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Rodoplu Solovchuk D. Advances in AI-assisted biochip technology for biomedicine. Biomed Pharmacother 2024; 177:116997. [PMID: 38943990 DOI: 10.1016/j.biopha.2024.116997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 06/13/2024] [Accepted: 06/15/2024] [Indexed: 07/01/2024] Open
Abstract
The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.
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Affiliation(s)
- Didem Rodoplu Solovchuk
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan.
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2
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Suh A, Ong J, Waisberg E, Lee AG. Neurostimulation as a technology countermeasure for dry eye syndrome in astronauts. LIFE SCIENCES IN SPACE RESEARCH 2024; 42:37-39. [PMID: 39067988 DOI: 10.1016/j.lssr.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 07/30/2024]
Abstract
Dry eye syndrome (DES) poses a significant challenge for astronauts during space missions, with reports indicating up to 30% of International Space Station (ISS) crew members. The microgravity environment of space alters fluid dynamics, affecting distribution of fluids on the surface of the eye as well as inducing cephalad fluid shifts that can alter tear drainage. Chronic and persistent DES not only impairs visual function, but also compromises the removal of debris, a heightened risk for corneal abrasions in the microgravity environment. Despite the availability of artificial tears on the ISS, the efficacy is challenged by altered fluid dynamics within the bottle and risks of contamination, thereby exacerbating the potential for corneal abrasions. In light of these challenges, there is a pressing need for innovative approaches to address DES in astronauts. Neurostimulation has emerged as a promising technology countermeasure for DES in spaceflight. By leveraging electrical signals to modulate neural function, neurostimulation offers a novel therapeutic avenue for managing DES symptoms. In this paper, we will explore the risk factors and current treatment modalities for DES, highlighting the limitations of existing approaches. Furthermore, we will delve into the novelty and potential of neurostimulation as a countermeasure for DES in future long-duration missions, including those to the Moon and Mars.
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Affiliation(s)
- Alex Suh
- Tulane University School of Medicine, New Orleans, LA, United States.
| | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan, United States
| | - Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, United Kingdom
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, Texas, United States; Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas, United States; The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, Texas, United States; Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, New York, United States; Department of Ophthalmology, University of Texas Medical Branch, Galveston, Texas, United States; University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Texas A&M College of Medicine, Texas, United States; Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
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3
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Dawit H, Zhao Y, Wang J, Pei R. Advances in conductive hydrogels for neural recording and stimulation. Biomater Sci 2024; 12:2786-2800. [PMID: 38682423 DOI: 10.1039/d4bm00048j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
The brain-computer interface (BCI) allows the human or animal brain to directly interact with the external environment through the neural interfaces, thus playing the role of monitoring, protecting, improving/restoring, enhancing, and replacing. Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. According to the electrode position, it can be divided into non-implantable, semi-implantable, and implantable. Among them, implantable neural electrodes can obtain the highest-quality electrophysiological information, so they have the most promising application. However, due to the chemo-mechanical mismatch between devices and tissues, the adverse foreign body response and performance loss over time seriously restrict the development and application of implantable neural electrodes. Given the challenges, conductive hydrogel-based neural electrodes have recently attracted much attention, owing to many advantages such as good mechanical match with the native tissues, negligible foreign body response, and minimal signal attenuation. This review mainly focuses on the current development of conductive hydrogels as a biocompatible framework for neural tissue and conductivity-supporting substrates for the transmission of electrical signals of neural tissue to speed up electrical regeneration and their applications in neural sensing and recording as well as stimulation.
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Affiliation(s)
- Hewan Dawit
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, PR China
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China.
| | - Yuewu Zhao
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China.
| | - Jine Wang
- College of Medicine and Nursing, Shandong Provincial Engineering Laboratory of Novel Pharmaceutical Excipients, Sustained and Controlled Release Preparations, Dezhou University, China.
- Jiangxi Institute of Nanotechnology, Nanchang, 330200, China
| | - Renjun Pei
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, PR China
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China.
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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Yogev D, Goldberg T, Arami A, Tejman-Yarden S, Winkler TE, Maoz BM. Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges. APL Bioeng 2023; 7:031506. [PMID: 37781727 PMCID: PMC10539032 DOI: 10.1063/5.0152290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.
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Affiliation(s)
| | | | | | | | | | - Ben M. Maoz
- Authors to whom correspondence should be addressed: and
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6
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Pham NT, Bunruangses M, Youplao P, Garhwal A, Ray K, Roy A, Boonkirdram S, Yupapin P, Jalil MA, Ali J, Kaiser S, Mahmud M, Mallik S, Zhao Z. An exploratory simulation study and prediction model on human brain behavior and activity using an integration of deep neural network and biosensor Rabi antenna. Heliyon 2023; 9:e15749. [PMID: 37305516 PMCID: PMC10256856 DOI: 10.1016/j.heliyon.2023.e15749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 06/13/2023] Open
Abstract
The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, and transmissions are connected via neurons. Communication signals are carried by electron spin (up and down) and adjustable Rabi frequency. Hidden variables and deep brain signals can be obtained by external detection. A Rabi antenna has been developed by simulation using computer simulation technology (CST) software. Additionally, a communication device has been developed that uses the Optiwave program with Finite-Difference Time-Domain (OptiFDTD). The output signal is plotted using the MATLAB program with the parameters of the OptiFDTD simulation results. The proposed antenna oscillates in the frequency range of 192 THz to 202 THz with a maximum gain of 22.4 dBi. The sensitivity of the sensor is calculated along with the result of electron spin and applied to form a human brain connection. Moreover, intelligent machine learning algorithms are proposed to identify high-quality transmissions and predict the behavior of transmissions in the near future. During the process, a root mean square error (RMSE) of 2.3332(±0.2338) was obtained. Finally, it can be said that our proposed model can efficiently predict human mind, thoughts, behavior as well as action/reaction, which can be greatly helpful in the diagnosis of various neuro-degenerative/psychological diseases (such as Alzheimer's, dementia, etc.) and for security purposes.
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Affiliation(s)
- Nhat Truong Pham
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Montree Bunruangses
- Department of Computer Engineering, Faculty of Industrial Education, Rajamangala University of Technology Phra Nakhon, Bangkok 10300, Thailand
| | - Phichai Youplao
- Department of Electrical Engineering, Faculty of Industry and Technology, Rajamangala University of Technology Isan Sakon Nakhon Campus, 199 Village no. 3, Phungkon, Sakon Nakhon 47160, Thailand
| | - Anita Garhwal
- Asia Metropolitan University, 6, Jalan Lembah, Bandar Baru Seri Alam 81750, Masai, Johor, Malaysia
| | - Kanad Ray
- Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, India
- Facultad de CienciasFisico-Matematicas, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y AV. 18 sur, Col. San Manuel Ciudad Universitaria, Pueble Pue. 72570, Mexico
- Faubert Lab, Ecole d'optométrie, Université de Montréal, Montréal, QC H3T1P1, Canada
| | - Arup Roy
- School of Computing and Information Technology, Reva University, Bengaluru, Karnataka 560064, India
| | - Sarawoot Boonkirdram
- Program of Electrical and Electronics, Faculty of Industrial Technology, Sakon Nakhon Rajabhat University, 680 Nittayo, Mueang, Sakon Nakhon 47000, Thailand
| | - Preecha Yupapin
- Department of Electrical Technology, School of Industrial Technology, Sakonnakhon Technical College, Institute of Vocational Education Northeastern 2, Sakonnakhon 47000, Thailand
| | - Muhammad Arif Jalil
- Department of Physics, Faculty of Science, Unversiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
| | - Jalil Ali
- Department of Electrical Engineering, Faculty of Industry and Technology, Rajamangala University of Technology Isan Sakon Nakhon Campus, 199 Village no. 3, Phungkon, Sakon Nakhon 47160, Thailand
| | - Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Mufti Mahmud
- Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, United Kingdom
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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7
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Shen K, Chen O, Edmunds JL, Piech DK, Maharbiz MM. Translational opportunities and challenges of invasive electrodes for neural interfaces. Nat Biomed Eng 2023; 7:424-442. [PMID: 37081142 DOI: 10.1038/s41551-023-01021-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/15/2023] [Indexed: 04/22/2023]
Abstract
Invasive brain-machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.
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Affiliation(s)
- Konlin Shen
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA.
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
| | - Oliver Chen
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Jordan L Edmunds
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - David K Piech
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Michel M Maharbiz
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
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8
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Al Abed A, Wei Y, Almasri RM, Lei X, Wang H, Firth J, Chen Y, Gouailhardou N, Silvestri L, Lehmann T, Ladouceur F, Lovell NH. Liquid crystal electro-optical transducers for electrophysiology sensing applications. J Neural Eng 2022; 19. [DOI: 10.1088/1741-2552/ac8ed6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/01/2022] [Indexed: 11/06/2022]
Abstract
Abstract
Objective. Biomedical instrumentation and clinical systems for electrophysiology rely on electrodes and wires for sensing and transmission of bioelectric signals. However, this electronic approach constrains bandwidth, signal conditioning circuit designs, and the number of channels in invasive or miniature devices. This paper demonstrates an alternative approach using light to sense and transmit the electrophysiological signals. Approach. We develop a sensing, passive, fluorophore-free optrode based on the birefringence property of liquid crystals (LCs) operating at the microscale. Main results. We show that these optrodes can have the appropriate linearity (µ ± s.d.: 99.4 ± 0.5%, n = 11 devices), relative responsivity (µ ± s.d.: 57 ± 12%V−1, n = 5 devices), and bandwidth (µ ± s.d.: 11.1 ± 0.7 kHz, n = 7 devices) for transducing electrophysiology signals into the optical domain. We report capture of rabbit cardiac sinoatrial electrograms and stimulus-evoked compound action potentials from the rabbit sciatic nerve. We also demonstrate miniaturisation potential by fabricating multi-optrode arrays, by developing a process that automatically matches each transducer element area with that of its corresponding biological interface. Significance. Our method of employing LCs to convert bioelectric signals into the optical domain will pave the way for the deployment of high-bandwidth optical telecommunications techniques in ultra-miniature clinical diagnostic and research laboratory neural and cardiac interfaces.
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Leva F, Palestri P, Selmi L. Multiscale simulation analysis of passive and active micro/nanoelectrodes for CMOS-based in vitro neural sensing devices. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210013. [PMID: 35658681 DOI: 10.1098/rsta.2021.0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/14/2021] [Indexed: 06/15/2023]
Abstract
Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for in vitro studies fabricated with modern (CMOS-based) micro-nano technology, this paper presents a mixed-mode device-circuit numerical-analytical multiscale and multiphysics simulation methodology to describe the neuron-sensor coupling, suitable to derive useful design guidelines. A few representative structures and coupling conditions are analysed in more detail in terms of the most relevant electrical figures of merit including signal-to-noise ratio. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.
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Affiliation(s)
- Federico Leva
- Dipartimento di ingegneria Enzo Ferrari, University of Modena and Reggio Emilia, Modena, Italy
| | - Pierpaolo Palestri
- Polytechnical Department of Engineering and Architecture, University of Udine, Udine, Italy
| | - Luca Selmi
- Dipartimento di ingegneria Enzo Ferrari, University of Modena and Reggio Emilia, Modena, Italy
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Sevcencu C. Single-interface bioelectronic medicines - concept, clinical applications and preclinical data. J Neural Eng 2022; 19. [PMID: 35533654 DOI: 10.1088/1741-2552/ac6e08] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 05/08/2022] [Indexed: 11/12/2022]
Abstract
Presently, large groups of patients with various diseases are either intolerant, or irresponsive to drug therapies and also intractable by surgery. For several diseases, one option which is available for such patients is the implantable neurostimulation therapy. However, lacking closed-loop control and selective stimulation capabilities, the present neurostimulation therapies are not optimal and are therefore used as only "third" therapeutic options when a disease cannot be treated by drugs or surgery. Addressing those limitations, a next generation class of closed-loop controlled and selective neurostimulators generically named bioelectronic medicines seems within reach. A sub-class of such devices is meant to monitor and treat impaired functions by intercepting, analyzing and modulating neural signals involved in the regulation of such functions using just one neural interface for those purposes. The primary objective of this review is to provide a first broad perspective on this type of single-interface devices for bioelectronic therapies. For this purpose, the concept, clinical applications and preclinical studies for further developments with such devices are here analyzed in a narrative manner.
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Affiliation(s)
- Cristian Sevcencu
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, Cluj-Napoca, 400293, ROMANIA
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11
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Nie B, Liu S, Qu Q, Zhang Y, Zhao M, Liu J. Bio-inspired flexible electronics for smart E-skin. Acta Biomater 2022; 139:280-295. [PMID: 34157454 DOI: 10.1016/j.actbio.2021.06.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/06/2021] [Accepted: 06/09/2021] [Indexed: 01/11/2023]
Abstract
"Learning from nature" provides endless inspiration for scientists to invent new materials and devices. Here, we review state-of-the-art technologies in flexible electronics, with a focus on bio-inspired smart skins. This review focuses on the development of E-skin for sensing a variety of parameters such as mechanical loads, temperature, light, and biochemical cues, with a trend of increased integration of multiple functions. It highlights the most recent advances in flexible electronics inspired by animals such as chameleons, squids, and octopi whose bodies have remarkable camouflage, mimicry, or self-healing attributes. Implantable devices, being overlapped with smart E-skin in a broad sense, are included in this review. This review outlines the remaining challenges in flexible electronics and the prospects for future development for biomedical applications. STATEMENT OF SIGNIFICANCE: This article reviews the state-of-the-art technologies of bio-inspired smart electronic skin (E-skin) developed in a "learning-mimicking-creating" (LMC) cycle. We emphasize the most recent innovations in the development of E-skin for sensing physical changes and biochemical cues, and for integrating multiple sensing modalities. We discuss the achievements in implantable materials, wireless communication, and device design pertaining to implantable flexible electronics. This review will provide prospective insights integrating material, electronics, and mechanical engineering viewpoints to foster new ideas for next-generation smart E-skin.
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Affiliation(s)
- Baoqing Nie
- School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, 215123, China
| | - Sidi Liu
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, China
| | - Qing Qu
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, China
| | - Yiqiu Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, China
| | - Mengying Zhao
- School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, 215123, China
| | - Jian Liu
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, China.
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Richter B, Mace Z, Hays ME, Adhikari S, Pham HQ, Sclabassi RJ, Kolber B, Yerneni SS, Campbell P, Cheng B, Tomycz N, Whiting DM, Le TQ, Nelson TL, Averick S. Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation. SENSORS (BASEL, SWITZERLAND) 2021; 21:7581. [PMID: 34833660 PMCID: PMC8619502 DOI: 10.3390/s21227581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/03/2021] [Accepted: 11/07/2021] [Indexed: 12/11/2022]
Abstract
Advancements in electrode technologies to both stimulate and record the central nervous system's electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging methods to interface with neural tissue. These electrodes may also degrade, resulting in additional invasive procedures. Furthermore, metal electrodes may cause nerve damage due to their inherent rigidity. This paper demonstrates that novel electrically conductive organic fibers (ECFs) can be used for direct nerve stimulation. The ECFs were prepared using a standard polyester material as the structural base, with a carbon nanotube ink applied to the surface as the electrical conductor. We report on three experiments: the first one to characterize the conductive properties of the ECFs; the second one to investigate the fiber cytotoxic properties in vitro; and the third one to demonstrate the utility of the ECF for direct nerve stimulation in an in vivo rodent model.
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Affiliation(s)
- Bertram Richter
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
| | - Zachary Mace
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
- Computational Diagnostics, Inc., Pittsburgh, PA 15213, USA
| | - Megan E. Hays
- Department of Chemistry, Oklahoma State University, Stillwater, OK 74078, USA; (M.E.H.); (S.A.); (T.L.N.)
| | - Santosh Adhikari
- Department of Chemistry, Oklahoma State University, Stillwater, OK 74078, USA; (M.E.H.); (S.A.); (T.L.N.)
| | - Huy Q. Pham
- Department of Biomedical Engineering, North Dakota State University, Fargo, ND 58102, USA;
| | - Robert J. Sclabassi
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
- Computational Diagnostics, Inc., Pittsburgh, PA 15213, USA
| | - Benedict Kolber
- Department of Neuroscience, University of Texas at Dallas, Richardson, TX 75080, USA;
| | - Saigopalakrishna S. Yerneni
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15217, USA; (S.S.Y.); (P.C.)
| | - Phil Campbell
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15217, USA; (S.S.Y.); (P.C.)
| | - Boyle Cheng
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
| | - Nestor Tomycz
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
| | - Donald M. Whiting
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
| | - Trung Q. Le
- Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58102, USA
| | - Toby L. Nelson
- Department of Chemistry, Oklahoma State University, Stillwater, OK 74078, USA; (M.E.H.); (S.A.); (T.L.N.)
| | - Saadyah Averick
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
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Kaiju T, Inoue M, Hirata M, Suzuki T. High-density mapping of primate digit representations with a 1152-channel µECoG array. J Neural Eng 2021; 18. [PMID: 33530064 DOI: 10.1088/1741-2552/abe245] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/02/2021] [Indexed: 12/11/2022]
Abstract
Objective.Advances in brain-machine interfaces (BMIs) are expected to support patients with movement disorders. Electrocorticogram (ECoG) measures electrophysiological activities over a large area using a low-invasive flexible sheet placed on the cortex. ECoG has been considered as a feasible signal source of the clinical BMI device. To capture neural activities more precisely, the feasibility of higher-density arrays has been investigated. However, currently, the number of electrodes is limited to approximately 300 due to wiring difficulties, device size, and system costs.Approach.We developed a high-density recording system with a large coverage (14 × 7 mm2) and using 1152 electrodes by directly integrating dedicated flexible arrays with the neural-recording application-specific integrated circuits and their interposers.Main results.Comparative experiments with a 128-channel array demonstrated that the proposed device could delineate the entire digit representation of a nonhuman primate. Subsampling analysis revealed that higher-amplitude signals can be measured using higher-density arrays.Significance.We expect that the proposed system that simultaneously establishes large-scale sampling, high temporal-precision of electrophysiology, and high spatial resolution comparable to optical imaging will be suitable for next-generation brain-sensing technology.
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Affiliation(s)
- Taro Kaiju
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka, Japan
| | - Masato Inoue
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka, Japan.,Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masayuki Hirata
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka, Japan.,Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Osaka, Japan
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14
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Cai L, Gutruf P. Soft, Wireless and subdermally implantable recording and neuromodulation tools. J Neural Eng 2021; 18. [PMID: 33607646 DOI: 10.1088/1741-2552/abe805] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 02/19/2021] [Indexed: 12/14/2022]
Abstract
Progress in understanding neuronal interaction and circuit behavior of the central and peripheral nervous system strongly relies on the advancement of tools that record and stimulate with high fidelity and specificity. Currently, devices used in exploratory research predominantly utilize cables or tethers to provide pathways for power supply, data communication, stimulus delivery and recording, which constrains the scope and use of such devices. In particular, the tethered connection, mechanical mismatch to surrounding soft tissues and bones frustrate the interface leading to irritation and limitation of motion of the subject, which in the case of fundamental and preclinical studies, impacts naturalistic behaviors of animals and precludes the use in experiments involving social interaction and ethologically relevant three-dimensional environments, limiting the use of current tools to mostly rodents and exclude species such as birds and fish. This review explores the current state-of-the-art in wireless, subdermally implantable tools that quantitively expand capabilities in analysis and perturbation of the central and peripheral nervous system by removing tethers and externalized features of implantable neuromodulation and recording tools. Specifically, the review explores power harvesting strategies, wireless communication schemes, and soft materials and mechanics that enable the creation of such devices and discuss their capabilities in the context of freely-behaving subjects. Highlights of this class of devices includes wireless battery-free and fully implantable operation with capabilities in cell specific recording, multimodal neural stimulation and electrical, optogenetic and pharmacological neuromodulation capabilities. We conclude with discussion on translation of such technologies which promises routes towards broad dissemination.
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Affiliation(s)
- Le Cai
- Biomedical Engineering, University of Arizona, 1230 N Cherry Ave., Tucson, Arizona, 85719, UNITED STATES
| | - Philipp Gutruf
- Biomedical Engineering, University of Arizona, 1230 N Cherry Ave., Tucson, Arizona, 85719, UNITED STATES
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15
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Wu Y, Chen H, Guo L. Opportunities and dilemmas of in vitro nano neural electrodes. RSC Adv 2020; 10:187-200. [PMID: 35492533 PMCID: PMC9047985 DOI: 10.1039/c9ra08917a] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 12/04/2019] [Indexed: 01/05/2023] Open
Abstract
Developing electrophysiological platforms to capture electrical activities of neurons and exert modulatory stimuli lays the foundation for many neuroscience-related disciplines, including the neuron–machine interface, neuroprosthesis, and mapping of brain circuitry.
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Affiliation(s)
- Yu Wu
- Department of Electrical and Computer Engineering
- The Ohio State University
- Columbus
- USA
| | - Haowen Chen
- Department of Electrical and Computer Engineering
- The Ohio State University
- Columbus
- USA
| | - Liang Guo
- Department of Electrical and Computer Engineering
- The Ohio State University
- Columbus
- USA
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