Evidence map›Paper›PMID 33291529›Full record

ArticleSensors (Basel, Switzerland)2020

Analytical Model for Blood Glucose Detection Using Electrical Impedance Spectroscopy.

Bruna Gabriela Pedro, David William Cordeiro Marcôndes, Pedro Bertemes-Filho

Open access · goldAbstract readLetter
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
5.9field-weighted citation impact, top 4% of its field
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

16 citing papers in PubMed, 43 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Noninvasive Glucose Sensing In Vivo.Sensors (Basel, Switzerland) · 2023
    Review
  12. Article
  13. Review
  14. Observational
  15. Review
  16. 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

3 authors at 1 institution in 1 country.

Bruna Gabriela PedroDepartamento de Engenharia Elétrica, Universidade do Estado de Santa Catarina, Joinville 89219-710, Santa Catarina, Brazil.
David William Cordeiro MarcôndesDepartamento de Engenharia Elétrica, Universidade do Estado de Santa Catarina, Joinville 89219-710, Santa Catarina, Brazil.
Pedro Bertemes-FilhoDepartamento de Engenharia Elétrica, Universidade do Estado de Santa Catarina, Joinville 89219-710, Santa Catarina, Brazil.ORCID 0000-0002-5264-4874
Universidade do Estado de Santa Catarina · BR

Funding

Universidade do Estado de Santa Catarina 092384469-46
6 · The paper itself

Abstract

Pathogens and adulterants in human feeding consumables can be readily identified according to their electrical properties. Electrical bioimpedance analysis (BIA) has been widely used for body contents characterization, such as blood, urine, lactate, and sweat. If the concentration of glucose in blood alters the electrical properties of the blood medium, then the impedance spectrum obtained by BIA can be used to measure glycemia. For some applications, artificial neural networks allow the correlation of these parameters both impedance and concentration of glucose by means of symbolic and statistical rules. According to our literature review, there is not any physical model that allows the interpretation of the relationship between blood's electrical properties from impedance spectra and the concentration of glucose in blood plasma. This article proposes a simplified physical model for blood electrical conductivity as a function of concentration of glucose, based on Bruggeman's effective medium theory. The equations of this model were obtained considering an insulating phase distribution diffused in a conductive matrix, in which red blood cells are represented by macroscopic insulating nuclei and glucose molecules by microscopic insulating particles. The impedance spectrum for different glucose concentrations (4.0 to 6.8 mmol/L) in a blood sample, published by Kamat Bagul (2014), were compared to the proposed model. The results showed a significant correlation with the experimental data, showing a maximum error of 5.2%. The proposed model might be useful in the design of noninvasive blood glucose monitoring systems.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringDielectric SpectroscopyElectric ConductivityElectric ImpedanceHumansBlood Glucoseanalytical modelblood glucoseimpedance spectroscopynoninvasive monitoring

Identifiers

PMID33291529
PMCPMC7731080
OpenAlexW3092736093

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.