Evidence map›Paper›PMID 35891020›Full record

ArticleSensors (Basel, Switzerland)2022

Microbe-Based Sensor for Long-Term Detection of Urine Glucose.

Dunzhu Li, Yunhong Shi, Yifan Sun, Zeena Wang, Daniel K Kehoe, Luis Romeral, Fei Gao, Luming Yang, David McCurtin, Yurii K Gun'ko and 2 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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

12 authors.

Dunzhu LiDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.
Yunhong ShiDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.
Yifan SunDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.ORCID 0000-0002-0347-9737
Zeena WangDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.
Daniel K KehoeAMBER Research Centre and Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Trinity College Dublin, D02 PN40 Dublin, Ireland.
Luis RomeralAMBER Research Centre and Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Trinity College Dublin, D02 PN40 Dublin, Ireland.
Fei GaoDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.
Luming YangDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.
David McCurtinDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.
Yurii K Gun'koSchool of Chemistry, Trinity College Dublin, D02 PN40 Dublin, Ireland.ORCID 0000-0002-4772-778X
Michael E G LyonsAMBER Research Centre and Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Trinity College Dublin, D02 PN40 Dublin, Ireland.ORCID 0000-0002-0320-7547
Liwen XiaoDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.

Funding

Enterprise Ireland CF-2021-1729-I
6 · The paper itself

Abstract

The development of a reusable and low-cost urine glucose sensor can benefit the screening and control of diabetes mellitus. This study focused on the feasibility of employing microbial fuel cells (MFC) as a selective glucose sensor for continuous monitoring of glucose levels in human urine. Using MFC technology, a novel cylinder sensor (CS) was developed. It had a quick response time (100 s), a large detection range (0.3-5 mM), and excellent accuracy. More importantly, the CS could last for up to 5 months. The selectivity of the CS was validated by both synthetic and actual diabetes-negative urine samples. It was found that the CS's selectivity could be significantly enhanced by adjusting the concentration of the culture's organic matter. The CS results were comparable to those of a commercial glucose meter (recovery ranged from 93.6% to 127.9%) when the diabetes-positive urine samples were tested. Due to the multiple advantages of high stability, low cost, and high sensitivity over urine test strips, the CS provides a novel and reliable approach for continuous monitoring of urine glucose, which will benefit diabetes assessment and control.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 1Blood Glucose Self-MonitoringGlucoseHumansInsulin Infusion SystemsBlood GlucoseGlucosediabetes pre-screeningglucose sensormicrobial fuel cellselectivityurine glucose

Identifiers

PMID35891020
PMCPMC9320042

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.