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Record W2183734092 · doi:10.3390/membranes5040824

The Position of Aβ22-40 and Aβ1-42 in Anionic Lipid Membranes Containing Cholesterol

2015· article· en· W2183734092 on OpenAlexaff
Matthew A. Barrett, Richard J. Alsop, Thomas Hauß, Maikel C. Rheinstädter

Bibliographic record

VenueMembranes · 2015
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMembranePeptideP3 peptideNeurodegenerationLipid bilayerAmyloid (mycology)CholesterolChemistryBiophysicsTransmembrane proteinNeurotoxicityBiochemistryCell membraneAmyloid precursor proteinBiological membraneAlzheimer's diseaseBiologyMedicineReceptorInternal medicineToxicityDisease

Abstract

fetched live from OpenAlex

Amyloid-β peptides interact with cell membranes in the human brain and are associated with neurodegenerative diseases, such as Alzheimer's disease. An emerging explanation of the molecular mechanism, which results in neurodegeneration, places the cause of neurotoxicity of the amyloid- peptides on their potentially negative interaction with neuronal membranes. It is known that amyloid-β peptides interact with the membrane, modifying the membrane's structural and dynamic properties. We present a series of X-ray diffraction experiments on anionic model lipid membranes containing various amounts of cholesterol. These experiments provide experimental evidence for an interaction of both the full length amyloid-β1-42 peptide, and the peptide fragment amyloid-β22-40 with anionic bilayer containing cholesterol. The location of the amyloid-β peptides was determined from these experiments, with the full length peptide embedding into the membrane, and the peptide fragment occupying 2 positions-on the membrane surface and embedded into the membrane core.

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 score0.002
Threshold uncertainty score0.004

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.312
Teacher spread0.277 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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