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Record W4399429474 · doi:10.1002/adma.202402009

Multifunctional Dopamine‐Based Hydrogel Microneedle Electrode for Continuous Ketone Sensing

2024· article· en· W4399429474 on OpenAlexafffund
Irfani R. Ausri, Sadegh Sadeghzadeh, Subhamoy Biswas, Hanjia Zheng, Peyman GhavamiNejad, Michelle Huynh, Fatemeh Keyvani, Erfan Shirzadi, Fasih A. Rahman, Joe Quadrilatero, Amin GhavamiNejad, Mahla Poudineh

Bibliographic record

VenueAdvanced Materials · 2024
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersBreakthrough T1D CanadaUniversity of Waterloo
KeywordsKetoneCatecholDopamineMaterials scienceCombinatorial chemistryChemistryOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Diabetic ketoacidosis (DKA), a severe complication of type 1 diabetes (T1D), is triggered by production of large quantities of ketone bodies, requiring patients with T1D to constantly monitor their ketone levels. Here, a skin-compatible hydrogel microneedle (HMN)-continuous ketone monitoring (HMN-CKM) device is reported. The sensing mechanism relies on the catechol-quinone chemistry inherent to the dopamine (DA) molecules that are covalently linked to the polymer structure of the HMN patch. The DA serves the dual-purpose of acting as a redox mediator for measuring the byproduct of oxidation of 3-beta-hydroxybutyrate (β-HB), the primary ketone bodies; while, also facilitating the formation of a crosslinked HMN patch. A universal approach involving pre-oxidation and detection of the generated catechol compounds is introduced to correlate the sensor response to the β-HB concentrations. It is further shown that real-time tracking of a decrease in ketone levels of T1D rat model is possible using the HMN-CKM device, in conjunction with a data-driven machine learning model that considers potential time delays.

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 score0.192
Threshold uncertainty score0.713

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 science0.0000.000
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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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

Citations38
Published2024
Admission routes2
Has abstractyes

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