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Record W2016473952 · doi:10.1073/pnas.1401165111

Lipid domains control myelin basic protein adsorption and membrane interactions between model myelin lipid bilayers

2014· article· en· W2016473952 on OpenAlexafffund
Dong Woog Lee, Xavier Banquy, Kai Kristiansen, Yair Kaufman, Joan M. Boggs, Jacob N. Israelachvili

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoUniversité de Montréal
FundersNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsMyelinMembraneLipid bilayerBiophysicsMyelin basic proteinBilayerAdhesionChemistryProteolipid protein 1AdsorptionBiochemistryBiologyPhysical chemistryNeuroscienceOrganic chemistryCentral nervous system

Abstract

fetched live from OpenAlex

The surface forces apparatus and atomic force microscope were used to study the effects of lipid composition and concentrations of myelin basic protein (MBP) on the structure of model lipid bilayers, as well as the interaction forces and adhesion between them. The lipid bilayers had a lipid composition characteristic of the cytoplasmic leaflets of myelin from "normal" (healthy) and "disease-like" [experimental allergic encephalomyelitis (EAE)] animals. They showed significant differences in the adsorption mechanism of MBP. MBP adsorbs on normal bilayers to form a compact film (3-4 nm) with strong intermembrane adhesion (∼0.36 mJ/m(2)), in contrast to its formation of thicker (7-8 nm) swelled films with weaker intermembrane adhesion (∼0.13 mJ/m(2)) on EAE bilayers. MBP preferentially adsorbs to liquid-disordered submicron domains within the lipid membranes, attributed to hydrophobic attractions. These results show a direct connection between the lipid composition of membranes and membrane-protein adsorption mechanisms that affects intermembrane spacing and adhesion and has direct implications for demyelinating diseases.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.022
GPT teacher head0.283
Teacher spread0.262 · 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 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

Citations67
Published2014
Admission routes2
Has abstractyes

Explore more

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