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Record W2001722542 · doi:10.1039/c3cp44564j

Single-molecule assays for investigating protein misfolding and aggregation

2013· article· en· W2001722542 on OpenAlex
Armin Hoffmann, Krishna Neupane, Michael T. Woodside

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsProtein aggregationComputational biologyProtein foldingNanotechnologyChemistrySmall moleculeComputer scienceBiophysicsBiologyMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Protein misfolding and aggregation are relevant to many fields. Recently, their investigation has experienced a revival as a central topic in the research of numerous human diseases, including Parkinson's and Alzheimer's. Much has been learned from ensemble biochemical approaches, but the inherently heterogeneous nature of the underlying processes has obscured many important details. Single-molecule techniques offer unique capabilities to study heterogeneous systems, while providing high temporal and structural resolution to characterize them. In this Perspective, we give an overview of the single-molecule assays that have been applied to protein misfolding and aggregation, which are mainly based on fluorescence and force spectroscopy. We describe some of the technical challenges involved in studying aggregation at the single-molecule level and discuss what has been learned about aggregation mechanisms from the different approaches.

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.

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 score0.121
Threshold uncertainty score0.937

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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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