Evidence map›Paper›PMID 35043720›Full record

ArticleJournal of diabetes science and technology2023

Development of a Novel Insulin Sensor for Clinical Decision-Making.

Eva Vargas, Eleonora M Aiello, Jordan E Pinsker, Hazhir Teymourian, Farshad Tehrani, Mei Mei Church, Lori M Laffel, Francis J Doyle, Mary-Elizabeth Patti, Eyal Dassau and 1 more

Abstract read
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In one paragraph

Article in Journal of diabetes science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Eva VargasDepartment of Nanoengineering, University of California San Diego, La Jolla, CA, USA.
Eleonora M AielloHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, USA.ORCID 0000-0001-5129-8829
Jordan E PinskerSansum Diabetes Research Institute, Santa Barbara, CA, USA.ORCID 0000-0003-4080-9034
Hazhir TeymourianDepartment of Nanoengineering, University of California San Diego, La Jolla, CA, USA.
Farshad TehraniDepartment of Nanoengineering, University of California San Diego, La Jolla, CA, USA.
Mei Mei ChurchSansum Diabetes Research Institute, Santa Barbara, CA, USA.ORCID 0000-0003-3393-2042
Lori M LaffelJoslin Diabetes Center, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-9675-3001
Francis J DoyleHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, USA.
Mary-Elizabeth PattiJoslin Diabetes Center, Harvard Medical School, Boston, MA, USA.
Eyal DassauHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, USA.ORCID 0000-0001-5333-6892
Joseph WangDepartment of Nanoengineering, University of California San Diego, La Jolla, CA, USA.

Funding

SPECIAL ASSAY COREP30DK036836 · NIDDK · JOSLIN DIABETES CENTER · PI ROHIT N. KULKARNI · 1986 to 2026
$50.5M
NIDDK NIH HHS P30 DK036836
6 · The paper itself

Abstract

backgroundClinical decision support systems that incorporate information from frequent insulin measurements to enhance individualized diabetes management remain an unmet goal. The development of a disposable insulin strip for fast decentralized point-of-care detection replacing the current centralized lab-based methods used in clinical practice would be highly desirable to improve the establishment of individual insulin absorption patterns and algorithm modeling processes.

methodsWe carried out the development and optimization of a novel decentralized disposable insulin electrochemical sensor focusing on obtaining high analytical and operational performance toward achieving a true point-of-care insulin testing device for clinical on-site application.

resultsOur novel insulin immunosensor demonstrated an attractive performance and efficient user-friendly operation by providing high sensitivity capability to detect endogenous and analog insulin with a limit of detection of 30.2 pM (4.3 µiU/mL), rapid time-to-result, stability toward remote site application, and scalable low-cost fabrication with an estimated cost-of-goods for disposable consumables of below $5, capable of near real-time insulin detection in a microliter (≤10 µL) sample droplet of undiluted serum within 30 minutes.

conclusionsThe results obtained in the optimization and characterization of our novel insulin sensor illustrate its suitability for its potential application in remote clinical environments for frequent insulin monitoring. Future work will test the insulin sensor in a clinical research setting to assess its efficacy in individuals with type 1 diabetes.

Indexed as

Biosensing TechniquesInsulinClinical Decision-MakingHumansImmunoassayInsulin, Regular, HumanInsulinInsulin, Regular, Humanimmunosensorinsulin measurementpoint-of-caretype 1 diabetes

Identifiers

PMID35043720
PMCPMC10347992

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.