MétaCan
Menu
Back to cohort
Record W2110797945 · doi:10.3389/fphys.2013.00045

The role of ABCB1 and ABCA1 in beta-amyloid clearance at the neurovascular unit in Alzheimer's disease

2013· article· en· W2110797945 on OpenAlexafffund
Ayman ElAli, Serge Rivest

Bibliographic record

VenueFrontiers in Physiology · 2013
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsCentre hospitalier de l'Université LavalUniversité Laval
FundersCanadian Institutes of Health ResearchCanadian Stroke Network
KeywordsNeurovascular bundleDiseaseMedicineABCA1Amyloid betaAmyloid (mycology)Alzheimer's diseaseNeuroscienceAmyloid βBETA (programming language)PharmacologyInternal medicinePathologyBiologyTransporterComputer scienceBiochemistryGene

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects elderly persons, evolving with age to reach severe cognitive impairment. Amyloid deposits and neurofibrillary tangles constitute the main pathological hallmarks of AD. Amyloid deposits are initiated by the excessive production and accumulation of beta-amyloid (Aβ) peptides in the brain. The dysfunction of the Neurovascular Unit (NVU) has been proposed to be causative in AD development, due to an impaired clearance of Aβ from the brain. Cells forming the NVU express several Adenosine Triphosphate ATP-Binding Cassette (ABC) transporters, among which ABCB1 and ABCA1 play an important role in Aβ processing. The drug transporter ABCB1 directly transports Aβ from the brain into the blood circulation, whereas the cholesterol transporter ABCA1 neutralizes Aβ aggregation capacity in an Apolipoprotein E (ApoE)-dependent manner, facilitating Aβ subsequent elimination from the brain. In the present minireview, we will summarize the contribution of ABCB1, and ABCA1 at the NVU in Aβ clearance. Moreover, we will outline and discuss the possible collaboration of ABCB1, and ABCA1 at the NVU in mediating an efficient clearance of Aβ from the 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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 designObservational
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

Citations110
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in PhysiologySame topicAlzheimer's disease research and treatmentsFrench-language works237,207