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Record W2964608167 · doi:10.1002/ppap.201900041

Functional plasma polymer films for the purification of pancreatic β cells

2019· article· en· W2964608167 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

VenuePlasma Processes and Polymers · 2019
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymerCopolymerFibroblastAdhesionPlasmaOxygenCoatingMaterials scienceChemical engineeringSubstrate (aquarium)SelectivitySolubilityPolymer chemistryChemistryNanotechnologyOrganic chemistryBiochemistryComposite materialIn vitroBiology

Abstract

fetched live from OpenAlex

Abstract Pure pancreatic β cells play a major role in multiple areas of diabetic research, nevertheless, the separation of β cells remains a challenge. The objective of this study is to show the feasibility of using plasma polymers to remove fibroblasts from β cells. Oxygen‐ and nitrogen‐rich organic coatings with different O and N concentrations were prepared by plasma copolymerization of binary gas mixtures combining a hydrocarbon and a heteroatom‐containing gas. The separation of cell pancreatic and fibroblast cell lines was tested using various plasma polymers. The results showed that a unique oxygen‐rich coating with ∼13% of oxygen, promoted fibroblast separation with 82% selectivity. The results support the concept that plasma polymers could be a suitable substrate for the selective adhesion and removal of fibroblasts.

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 score0.142
Threshold uncertainty score0.912

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)0.0010.000

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 head0.015
GPT teacher head0.229
Teacher spread0.213 · 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