Evidence map›Paper›PMID 33652786›Full record

ArticleBiosensors2021

Non-Invasive Determination of Glucose Concentration Using a Near-Field Sensor.

Aleksandr Gorst, Kseniya Zavyalova, Aleksandr Mironchev

Abstract read
In one paragraph

Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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

3 authors.

Aleksandr GorstRadiophysics Faculty, Tomsk State University, 634050 Tomsk, Russia.ORCID 0000-0001-7081-8738
Kseniya ZavyalovaRadiophysics Faculty, Tomsk State University, 634050 Tomsk, Russia.ORCID 0000-0002-7109-9007
Aleksandr MironchevRadiophysics Faculty, Tomsk State University, 634050 Tomsk, Russia.

Funding

Russian Science Foundation 18-75-10101
6 · The paper itself

Abstract

The article presents a model of a near-field sensor for non-invasive glucose monitoring. The sensor has a specific design and forms a rather extended near-field. Due to this, the high penetration of electromagnetic waves into highly absorbing media (biologic media) is achieved. It represents a combined slot antenna based on a flexible RO3003 substrate. Moreover, it is small and rather flat (25 mm in diameter, 0.76 mm thick). These circumstances are the distinguishing features of this sensor in comparison with microwave sensors of other designs. The article presents the results of numerical modeling and experimental verification of a near-field sensor. Furthermore, a phantom of human biological media (human hand) was created for experimentation. In the case of numerical modeling, the sensor is located close to the hand model. In a full-scale experiment, it is located close to the phantom of the human hand for the maximum interaction of the near-field with biological materials. As a result of a series of measurements for this sensor, the reflection coefficient is measured, and the dependences of the reflected signal on the frequency are plotted. According to the results of the experiments carried out, there is a clear difference in glucose concentrations. At the same time, the accuracy of determining the difference in glucose concentrations is high. The values of the amplitude of the reflected signal with a change in concentration differ by 0.5-0.8 dB. This sensor can be used for developing a non-invasive blood glucose measurement procedure.

Indexed as

Biosensing TechniquesBlood GlucoseBlood Glucose Self-MonitoringEquipment DesignHumansMicrowavesBlood Glucosecombined slot antennadiabetesdielectric permeabilityelectromagnetic fieldsglucose concentrationmicrowave soundingnear-field sensornon-invasive measurementssensor

Identifiers

PMID33652786
PMCPMC7996804

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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.