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Modified Poly(ε-caprolactone) with Tunable Degradability and Improved Biofunctionality for Regenerative Medicine

2023· article· en· W4383821831 on OpenAlexafffund
Jun Shen, Weihao Yuan, Maryam Badv, Alireza Moshaverinia, Paul S. Weiss

Bibliographic record

VenueACS Materials Au · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Calgary
FundersCalifornia NanoSystems InstituteNational Institute of Dental and Craniofacial ResearchNatural Sciences and Engineering Research Council of CanadaUniversity of California, Los AngelesNational Institutes of Health
KeywordsBiocompatibilityDegradation (telecommunications)ElectrospinningMaterials scienceCaprolactoneMembraneChemical engineeringPotassium permanganateBiomedical engineeringComposite materialChemistryPolymerPolymerizationComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The use of poly(ε-caprolactone) (PCL) for biomedical applications is well established, particularly for permanent implants, due to its slow degradation rate, suitable mechanical properties, and biocompatibility. However, the slow degradation rate of PCL limits its application for short-term and temporary biomedical applications where bioabsorbability is required. To enhance the properties of PCL and to expand its biomedical applications, we developed an approach to produce PCL membranes with tunable degradation rates, mechanical properties, and biofunctional features. Specifically, we utilized electrospinning to create fibrous PCL membranes, which were then chemically modified using potassium permanganate to alter their degradability while having minimal impact on their fibrous morphology. The effects of the chemical treatments were investigated by treating the samples for different time periods ranging from 6 to 48 h. After the 48 h treatment, the membrane degraded by losing 25% of its mass over 12 weeks in degradation studies, while maintaining its mechanical strength and exhibiting superior biofunctional features. Our results suggest that this approach for developing PCL with tailored properties could have significant potential for a range of biomedical applications.

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)0.001
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 score0.012
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.027
GPT teacher head0.276
Teacher spread0.249 · 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
Published2023
Admission routes2
Has abstractyes

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