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Record W1561483156 · doi:10.1002/jbm.b.33028

Postelectrospinning modifications for alginate nanofiber‐based wound dressings

2013· article· en· W1561483156 on OpenAlexaff
Victor C. M. Leung, Ryan Hartwell, Sanam Salimi Elizei, Heejae Yang, Aziz Ghahary, Frank Ko

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2013
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of British Columbia
FundersNational Institutes of Health
KeywordsNanofiberElectrospinningGlutaraldehydeMaterials scienceAqueous solutionChemical engineeringPolylysineGelatinCalcium alginatePhosphate buffered salinePolymer chemistryComposite materialPolymerChemistryChromatographyOrganic chemistryCalcium

Abstract

fetched live from OpenAlex

Alginate nanofibers have been attractive for potential tissue regeneration applications due to a combination of their moisture retention ability and large surface area available in a nonwoven nanofiber form. This study aims to address several challenges in alginate nanofiber application, including the lack of structural stability in aqueous environment and limited cell attachment as compared to commercial wound dressings, via examining crosslinking techniques. In addition to the commonly performed divalent ion crosslinking, a glutaraldehyde double-crosslinking step and polylysine addition were applied to an electrospun alginate nanofiber nonwoven mat. With optimization of the electrospinning solution, nanofiber morphology was maintained after the two-stage crosslinking process. Extensibility of the nanofiber mat reduced after the crosslinking process. However, both aqueous stability and cell attachment improved after the postspinning modifications, as shown through degradation tests in phosphate buffered saline solutions and fibroblast cell culture studies, respectively.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.050
GPT teacher head0.355
Teacher spread0.305 · 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; both teacher heads agree on what is shown here.

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

Citations59
Published2013
Admission routes1
Has abstractyes

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