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Record W2120524194 · doi:10.3109/13506129.2015.1070824

Wildlife sequences of islet amyloid polypeptide (IAPP) identify critical species variants for fibrillization

2015· article· en· W2120524194 on OpenAlexafffund
Jessica S. Fortin, Marie‐Odile Benoit‐Biancamano

Bibliographic record

VenueAmyloid · 2015
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité de Montréal
FundersDiabète Québec
KeywordsIsletAmyloid (mycology)In silicoAmyloidosisBiologyPeptideGene isoformBiochemistryPathogenesisComputational biologyDiabetes mellitusGeneMedicineImmunologyEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

Amyloid can be detected in the islets of Langerhans in a majority of type 2 diabetic patients. These deposits have been associated with β-cell death, thereby furthering diabetes progression. Islet amyloid polypeptide (IAPP) amyloidogenicity is quite variable among animal species, and studying this variability could further our understanding of the mechanisms involved in the aggregation process. Thus, the general aim of this study was to identify IAPP isoforms in different animal species and characterize their propensity to form fibrillar aggregates. A library of 23 peptides (fragment 8-32) was designed to study the amyloid formation using in silico analysis and in vitro assays. Amyloid formation was impeded when the NFLVH motif found in segment 8-20 was substituted by DFLGR or KFLIR segments. A 29P, 14K and 18R substitution were often present in non-amyloidogenic sequences. Non-amyloidogenic sequences were obtained from Leontopithecus rosalia, Tursiops truncatus and Vicugna pacos. Fragment peptides from 34 species were amyloidogenic. To conclude, this project advances our knowledge on the comparative pathogenesis of amyloidosis in type II diabetes. It is conceivable that the additional information gained may help point towards new therapeutic strategies for diabetes patients.

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)0.001
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 score0.228
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.076
GPT teacher head0.371
Teacher spread0.295 · 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 designObservational
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

Citations20
Published2015
Admission routes2
Has abstractyes

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