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Dong H, Lin J, Tao Y, Jia Y, Sun L, Li WJ, Sun H. AI-enhanced biomedical micro/nanorobots in microfluidics. LAB ON A CHIP 2024; 24:1419-1440. [PMID: 38174821 DOI: 10.1039/d3lc00909b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.
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Affiliation(s)
- Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Jiawen Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
| | - Yihui Tao
- Department of Automation Control and System Engineering, University of Sheffield, Sheffield, UK
| | - Yuan Jia
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China
| | - Lining Sun
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Wen Jung Li
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
| | - Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- Research Center of Aerospace Mechanism and Control, Harbin Institute of Technology, Harbin, China
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Sun A, Li Y, Zhu P, He X, Jiang Z, Kong Y, Liu C, Wang S. Dual-view transport of intensity phase imaging flow cytometry. BIOMEDICAL OPTICS EXPRESS 2023; 14:5199-5207. [PMID: 37854577 PMCID: PMC10581798 DOI: 10.1364/boe.504863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 09/03/2023] [Indexed: 10/20/2023]
Abstract
In this work, we design multi-parameter phase imaging flow cytometry based on dual-view transport of intensity (MPFC), which integrates phase imaging and microfluidics to a microscope, to obtain single-shot quantitative phase imaging on cells flowing in the microfluidic channel. The MPFC system has been proven with simple configuration, accurate phase retrieval, high imaging contrast, and real-time imaging and has been successfully employed not only in imaging, recognizing, and analyzing the flowing cells even with high-flowing velocities but also in tracking cell motilities, including rotation and binary rotation. Current results suggest that our proposed MPFC provides an effective tool for imaging and analyzing cells in microfluidics and can be potentially used in both fundamental and clinical studies.
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Affiliation(s)
- Aihui Sun
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yaxi Li
- Radiology Department, Jiangnan University Medical Center, Wuxi, Jiangsu, 214122, China
| | - Pengfei Zhu
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Xiaoliang He
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Zhilong Jiang
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yan Kong
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
- Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - Cheng Liu
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
- Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - Shouyu Wang
- Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System & School of Electronics and Information Engineering, OptiX+ Laboratory, Wuxi University, Wuxi, Jiangsu 214105, China
- Single Molecule Nanometry Laboratory, China
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Kokabi M, Tahir MN, Singh D, Javanmard M. Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis. BIOSENSORS 2023; 13:884. [PMID: 37754118 PMCID: PMC10526782 DOI: 10.3390/bios13090884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.
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Affiliation(s)
| | | | | | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA; (M.K.); (M.N.T.); (D.S.)
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Murai T, Matsuda S. Integrated Multimodal Omics and Dietary Approaches for the Management of Neurodegeneration. EPIGENOMES 2023; 7:20. [PMID: 37754272 PMCID: PMC10529483 DOI: 10.3390/epigenomes7030020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/26/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
Neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are caused by a combination of multiple events that damage neuronal function. A well-characterized biomarker of neurodegeneration is the accumulation of proteinaceous aggregates in the brain. However, the gradually worsening symptoms of neurodegenerative diseases are unlikely to be solely due to the result of a mutation in a single gene, but rather a multi-step process involving epigenetic changes. Recently, it has been suggested that a fraction of epigenetic alternations may be correlated to neurodegeneration in the brain. Unlike DNA mutations, epigenetic alterations are reversible, and therefore raise the possibilities for therapeutic intervention, including dietary modifications. Additionally, reactive oxygen species may contribute to the pathogenesis of Alzheimer's disease and Parkinson's disease through epigenetic alternation. Given that the antioxidant properties of plant-derived phytochemicals are likely to exhibit pleiotropic effects against ROS-mediated epigenetic alternation, dietary intervention may be promising for the management of neurodegeneration in these diseases. In this review, the state-of-the-art applications using single-cell multimodal omics approaches, including epigenetics, and dietary approaches for the identification of novel biomarkers and therapeutic approaches for the treatment of neurodegenerative diseases are discussed.
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Affiliation(s)
- Toshiyuki Murai
- Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita 565-0871, Japan;
| | - Satoru Matsuda
- Department of Food Science and Nutrition, Nara Women’s University, Kita-Uoya Nishimachi, Nara 630-8506, Japan
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Gao Z, Li Y. Enhancing single-cell biology through advanced AI-powered microfluidics. BIOMICROFLUIDICS 2023; 17:051301. [PMID: 37799809 PMCID: PMC10550334 DOI: 10.1063/5.0170050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/23/2023] [Indexed: 10/07/2023]
Abstract
Microfluidic technology has largely benefited both fundamental biological research and translational clinical diagnosis with its advantages in high-throughput, single-cell resolution, high integrity, and wide-accessibility. Despite the merits we obtained from microfluidics in the last two decades, the current requirement of intelligence in biomedicine urges the microfluidic technology to process biological big data more efficiently and intelligently. Thus, the current readout technology based on the direct detection of the signals in either optics or electrics was not able to meet the requirement. The implementation of artificial intelligence (AI) in microfluidic technology matches up with the large-scale data usually obtained in the high-throughput assays of microfluidics. At the same time, AI is able to process the multimodal datasets obtained from versatile microfluidic devices, including images, videos, electric signals, and sequences. Moreover, AI provides the microfluidic technology with the capability to understand and decipher the obtained datasets rather than simply obtaining, which eventually facilitates fundamental and translational research in many areas, including cell type discovery, cell signaling, single-cell genetics, and diagnosis. In this Perspective, we will highlight the recent advances in employing AI for single-cell biology and present an outlook on the future direction with more advanced AI algorithms.
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Affiliation(s)
- Zhaolong Gao
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics—Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Systems Biology Theme, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yiwei Li
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics—Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Systems Biology Theme, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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Wang Y, Huang Z, Wang X, Yang F, Yao X, Pan T, Li B, Chu J. Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms. LAB ON A CHIP 2023; 23:3615-3627. [PMID: 37458395 DOI: 10.1039/d3lc00194f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion blur in cell images, making it challenging to identify cell types from the raw images. In this study, we present a real-time single-cell imaging and classification system based on a fluorescence microscope and deep learning algorithm, which is able to directly identify cell types from motion-blur images. To obtain annotated datasets of blurred images for deep learning model training, we developed a motion deblurring algorithm for the reconstruction of blur-free images. To demonstrate the ability of this system, deblurred images of HeLa cells with various fluorescent labels and HeLa cells at different cell cycle stages were acquired. The trained ResNet achieved a high accuracy of 96.6% for single-cell classification of HeLa cells in three different mitotic stages, with a short processing time of only 2 ms. This technology provides a simple way to realize single-cell fluorescence IFC and real-time cell classification, offering significant potential in various biological and medical applications.
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Affiliation(s)
- Yiming Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Ziwei Huang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Xiaojie Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Fengrui Yang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China School of Life Sciences, Hefei, 230026, China
| | - Xuebiao Yao
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China School of Life Sciences, Hefei, 230026, China
| | - Tingrui Pan
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, China
| | - Baoqing Li
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Jiaru Chu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
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Dudaie M, Barnea I, Nissim N, Shaked NT. On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning. Sci Rep 2023; 13:12370. [PMID: 37524884 PMCID: PMC10390541 DOI: 10.1038/s41598-023-38160-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/04/2023] [Indexed: 08/02/2023] Open
Abstract
We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.
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Affiliation(s)
- Matan Dudaie
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Itay Barnea
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Noga Nissim
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Natan T Shaked
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel.
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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Dedeloudi A, Weaver E, Lamprou DA. Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems. Int J Pharm 2023; 636:122818. [PMID: 36907280 DOI: 10.1016/j.ijpharm.2023.122818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/23/2023] [Accepted: 03/06/2023] [Indexed: 03/13/2023]
Abstract
A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been introduced to Additive Manufacturing (AM) and Microfluidics (MFs) to predict and generate learning patterns for precise fabrication of tailor-made pharmaceutical treatments. Moreover, regarding the diversity and complexity of personalised medicine, ML has been part of quality by design strategy, targeting towards the development of safe and effective drug delivery systems. The utilisation of different and novel ML techniques along with Internet of Things sensors in AM and MFs, have shown promising aspects regarding the development of well-defined automated procedures towards the production of sustainable and quality-based therapeutic systems. Thus, the effective data utilisation, prospects on a flexible and broader production of "on demand" treatments. In this study, a thorough overview has been achieved, concerning scientific achievements of the past decade, which aims to trigger the research interest on incorporating different types of ML in AM and MFs, as essential techniques for the enhancement of quality standards of customised medicinal applications, as well as the reduction of variability potency, throughout a pharmaceutical process.
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Affiliation(s)
- Aikaterini Dedeloudi
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Edward Weaver
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Dimitrios A Lamprou
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK.
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Siu DMD, Lee KCM, Chung BMF, Wong JSJ, Zheng G, Tsia KK. Optofluidic imaging meets deep learning: from merging to emerging. LAB ON A CHIP 2023; 23:1011-1033. [PMID: 36601812 DOI: 10.1039/d2lc00813k] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.
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Affiliation(s)
- Dickson M D Siu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Bob M F Chung
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Justin S J Wong
- Conzeb Limited, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Guoan Zheng
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
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Boosting the Clinical Translation of Organ-on-a-Chip Technology. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9100549. [PMID: 36290517 PMCID: PMC9598310 DOI: 10.3390/bioengineering9100549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/02/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022]
Abstract
Organ-on-a-chip devices have become a viable option for investigating critical physiological events and responses; this technology has matured substantially, and many systems have been reported for disease modeling or drug screening over the last decade. Despite the wide acceptance in the academic community, their adoption by clinical end-users is still a non-accomplished promise. The reasons behind this difficulty can be very diverse but most likely are related to the lack of predictive power, physiological relevance, and reliability necessary for being utilized in the clinical area. In this Perspective, we briefly discuss the main attributes of organ-on-a-chip platforms in academia and how these characteristics impede their easy translation to the clinic. We also discuss how academia, in conjunction with the industry, can contribute to boosting their adoption by proposing novel design concepts, fabrication methods, processes, and manufacturing materials, improving their standardization and versatility, and simplifying their manipulation and reusability.
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