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A glucose-responsive insulin delivery micro device embedded with nanohydrogel particles as “smart valves”

2011· article· en· W2131451252 on OpenAlexafffund
Jian Chen, Cláudia R. Gordijo, Michael Chu, Xiao Yu Wu, Yu Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsGlucose oxidaseMembraneInsulinDrug deliveryDiffusionNanoparticleMaterials scienceChemistryBiophysicsBiomedical engineeringInternal medicineNanotechnologyBiochemistryBiosensorMedicineBiology

Abstract

fetched live from OpenAlex

This paper presents a glucose-responsive micro device capable of modulating drug diffusion rates according to changes in environmental glucose levels. New glucose-responsive composite membranes with enhanced mechanical strength were developed, in which chemically immobilized glucose oxidase and pH-responsive hydrogel nanoparticles were embedded within PDMS micro grids, functioning as intelligent `nano-valves' in response to surrounding glucose concentration variations. Membrane responsive release profiles were quantified by testing model drug diffusion (bovine insulin), with an increase in release rates in response to increasing glucose levels. The composite membranes were integrated with PDMS drug reservoirs to form proof-of-concept devices. Release profiles of the glucose-responsive micro devices were also measured, demonstrating a marked increase in release rates of insulin when glucose levels in the surrounding media increased from 100 to 300 mg/dL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.212
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2011
Admission routes2
Has abstractyes

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