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Beatty R, Mendez KL, Schreiber LHJ, Tarpey R, Whyte W, Fan Y, Robinson ST, O'Dwyer J, Simpkin AJ, Tannian J, Dockery P, Dolan EB, Roche ET, Duffy GP. Soft robot-mediated autonomous adaptation to fibrotic capsule formation for improved drug delivery. Sci Robot 2023; 8:eabq4821. [PMID: 37647382 DOI: 10.1126/scirobotics.abq4821] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 08/02/2023] [Indexed: 09/01/2023]
Abstract
The foreign body response impedes the function and longevity of implantable drug delivery devices. As a dense fibrotic capsule forms, integration of the device with the host tissue becomes compromised, ultimately resulting in device seclusion and treatment failure. We present FibroSensing Dynamic Soft Reservoir (FSDSR), an implantable drug delivery device capable of monitoring fibrotic capsule formation and overcoming its effects via soft robotic actuations. Occlusion of the FSDSR porous membrane was monitored over 7 days in a rodent model using electrochemical impedance spectroscopy. The electrical resistance of the fibrotic capsule correlated to its increase in thickness and volume. Our FibroSensing membrane showed great sensitivity in detecting changes at the abiotic/biotic interface, such as collagen deposition and myofibroblast proliferation. The potential of the FSDSR to overcome fibrotic capsule formation and maintain constant drug dosing over time was demonstrated in silico and in vitro. Controlled closed loop release of methylene blue into agarose gels (with a comparable fold change in permeability relating to 7 and 28 days in vivo) was achieved by adjusting the magnitude and frequency of pneumatic actuations after impedance measurements by the FibroSensing membrane. By sensing fibrotic capsule formation in vivo, the FSDSR will be capable of probing and adapting to the foreign body response through dynamic actuation changes. Informed by real-time sensor signals, this device offers the potential for long-term efficacy and sustained drug dosing, even in the setting of fibrotic capsule formation.
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Affiliation(s)
- Rachel Beatty
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
- SFI Centre for Advanced Materials and BioEngineering Research (AMBER), Trinity College Dublin, Dublin, Ireland
| | - Keegan L Mendez
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lucien H J Schreiber
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
| | - Ruth Tarpey
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
- CÚRAM, Centre for Research in Medical Devices, University of Galway, Galway, Ireland
- Biomedical Engineering, School of Engineering, University of Galway, Galway, Ireland
| | - William Whyte
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yiling Fan
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Scott T Robinson
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
- SFI Centre for Advanced Materials and BioEngineering Research (AMBER), Trinity College Dublin, Dublin, Ireland
| | - Joanne O'Dwyer
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
| | - Andrew J Simpkin
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
| | - Joseph Tannian
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
| | - Peter Dockery
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
| | - Eimear B Dolan
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
- CÚRAM, Centre for Research in Medical Devices, University of Galway, Galway, Ireland
- Biomedical Engineering, School of Engineering, University of Galway, Galway, Ireland
| | - Ellen T Roche
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Duffy
- Anatomy and Regenerative Medicine Institute (REMEDI), School of Medicine, University of Galway, Galway, Ireland
- SFI Centre for Advanced Materials and BioEngineering Research (AMBER), Trinity College Dublin, Dublin, Ireland
- CÚRAM, Centre for Research in Medical Devices, University of Galway, Galway, Ireland
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Van Haeverbeke M, De Baets B, Stock M. Plant impedance spectroscopy: a review of modeling approaches and applications. Front Plant Sci 2023; 14:1187573. [PMID: 37588419 PMCID: PMC10426379 DOI: 10.3389/fpls.2023.1187573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/20/2023] [Indexed: 08/18/2023]
Abstract
Electrochemical impedance spectroscopy has emerged over the past decade as an efficient, non-destructive method to investigate various (eco-)physiological and morphological properties of plants. This work reviews the state-of-the-art of impedance spectra modeling for plant applications. In addition to covering the traditional, widely-used representations of electrochemical impedance spectra, we also consider the more recent machine-learning-based approaches.
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Affiliation(s)
- Maxime Van Haeverbeke
- Knowledge-Based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
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Flatscher M, Neumayer M, Bretterklieber T, Wegleiter H. Transmission Lines in Capacitance Measurement Systems: An Investigation of Receiver Structures. Sensors (Basel) 2023; 23:1148. [PMID: 36772186 PMCID: PMC9920718 DOI: 10.3390/s23031148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Dielectric sensing based on capacitive measurement technology is a favourable measurement approach in many industries and fields of application. From an electrical point of view, a coupling capacitance must be measured in the presence of stray capacitances. Different receiver circuit structures have been proposed for the underlying displacement current measurement. Ideally, the sensor assembly is directly connected to the sensor circuitry to minimize the influence with respect to these parasitic capacitances. However, under harsh operating conditions, e.g., at high temperatures, the sensor and the receiver circuit must be separated in order to protect the electronics. Consequently, the receiver circuit and the sensor have to be connected by cables, e.g., coaxial cables. The measurement setup differs significantly from the ideal design with a direct connection. In this paper, we investigate the behaviour of three common measurement circuits for capacitive measurements in instrumentations with cables. We study the interaction between the sensor and the electronics and analyse the operating behaviour of the circuit, as well as the operating states of the amplifiers used. We also address cross-sensitivities in the sensor design due to stray capacitances. The analyses are carried out for different cable lengths and measuring frequencies, and conditions for the usability of the circuit are deduced. In addition to the operational behaviour, we also evaluate the circuits by means of a noise analyses. Based on this analysis, we show a direct comparison of the circuits. The analysis is based on simulation studies, as well as collaborative measurements on test circuits where all circuit parameters are provided. The test circuits are realized with dedicated state-of-the-art circuit elements and, together with the analysis approach and the results, thus provide a basis for future developments.
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Affiliation(s)
- Matthias Flatscher
- Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33, 8010 Graz, Austria
| | - Markus Neumayer
- Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33, 8010 Graz, Austria
- Christian Doppler Laboratory for Measurement Systems for Harsh Operating Conditions, Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33, 8010 Graz, Austria
| | - Thomas Bretterklieber
- Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33, 8010 Graz, Austria
| | - Hannes Wegleiter
- Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33, 8010 Graz, Austria
- Christian Doppler Laboratory for Measurement Systems for Harsh Operating Conditions, Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33, 8010 Graz, Austria
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Pandeya SR, Nagy JA, Riveros D, Semple C, Taylor RS, Hu A, Sanchez B, Rutkove SB. Using machine learning algorithms to enhance the diagnostic performance of electrical impedance myography. Muscle Nerve 2022; 66:354-361. [PMID: 35727064 DOI: 10.1002/mus.27664] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 04/23/2022] [Accepted: 06/14/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION/AIMS We assessed the classification performance of machine learning (ML) using multifrequency electrical impedance myography (EIM) values to improve upon diagnostic outcomes as compared to those based on a single EIM value. METHODS EIM data was obtained from unilateral excised gastrocnemius in eighty diseased mice (26 D2-mdx, Duchenne muscular dystrophy model, 39 SOD1G93A ALS model, and 15 db/db, a model of obesity-induced muscle atrophy) and 33 wild-type (WT) animals. We assessed the classification performance of a ML random forest algorithm incorporating all the data (multifrequency resistance, reactance and phase values) comparing it to the 50 kHz phase value alone. RESULTS ML outperformed the 50 kHz analysis as based on receiver-operating characteristic curves and measurement of the area under the curve (AUC). For example, comparing all diseases together versus WT from the test set outputs, the AUC was 0.52 for 50 kHz phase, but was 0.94 for the ML model. Similarly, when comparing ALS versus WT, the AUCs were 0.79 for 50 kHz phase and 0.99 for ML. DISCUSSION Multifrequency EIM utilizing ML improves upon classification compared to that achieved with a single-frequency value. ML approaches should be considered in all future basic and clinical diagnostic applications of EIM.
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Affiliation(s)
- Sarbesh R Pandeya
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA
| | - Janice A Nagy
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA
| | - Daniela Riveros
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA
| | - Carson Semple
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA
| | - Rebecca S Taylor
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA
| | | | - Benjamin Sanchez
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah
| | - Seward B Rutkove
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA
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Liu Y, Li D, Qian J, Di B, Zhang G, Ren Z. Electrical impedance spectroscopy (EIS) in plant roots research: a review. Plant Methods 2021; 17:118. [PMID: 34774075 PMCID: PMC8590265 DOI: 10.1186/s13007-021-00817-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 11/01/2021] [Indexed: 05/06/2023]
Abstract
Nondestructive testing of plant roots is a hot topic in recent years. The traditional measurement process is time-consuming and laborious, and it is impossible to analyze the state of plant roots without destroying the sample. Recent studies have shown that as an excellent nondestructive measurement method, although electrical impedance spectroscopy (EIS) has made great achievements in many botanical research fields such as plant morphology and stress resistance, there are still limitations. This review summarizes the application of EIS in plant root measurement. The experiment scheme, instrument and electrode, excitation frequency range, root electrical characteristics, equivalent circuit, and combination of EIS and artificial intelligence (AI) are discussed. Furthermore, the review suggests that future research should focus on miniaturization of measurement equipment, standardization of planting environment and intelligentization of root diagnosis, so as to better apply EIS technology to in situ root nondestructive measurement.
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Affiliation(s)
- Yang Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, People's Republic of China
- Department of Computer Application Engineering, Hebei Software Institute, Baoding, 071000, China
| | - DongMing Li
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, People's Republic of China
| | - Ji Qian
- College of Horticulture, Hebei Agricultural University, Baoding, 071001, China
| | - Bao Di
- College of Horticulture, Hebei Agricultural University, Baoding, 071001, China
| | - Gang Zhang
- College of Horticulture, Hebei Agricultural University, Baoding, 071001, China
| | - ZhenHui Ren
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, People's Republic of China.
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Pal A, Biswas S, O Kare SP, Biswas P, Jana SK, Das S, Chaudhury K. Development of an impedimetric immunosensor for machine learning-based detection of endometriosis: A proof of concept. Sensors and Actuators B: Chemical 2021; 346:130460. [DOI: 10.1016/j.snb.2021.130460] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Xu Y, Jiang Y, Li C, Chen Y, Yang Y. Integration of an XGBoost model and EIS detection to determine the effect of low inhibitor concentrations on E. coli. J Electroanal Chem (Lausanne) 2020; 877:114534. [DOI: 10.1016/j.jelechem.2020.114534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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