularityMedicinePathogenesisVascularityAlzheimer's diseaseDiseaseReversionAmyloid betaBlood–brain barrierAmyloid (mycology)ImmunotherapyPathologyImmunologyCentral nervous systemImmune systemBiologyInternal medicine

Abstract

fetched live from OpenAlex

Pathogenic neoangiogenesis in Alzheimer's disease (AD) is due to amyloid-beta (Aβ) and results in blood-brain barrier (BBB) leakiness in AD. It likely occurs as a compensatory response to impaired cerebral blood flow and provides a strong link between brain vascularity and AD. Aβ immunotherapy is an experimental treatment for AD; however, unexpected negative vascular side effects seen in early human clinical trials demonstrate that our knowledge of Aβ and AD pathogenesis is incomplete. We demonstrate that immunization with Aβ peptides neutralizes the amyloid trigger leading to neoangiogenesis and reverses hypervascularity in Tg2576 AD mice. This process resolves plaque burden suggesting that neoangiogenesis is a key mechanism underlying plaque formation. A meta-analysis demonstrated that hypervascular reversion in vaccinated Alzheimer's patients. This appears to be the first example of vascular reversion following any therapeutic intervention and supports the conclusion that modulation of neoangiogenesis may repair damage in the AD brain.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.290
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations34
Published2013
Admission routes2
Has abstractyes

Explore more

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