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Record W2062350109 · doi:10.4061/2011/548380

Mechanisms of Amyloid‐Beta Peptide Uptake by Neurons: The Role of Lipid Rafts and Lipid Raft‐Associated Proteins

2010· article· en· W2062350109 on OpenAlexafffund
Aaron Y. Lai, JoAnne McLaurin

Bibliographic record

VenueInternational Journal of Alzheimer s Disease · 2010
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlzheimer's Society
KeywordsLipid raftInternalizationExtracellularBiologyCell biologyIntracellularAmyloid (mycology)RaftAmyloid betaNeurosciencePathogenesisChemistrySignal transductionReceptorBiochemistryPeptideImmunology

Abstract

fetched live from OpenAlex

A hallmark pathological feature of Alzheimer's disease (AD) is the accumulation of extracellular plaques composed of the amyloid-beta (Aβ) peptide. Thus, classically experiments were designed to examine Aβ toxicities within the central nervous system (CNS) from the extracellular space. However, a significant amount of evidence now suggests that intraneuronal accumulation of Aβ is neurotoxic and may play an important role in the disease progression of AD. One of the means by which neurons accumulate intracellular Aβ is through uptake of extracellular Aβ peptides, and this process may be a potential link between Aβ generation, synaptic dysfunction, and AD pathology. Recent studies have found that neuronal internalization of Aβ involves lipid rafts and various lipid raft-associated receptor proteins. Uptake mechanisms independent of lipid rafts have also been implicated. The aim of this paper is to summarize these findings and discuss their significance in the pathogenesis of AD.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.284
Teacher spread0.271 · 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

Citations131
Published2010
Admission routes2
Has abstractyes

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