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Record W3197195319 · doi:10.1002/jbio.202100203

Spectral photokinetic conversion of the fluorescent probes BSB and K114 for improved detection of amyloid assemblies

2021· article· en· W3197195319 on OpenAlexafffund
Anastasiia A. Stepanchuk, Jeffrey T. Joseph, Peter K. Stys

Bibliographic record

VenueJournal of Biophotonics · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsAlberta Health ServicesUniversity of Calgary
FundersCanada Research ChairsCanada Foundation for InnovationAlzheimer's Drug Discovery Foundation
KeywordsAmyloid (mycology)FluorescenceBiophysicsFibrilAmyloid fibrilChemistryProtein aggregationKineticsAmyloidosisBiochemistryAmyloid βPathologyBiologyDiseaseMedicineOptics

Abstract

fetched live from OpenAlex

Cross-β-sheet-rich protein fibrils are infamous for their accumulation in the brains of patients diagnosed with a number of neurodegenerative diseases, including Alzheimer's disease (AD). Disease-relevant fibrils are a result of deviation of the proteins from their native structure to a misfolded state resulting in aggregation and formation of fibrils. In this study, we explored the phenomenon of light-induced fluorescence enhancement of amyloid assemblies stained with two amyloid probes (BSB and K114) using Bombyx mori silk and human AD brain sections. The photoconversion effect, accompanied by an increase in fluorescence intensity and spectral blue-shift, was highly dependent on the chemical structures of the dyes, pH, presence of glycerol and the type of amyloid. The degree of intensity and spectral change over time in response to high laser exposure were quantified and analyzed using custom-written analysis tools. Our findings provide further insight into possible mechanisms of amyloid-mediated photoconversion kinetics of K114 and BSB, and may provide more insight into the molecular nature of various amyloid assemblies.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.280

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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