MétaCan
Menu
Back to cohort
Record W2962908035 · doi:10.1021/acs.langmuir.9b01645

Size-Dependent Interaction of Amyloid β Oligomers with Brain Total Lipid Extract Bilayer—Fibrillation Versus Membrane Destruction

2019· article· en· W2962908035 on OpenAlexaff
Dušan Mrđenović, Marta Majewska, Izabela S. Pieta, Piotr Bernatowicz, Robert Nowakowski, Włodzimierz Kutner, Jacek Lipkowski, Piotr Pięta

Bibliographic record

VenueLangmuir · 2019
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFibrillogenesisMembraneOligomerChemistryBiophysicsLipid bilayerFibrilAmyloid (mycology)Senile plaquesAlzheimer's diseaseBiochemistryPolymer chemistryBiologyPathology

Abstract

fetched live from OpenAlex

Amyloid β, Aβ(1-42), is a component of senile plaques present in the brain of Alzheimer's disease patients and one of the main suspects responsible for pathological consequences of the disease. Herein, we directly visualize the Aβ activity toward a brain-like model membrane and demonstrate that this activity strongly depends on the Aβ oligomer size. PeakForce quantitative nanomechanical mapping mode of atomic force microscopy imaging revealed that the interaction of large-size (LS) Aβ oligomers, corresponding to high-molecular-weight Aβ oligomers, with the brain total lipid extract (BTLE) membrane resulted in accelerated Aβ fibrillogenesis on the membrane surface. Importantly, the fibrillogenesis did not affect integrity of the membrane. In contrast, small-size (SS) Aβ oligomers, corresponding to low-molecular-weight Aβ oligomers, created pores and then disintegrated the BTLE membrane. Both forms of the Aβ oligomers changed nanomechanical properties of the membrane by decreasing its Young's modulus by ∼45%. Our results demonstrated that both forms of Aβ oligomers induce the neurotoxic effect on the brain cells but their action toward the membrane differs significantly.

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 score0.265
Threshold uncertainty score0.565

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.301
Teacher spread0.282 · 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

Citations39
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueLangmuirSame topicAlzheimer's disease research and treatmentsFrench-language works237,207