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Record W2951888728 · doi:10.1021/acs.biomac.9b00417

Cross-Linked Elastin-like Polypeptide Membranes as a Model for Medial Arterial Calcification

2019· article· en· W2951888728 on OpenAlexafffund
Ophélie Gourgas, Lisa D. Muiznieks, Dainelys Guadarrama Bello, Antonio Nanci, Simon Sharpe, Marta Cerruti

Bibliographic record

VenueBiomacromolecules · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsUniversity of TorontoUniversité de MontréalHospital for Sick ChildrenMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchPolytechnique MontréalHeart and Stroke Foundation of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesCanadian Light SourceCanada Research Chairs
KeywordsElastinCalcificationCalciumChemistryMineralization (soil science)NucleationMembraneBiophysicsIn vivoAnatomyBiochemistryInternal medicinePathologyBiologyMedicine

Abstract

fetched live from OpenAlex

Calcium phosphate minerals deposit on the elastin-rich medial layers of arteries in the majority of seniors, diabetic, and chronic kidney disease patients, causing severe cardiovascular complications. There is no cure for medial calcification, and the mechanism of mineral formation on elastin layers is unknown. Here we propose cross-linked elastin-like polypeptide membranes as models to study medial calcification. Calcium phosphates deposit first on fibers and filaments and then spread to globular structures present in the membranes. Mineral phase evolution analyzed by near-edge X-ray spectroscopy matches that previously observed in a mouse model of medial calcification, showing that this simple system captures some of the key in vivo findings. This work shows how minerals form and evolve upon nucleation on elastin and provides an in vitro model that can be tuned to study hypotheses related to arterial calcification mechanisms and test drugs to stop or revert mineralization.

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 score0.037
Threshold uncertainty score0.892

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 head0.020
GPT teacher head0.327
Teacher spread0.307 · 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

Citations29
Published2019
Admission routes2
Has abstractyes

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