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Record W4415446010 · doi:10.3390/antiox14111269

Effect of Copper-Catalyzed Oxidation on the Aggregation of the Islet Amyloid Polypeptide

2025· article· en· W4415446010 on OpenAlexafffund
Océane Amilca, Phuong Trang Nguyen, Lucie Perquis, Fabrice Collin, Steve Bourgault

Bibliographic record

VenueAntioxidants · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsPROTEOUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsIsletAmyloid (mycology)Pancreatic isletsFibrilAmylinProtein aggregationPeptideOxidative phosphorylationAmyloid fibril

Abstract

fetched live from OpenAlex

The islet amyloid polypeptide (IAPP) is a 37-residue peptide hormone secreted by pancreatic β-cells that is known to aggregate into amyloid fibrils. These fibrils accumulate in the pancreatic islets of individuals afflicted with type 2 diabetes and are implicated in β-cell dysfunction. Metal ions such as copper and zinc are known to modulate IAPP fibrillization, yet the role of metal-induced oxidative modifications in this process remains largely unexplored. This study examines the non-enzymatic post-translational oxidation of IAPP and its effects on aggregation using the biologically relevant Cu/O2/ascorbate system. Mass spectrometry identified residues within the amyloidogenic region (residues 20–29) as the primary targets of oxidation. These oxidative modifications impaired the formation of cross-β-sheet amyloid fibrils and promoted the accumulation of amorphous aggregates. The H18A IAPP derivative, lacking the key metal-binding histidine, was also examined to assess the impact of sequence variation on oxidation and aggregation. Copper-mediated oxidation of H18A resulted in a broader distribution of oxidation sites and impacts fibril formation. These findings provide preliminary mechanistic insights into copper-induced oxidation and its impact on IAPP 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.

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 score0.300
Threshold uncertainty score0.208

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 head0.012
GPT teacher head0.306
Teacher spread0.294 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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