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Record W3095312696 · doi:10.3390/ijms21218114

Soft Template Electropolymerization of Polypyrrole for Improved pH-Induced Drug Delivery

2020· article· en· W3095312696 on OpenAlexafffund
Iryna Liubchak, Matthew T. Lawrence, F. Benjamin Holness, Aaron D. Price

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2020
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsPolypyrroleDrug deliveryPolymerPolymerizationNanotechnologyMaterials scienceDrugChemical engineeringChemistryOrganic chemistryPharmacology

Abstract

fetched live from OpenAlex

Chronic wounds are characterized by a localized pH change from acidic (healthy) to alkaline (unhealthy), which can be harnessed to act as a switch for drug release from a polymer medium covering the wound for improved healing. To realize this, a new polymer dressing material is needed to help heal chronic wounds. Polypyrrole (PPy) is a biocompatible electroactive polymer that has been proven as a successful drug delivery mechanism, but currently lacks the capacity for scalable clinical applications due to its poor processability. In this study, PPy films with and without microstructures were produced using electrochemical oxidation and subsequently doped with fluorescein, a model drug molecule. To increase the drug loading capacity, microstructures were created through soft template polymerization of pyrrole around hydrogen gas bubbles. Fluorescein release was measured using UV spectroscopy over a pH range of 2 to 11, showing increased release at higher pH values. Microstructured films showed an increased doping capacity compared to flat PPy films, attributed to the increase in drug incorporation sites. The pH-activated release mechanism was shown to be successful and can be applied as a pH-sensitive biosensor and drug delivery system in vitro.

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 score0.033
Threshold uncertainty score0.263

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 science0.0010.000
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 head0.022
GPT teacher head0.288
Teacher spread0.266 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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