Evidence map›Paper›PMID 35027630›Full record

ArticleScientific reports2022

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

Pasquale Arpaia, Federica Crauso, Mirco Frosolone, Massimo Mariconda, Simone Minucci, Nicola Moccaldi

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 52% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 1 institution in 1 country.

Pasquale ArpaiaLaboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Federica CrausoLaboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Mirco FrosoloneLaboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Massimo MaricondaDepartment of Public Health, University of Naples Federico II, Naples, Italy.
Simone MinucciLaboratory 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.
Nicola MoccaldiLaboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
University of Naples Federico II · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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%.

Indexed as

Administration, CutaneousAlgorithmsAnti-Inflammatory Agents, Non-SteroidalFemaleFinite Element AnalysisHumansKnee JointMaleModels, AnatomicOsteoarthritis, KneeReproducibility of ResultsAnti-Inflammatory Agents, Non-Steroidal

Identifiers

PMID35027630
PMCPMC8758660
OpenAlexW4220840853

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.