Evidence map›Paper›PMID 32406379›Full record

ArticleIET systems biology2020

Various skin impedance models based on physiological stratification.

Dhruba Jyoti Bora, Rajdeep Dasgupta

Abstract read
In one paragraph

Article in IET systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
  2. Review
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  4. Article
  5. Article
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  7. Article
  8. Detecting and monitoring incontinence associated dermatitis: Does impedance spectroscopy have a part to play?Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2024
    Review
  9. Review
  10. Article
  11. Article
  12. A review of bio-impedance devices.Medical & biological engineering & computing · 2023
    Review
  13. Article
  14. 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

2 authors.

Dhruba Jyoti BoraDepartment of Electronics and Instrumentation Engineering, NIT Silchar, Assam, Silchar, India. dhrubajyoti139@gmail.com.
Rajdeep DasguptaDepartment of Electronics and Instrumentation Engineering, NIT Silchar, Assam, Silchar, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transdermal drug delivery is a non-invasive method of drug administration. However, to achieve this, the drug has to pass through the complicated structure of the skin. The complex structure of skin can be modelled by an electrical equivalent circuit to calculate its impedance. In this work, the transfer function of three electrical models of the human skin (Montague, Tregear and Lykken Model) based on physiological stratification are analysed. Sensitivity analysis of these models is carried out to consider the extent to which changes in system parameters (different types of R and C as described by different models) affect the behaviour of the model. Techniques like normal of derivative and Hausdorff Distance is also used to study and understand the different curves. Comparison is also made with CPE based model. As Montague Model is the most widely used model, Tregear and Lykken Model are compared with it. It can be commented that out of the above observations Tregear Model at Level 3 can be used for establishing the electrical equivalent of human skin due to its simplicity. However, fractional ordered CPE models provide a good approximation. Future prospect lies in developing a model that characterize both biological properties and physiological stratification.

Indexed as

Models, BiologicalSkin Physiological PhenomenaElectric ImpedanceHumans

Identifiers

PMID32406379
PMCPMC8687402

What OpenQuestion holds

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Read underepoch 390

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.