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Record W1604126483 · doi:10.3389/fnagi.2015.00114

Can insulin signaling pathways be targeted to transport Aβ out of the brain?

2015· review· en· W1604126483 on OpenAlexaff
Milène Vandal, Philippe Bourassa, Frédéric Calon

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2015
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsBlood–brain barrierInsulinInsulin receptorNeurocognitiveMedicineCognitive declineNeuroscienceDiseaseEndocrinologyInternal medicineTransporterCentral nervous systemPharmacologyCognitionBiologyInsulin resistanceDementiaBiochemistry

Abstract

fetched live from OpenAlex

Although the causal role of Amyloid-β (Aβ) in Alzheimer's disease (AD) is unclear, it is still reasonable to expect that lowering concentrations of Aβ in the brain may decrease the risk of developing the neurocognitive symptoms of the disease. Brain capillary endothelial cells forming the blood-brain barrier (BBB) express transporters regulating the efflux of Aβ out of the cerebral tissue. Age-related BBB dysfunctions, that have been identified in AD patients, might impair Aβ clearance from the brain. Thus, targeting BBB outward transport systems has been suggested as a way to stimulate the clearance of Aβ from the brain. Recent data indicate that the increase in soluble brain Aβ and behavioral impairments in 3×Tg-AD mice generated by months of intake of a high-fat diet can be acutely reversed by the administration of a single dose of insulin. A concomitant increase in plasma Aβ suggests that clearance from the brain through the BBB is a likely mechanism for this rapid effect of insulin. Here, we review how BBB insulin response pathways could be stimulated to decrease brain Aβ concentrations and improve cognitive performance, at least on the short term.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.087
GPT teacher head0.345
Teacher spread0.259 · 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 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

Citations39
Published2015
Admission routes1
Has abstractyes

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