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Record W4396955304 · doi:10.1002/adma.202313743

Wearable Aptalyzer Integrates Microneedle and Electrochemical Sensing for In Vivo Monitoring of Glucose and Lactate in Live Animals

2024· article· en· W4396955304 on OpenAlexafffund
‪Fatemeh Bakhshandeh, Hanjia Zheng, Nicole G. Barra, Sadegh Sadeghzadeh, Irfani R. Ausri, Payel Sen, Fatemeh Keyvani, Fasih A. Rahman, Joe Quadrilatero, Juewen Liu, Jonathan D. Schertzer, Leyla Soleymani, Mahla Poudineh

Bibliographic record

VenueAdvanced Materials · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsPopulation Health Research InstituteUniversity of WaterlooMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsAptamerAnalyteBiosensorWearable computerMaterials scienceBioanalysisContinuous monitoringContinuous glucose monitoringBiomedical engineeringNanotechnologyBiocompatible materialComputer scienceChromatographyEmbedded systemChemistryDiabetes mellitusType 1 diabetesMedicineBiology

Abstract

fetched live from OpenAlex

Continuous monitoring of clinically relevant biomarkers within the interstitial fluid (ISF) using microneedle (MN)-based assays, has the potential to transform healthcare. This study introduces the Wearable Aptalyzer, an integrated system fabricated by combining biocompatible hydrogel MN arrays for ISF extraction with an electrochemical aptamer-based biosensor for in situ monitoring of blood analytes. The use of aptamers enables continuous monitoring of a wide range of analytes, beyond what is possible with enzymatic monitoring. The Wearable Aptalyzer is used for real-time and multiplexed monitoring of glucose and lactate in ISF. Validation experiments using live mice and rat models of type 1 diabetes demonstrate strong correlation between the measurements collected from the Wearable Aptalyzer in ISF and those obtained from gold-standard techniques for blood glucose and lactate, for each analyte alone and in combination. The Wearable Aptalyzer effectively addresses the limitations inherent in enzymatic detection methods as well as solid MN biosensors and the need for reliable and multiplexed bioanalytical monitoring in vivo.

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 score0.005
Threshold uncertainty score0.710

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 head0.042
GPT teacher head0.393
Teacher spread0.351 · 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

Citations87
Published2024
Admission routes2
Has abstractyes

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