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Record W4224324374 · doi:10.1002/1873-3468.14358

Biophysical characterization as a tool to predict amyloidogenic and toxic properties of amyloid‐β42 peptides

2022· article· en· W4224324374 on OpenAlexafffund
Adeola Shobo, Alexander Röntgen, Mark A. Hancock, Gerd Multhaup

Bibliographic record

VenueFEBS Letters · 2022
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsThioflavinCircular dichroismChemistryPeptideSurface plasmon resonanceAmyloid (mycology)Random coilMass spectrometryMatrix-assisted laser desorption/ionizationBiophysicsBiochemistryChromatographyAlzheimer's diseaseNanotechnologyDesorptionMaterials scienceBiologyPathologyNanoparticle

Abstract

fetched live from OpenAlex

Amyloid-β42 (Aβ42) peptides are central to the amyloid pathology in Alzheimer's disease (AD). As biological mimetics, properties of synthetic Aβ peptides usually vary between vendors and batches, thus impacting the reproducibility of experimental studies. Here, we tested recombinantly expressed Aβ42 (Asp1 to Ala42) against synthetic Aβ42 from different suppliers using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), circular dichroism (CD) spectroscopy, thioflavin T aggregation, surface plasmon resonance, and MTT cell viability assays. Overall, our recombinant Aβ42 provided a reproducible mimetic of desired properties. Across experimental approaches, the combined detection of Aβ42 dimers and random coil to β-sheet transition only correlated with aggregation-prone and cytotoxic peptides. Conclusively, combining MALDI-MS with CD appears to provide a rapid, reliable means to predict the 'bioactivity' of Aβ42.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.419

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)0.0000.000

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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