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Record W3017382256 · doi:10.1039/d0ra01382j

A simple and cost-effective approach to fabricate tunable length polymeric microneedle patches for controllable transdermal drug delivery

2020· article· en· W3017382256 on OpenAlexafffund
Yongli Chen, Yiwen Xian, Andrew Carrier, Brian Youden, Mark R. Servos, Shufen Cui, Tiangang Luan, Sujing Lin, Xu Zhang

Bibliographic record

VenueRSC Advances · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsRegional Municipality of WaterlooUniversity of WaterlooCape Breton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCape Breton UniversityShenzhen PolytechnicGuangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded SchemeBeatrice Hunter Cancer Research InstituteChina Postdoctoral Science FoundationCancer Research Institute
KeywordsTransdermalDrug deliverySimple (philosophy)Biomedical engineeringMaterials scienceNanotechnologyPharmacologyEngineeringMedicine

Abstract

fetched live from OpenAlex

Polymeric microneedles (MNs) are attractive transdermal drug delivery systems because of their efficient drug delivery and minimal invasiveness. Master template fabrication is the most time-consuming and costly step in producing polymeric MNs using a micromoulding approach. Herein, this issue is addressed by modifying tattoo needle cartridges by adjusting the volume of a PDMS spacer, thus streamlining polymeric MN fabrication and significantly reducing its manufacturing cost. Using the fabricated master template, dissolvable polymeric MN systems containing poly(vinyl pyrrolidone) (PVP) and poly(vinyl alcohol) (PVA) were developed. This MN system exhibits several advantages, including controllable MN length, uniform distribution of each needle, and controllable drug release profiles. Overall, polymeric MN fabrication using this method is inexpensive, simple, and yields controllable and effective transdermal drug delivery.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.065
GPT teacher head0.370
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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
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

Citations33
Published2020
Admission routes2
Has abstractyes

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