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Record W4406703262 · doi:10.1002/bip.23656

Evaluating Electrospun Polycaprolactone Fibers for Blood‐Contacting Applications

2025· article· en· W4406703262 on OpenAlexafffund
Muzammil Kuddushi, Aishwarya Pawar, Mehdi Ghaffari Sharaf, Larry D. Unsworth, Xuehua Zhang

Bibliographic record

VenueBiopolymers · 2025
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsChemistryPolycaprolactoneDialysisAdsorptionProtein adsorptionBiophysicsFiberSurgeryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

When the kidneys are injured, uremic toxins (UTXs) accumulate in the body, affecting other tissues and causing a loss of essential body functions. This study investigated the adsorption of blood plasma-laden UTXs on the surface of PCL fibers to assess their potential as an alternative to membrane dialysis materials. Using plasma containing 26 UTXs at a concentration similar to that found in end-stage kidney disease patients, we analyzed the adsorbed proteins and examined clot formation in normal and toxin-treated plasma in the presence of PCL fibers. Our findings revealed that the presence of UTXs significantly increased the adsorption of proteins on PCL fiber meshes, without leading to increased clot formation. This suggests a lack of enzymatic activation despite the higher protein adsorption. Additionally, our study indicates that unmodified PCL surfaces have the potential to trigger a strong humoral immune response, underscoring the importance of understanding these interactions for the development of personalized treatment approaches for patients with kidney failure.

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: Methods · Consensus signal: none
Teacher disagreement score0.398
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.359
Teacher spread0.341 · 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
GenreMethods

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

Citations2
Published2025
Admission routes2
Has abstractyes

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