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Record W2086367553 · doi:10.1038/srep02053

VEGF significantly restores impaired memory behavior in Alzheimer's mice by improvement of vascular survival

2013· article· en· W2086367553 on OpenAlexaff
Piotr Religa, Renhai Cao, Dorota Religa, Yuan Xue, Nenad Bogdanović, David Westaway, Hugo H. Marti, Bengt Winblad, Yihai Cao

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersSvenska LäkaresällskapetNatural Science Foundation of Tianjin CityKarolinska InstitutetEuropean CommissionVetenskapsrådetTianjin Medical UniversitySwedish Cancer Foundation
KeywordsVEGF receptorsMedicineNeuroscienceInternal medicinePsychology

Abstract

fetched live from OpenAlex

The functional impact of amyloid peptides (Aβs) on the vascular system is less understood despite these pathologic peptides are substantially deposited in the brain vasculature of Alzheimer's patients. Here we show substantial accumulation of Aβs 40 and 42 in the brain arterioles of Alzheimer's patients and of transgenic Alzheimer's mice. Purified Aβs 1-40 and 1-42 exhibited vascular regression activity in the in vivo animal models and vessel density was reversely correlated with numbers and sizes of amyloid plaques in human patients. A significant high number of vascular cells underwent cellular apoptosis in the brain vasculature of Alzheimer's patients. VEGF significantly prevented Aβ-induced endothelial apoptosis in vitro. Neuronal expression of VEGF in transgenic mice restored memory behavior of Alzheimer's. These findings provide conceptual implication of improvement of vascular functions as a novel therapeutic approach for the treatment of Alzheimer's disease.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.265
Teacher spread0.230 · 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

Citations130
Published2013
Admission routes1
Has abstractyes

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