Gupta, I;
Cassará, AM;
Tarotin, I;
Donega, M;
Miranda, JA;
Sokal, DM;
Ouchouche, S;
... Chew, DJ; + view all
(2020)
Quantification of clinically applicable stimulation parameters for precision near-organ neuromodulation of human splenic nerves.
Communications Biology
, 3
, Article 577. 10.1038/s42003-020-01299-0.
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Abstract
Neuromodulation is a new therapeutic pathway to treat inflammatory conditions by modulating the electrical signalling pattern of the autonomic connections to the spleen. However, targeting this sub-division of the nervous system presents specific challenges in translating nerve stimulation parameters. Firstly, autonomic nerves are typically embedded non-uniformly among visceral and connective tissues with complex interfacing requirements. Secondly, these nerves contain axons with populations of varying phenotypes leading to complexities for axon engagement and activation. Thirdly, clinical translational of methodologies attained using preclinical animal models are limited due to heterogeneity of the intra- and inter-species comparative anatomy and physiology. Here we demonstrate how this can be accomplished by the use of in silico modelling of target anatomy, and validation of these estimations through ex vivo human tissue electrophysiology studies. Neuroelectrical models are developed to address the challenges in translation of parameters, which provides strong input criteria for device design and dose selection prior to a first-in-human trial.
| Type: | Article |
|---|---|
| Title: | Quantification of clinically applicable stimulation parameters for precision near-organ neuromodulation of human splenic nerves |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s42003-020-01299-0 |
| Publisher version: | https://doi.org/10.1038/s42003-020-01299-0 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Computational models, Electrophysiology, Translational research |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10119809 |
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