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Record W2020061896 · doi:10.1088/1478-3975/8/6/066009

Reverse engineering an amyloid aggregation pathway with dimensional analysis and scaling

2011· article· en· W2020061896 on OpenAlex

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 Biology · 2011
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical SciencesMitacsNational Institutes of HealthMichael Smith Health Research BC
KeywordsThioflavinMonomerOligomerNucleationChemistryFibrilBiophysicsAmyloid (mycology)NucleusScalingPolymerBiochemistryBiologyPolymer chemistryCell biology

Abstract

fetched live from OpenAlex

Human islet amyloid polypeptide (hIAPP) is a cytotoxic protein that aggregates into oligomers and fibrils that kill pancreatic β-cells. Here we analyze hIAPP aggregation in vitro, measured via thioflavin-T fluorescence. We use mass-action kinetics and scaling analysis to reconstruct the aggregation pathway, and find that the initiation step requires four hIAPP monomers. After this step, monomers join the nucleus in pairs, until the first stable nucleus (of size approximately 20 monomers) is formed. This nucleus then elongates by successive addition of single monomers. We find that the best-fit of our data is achieved when we include a secondary fibril-dependent nucleation pathway in the reaction scheme. We predict how interventions that change rates of fibril elongation or nucleation rates affect the accumulation of potentially cytotoxic oligomer species. Our results demonstrate the power of scaling analysis in reverse engineering biochemical aggregation pathways.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.221

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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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