A personalized FEM model for reproducible measurement of anti-inflammatory drugs in transdermal administration to knee.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
13 01 2022
Historique:
received: 22 08 2021
accepted: 08 11 2021
entrez: 14 1 2022
pubmed: 15 1 2022
medline: 1 3 2022
Statut: epublish

Résumé

A personalized model of the human knee for enhancing the inter-individual reproducibility of a measurement method for monitoring Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) after transdermal delivery is proposed. The model is based on the solution of Maxwell Equations in the electric-quasi-stationary limit via Finite Element Analysis. The dimensions of the custom geometry are estimated on the basis of knee circumference at the patella, body mass index, and sex of each individual. An optimization algorithm allows to find out the electrical parameters of each subject by experimental impedance spectroscopy data. Muscular tissues were characterized anisotropically, by extracting Cole-Cole equation parameters from experimental data acquired with twofold excitation, both transversal and parallel to tissue fibers. A sensitivity and optimization analysis aiming at reducing computational burden in model customization achieved a worst-case reconstruction error lower than 5%. The personalized knee model and the optimization algorithm were validated in vivo by an experimental campaign on thirty volunteers, 67% healthy and 33% affected by knee osteoarthritis (Kellgren-Lawrence grade ranging in [1,4]), with an average error of 3%.

Identifiants

pubmed: 35027630
doi: 10.1038/s41598-021-04718-2
pii: 10.1038/s41598-021-04718-2
pmc: PMC8758660
doi:

Substances chimiques

Anti-Inflammatory Agents, Non-Steroidal 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

673

Informations de copyright

© 2022. The Author(s).

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Auteurs

Pasquale Arpaia (P)

Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Interdepartmental Center for Research in Health Management and Innovation in Health (CIRMIS), University of Naples Federico II, Naples, Italy.

Federica Crauso (F)

Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.

Mirco Frosolone (M)

Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Department of Public Health, University of Naples Federico II, Naples, Italy.

Massimo Mariconda (M)

Department of Public Health, University of Naples Federico II, Naples, Italy.

Simone Minucci (S)

Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy. simone.minucci@unitus.it.
Department of Economics, Engineering, Society and Business Organization (DEIM), University of Tuscia, Viterbo, Italy. simone.minucci@unitus.it.

Nicola Moccaldi (N)

Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.

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Classifications MeSH