1
|
Ma X, Guo G, Wu X, Wu Q, Liu F, Zhang H, Shi N, Guan Y. Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems. MICROMACHINES 2023; 14:mi14050972. [PMID: 37241596 DOI: 10.3390/mi14050972] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/20/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023]
Abstract
Microfluidics attracts much attention due to its multiple advantages such as high throughput, rapid analysis, low sample volume, and high sensitivity. Microfluidics has profoundly influenced many fields including chemistry, biology, medicine, information technology, and other disciplines. However, some stumbling stones (miniaturization, integration, and intelligence) strain the development of industrialization and commercialization of microchips. The miniaturization of microfluidics means fewer samples and reagents, shorter times to results, and less footprint space consumption, enabling a high throughput and parallelism of sample analysis. Additionally, micro-size channels tend to produce laminar flow, which probably permits some creative applications that are not accessible to traditional fluid-processing platforms. The reasonable integration of biomedical/physical biosensors, semiconductor microelectronics, communications, and other cutting-edge technologies should greatly expand the applications of current microfluidic devices and help develop the next generation of lab-on-a-chip (LOC). At the same time, the evolution of artificial intelligence also gives another strong impetus to the rapid development of microfluidics. Biomedical applications based on microfluidics normally bring a large amount of complex data, so it is a big challenge for researchers and technicians to analyze those huge and complicated data accurately and quickly. To address this problem, machine learning is viewed as an indispensable and powerful tool in processing the data collected from micro-devices. In this review, we mainly focus on discussing the integration, miniaturization, portability, and intelligence of microfluidics technology.
Collapse
Affiliation(s)
- Xingfeng Ma
- School of Communication and Information Engineering, Shanghai University, Shanghai 200000, China
- Department of Microelectronics, Shanghai University, Shanghai 200000, China
| | - Gang Guo
- Department of Microelectronics, Shanghai University, Shanghai 200000, China
| | - Xuanye Wu
- Department of Microelectronics, Shanghai University, Shanghai 200000, China
- Shanghai Industrial μTechnology Research Institute, Shanghai 200000, China
| | - Qiang Wu
- Shanghai Aure Technology Limited Company, Shanghai 200000, China
| | - Fangfang Liu
- Shanghai Industrial μTechnology Research Institute, Shanghai 200000, China
| | - Hua Zhang
- Shanghai Aure Technology Limited Company, Shanghai 200000, China
| | - Nan Shi
- Shanghai Industrial μTechnology Research Institute, Shanghai 200000, China
- Institute of Translational Medicine, Shanghai University, Shanghai 200000, China
| | - Yimin Guan
- Department of Microelectronics, Shanghai University, Shanghai 200000, China
- Shanghai Aure Technology Limited Company, Shanghai 200000, China
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Recent advances in non-optical microfluidic platforms for bioparticle detection. Biosens Bioelectron 2023; 222:114944. [PMID: 36470061 DOI: 10.1016/j.bios.2022.114944] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 12/03/2022]
Abstract
The effective analysis of the basic structure and functional information of bioparticles are of great significance for the early diagnosis of diseases. The synergism between microfluidics and particle manipulation/detection technologies offers enhanced system integration capability and test accuracy for the detection of various bioparticles. Most microfluidic detection platforms are based on optical strategies such as fluorescence, absorbance, and image recognition. Although optical microfluidic platforms have proven their capabilities in the practical clinical detection of bioparticles, shortcomings such as expensive components and whole bulky devices have limited their practicality in the development of point-of-care testing (POCT) systems to be used in remote and underdeveloped areas. Therefore, there is an urgent need to develop cost-effective non-optical microfluidic platforms for bioparticle detection that can act as alternatives to optical counterparts. In this review, we first briefly summarise passive and active methods for bioparticle manipulation in microfluidics. Then, we survey the latest progress in non-optical microfluidic strategies based on electrical, magnetic, and acoustic techniques for bioparticle detection. Finally, a perspective is offered, clarifying challenges faced by current non-optical platforms in developing practical POCT devices and clinical applications.
Collapse
|
4
|
Ahmad A, Sala F, Paiè P, Candeo A, D'Annunzio S, Zippo A, Frindel C, Osellame R, Bragheri F, Bassi A, Rousseau D. On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy. LAB ON A CHIP 2022; 22:3453-3463. [PMID: 35946995 DOI: 10.1039/d2lc00482h] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Single-cell imaging and sorting are critical technologies in biology and clinical applications. The power of these technologies is increased when combined with microfluidics, fluorescence markers, and machine learning. However, this quest faces several challenges. One of these is the effect of the sample flow velocity on the classification performances. Indeed, cell flow speed affects the quality of image acquisition by increasing motion blur and decreasing the number of acquired frames per sample. We investigate how these visual distortions impact the final classification task in a real-world use-case of cancer cell screening, using a microfluidic platform in combination with light sheet fluorescence microscopy. We demonstrate, by analyzing both simulated and experimental data, that it is possible to achieve high flow speed and high accuracy in single-cell classification. We prove that it is possible to overcome the 3D slice variability of the acquired 3D volumes, by relying on their 2D sum z-projection transformation, to reach an efficient real time classification with an accuracy of 99.4% using a convolutional neural network with transfer learning from simulated data. Beyond this specific use-case, we provide a web platform to generate a synthetic dataset and to investigate the effect of flow speed on cell classification for any biological samples and a large variety of fluorescence microscopes (https://www.creatis.insa-lyon.fr/site7/en/MicroVIP).
Collapse
Affiliation(s)
- Ali Ahmad
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 - INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France
| | - Federico Sala
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Petra Paiè
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Alessia Candeo
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | | | | | - Carole Frindel
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 - INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France
| | - Roberto Osellame
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Francesca Bragheri
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Andrea Bassi
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
| |
Collapse
|
5
|
Raji H, Tayyab M, Sui J, Mahmoodi SR, Javanmard M. Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review. Biomed Microdevices 2022; 24:26. [PMID: 35953679 DOI: 10.1007/s10544-022-00627-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2022] [Indexed: 12/16/2022]
Abstract
Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.
Collapse
Affiliation(s)
- Hassan Raji
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Muhammad Tayyab
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Seyed Reza Mahmoodi
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
| |
Collapse
|
6
|
Kitahara Y, Itani A, Oda Y, Okamura M, Mizoshiri M, Shida Y, Nakamura T, Kasahara K, Ogasawara W. A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter. Appl Microbiol Biotechnol 2022; 106:4683-4693. [PMID: 35687157 DOI: 10.1007/s00253-022-12002-0] [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: 04/29/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/28/2022]
Abstract
The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.
Collapse
Affiliation(s)
- Yukina Kitahara
- Department of Science of Technology Innovation, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Ayaka Itani
- Department of Bioengineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Yosuke Oda
- Department of Mechanical Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Makoto Okamura
- NRI System Techno Ltd, 134, Kobecho, Hodogaya-ku, Yokohama, Kanagawa, 240-0005, Japan
| | - Mizue Mizoshiri
- Department of Mechanical Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Yosuke Shida
- Department of Bioengineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Toru Nakamura
- NRI System Techno Ltd, 134, Kobecho, Hodogaya-ku, Yokohama, Kanagawa, 240-0005, Japan
| | - Ken Kasahara
- Chitose Laboratory Corp, Biotechnology Research Center, 2-13-3 Nogawahoncho, Miyamae-ku, Kawasaki, Kanagawa, 216-0041, Japan
| | - Wataru Ogasawara
- Department of Science of Technology Innovation, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan. .,Department of Bioengineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan.
| |
Collapse
|
7
|
Lv H, Chen X. Intelligent control of nanoparticle synthesis through machine learning. NANOSCALE 2022; 14:6688-6708. [PMID: 35450983 DOI: 10.1039/d2nr00124a] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The synthesis of nanoparticles is affected by many reaction conditions, and their properties are usually determined by factors such as their size, shape and surface chemistry. In order for the synthesized nanoparticles to have functions suitable for different fields (for example, optics, electronics, sensor applications and so on), precise control of their properties is essential. However, with the current technology of preparing nanoparticles on a microreactor, it is time-consuming and laborious to achieve precise synthesis. In order to improve the efficiency of synthesizing nanoparticles with the expected functionality, the application of machine learning-assisted synthesis is an intelligent choice. In this article, we mainly introduce the typical methods of preparing nanoparticles on microreactors, and explain the principles and procedures of machine learning, as well as the main ways of obtaining data sets. We have studied three types of representative nanoparticle preparation methods assisted by machine learning. Finally, the current problems in machine learning-assisted nanoparticle synthesis and future development prospects are discussed.
Collapse
Affiliation(s)
- Honglin Lv
- College of Transportation, Ludong University, Yantai, Shandong 264025, China.
| | - Xueye Chen
- College of Transportation, Ludong University, Yantai, Shandong 264025, China.
| |
Collapse
|
8
|
Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021; 194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 02/06/2023]
Abstract
Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.
Collapse
Affiliation(s)
- Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Jeeson Kim
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| |
Collapse
|
9
|
Khater A, Abdelrehim O, Mohammadi M, Mohamad A, Sanati-Nezhad A. Thermal droplet microfluidics: From biology to cooling technology. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116234] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
|
10
|
Gupta P, Balasubramaniam N, Chang HY, Tseng FG, Santra TS. A Single-Neuron: Current Trends and Future Prospects. Cells 2020; 9:E1528. [PMID: 32585883 PMCID: PMC7349798 DOI: 10.3390/cells9061528] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/15/2020] [Accepted: 06/19/2020] [Indexed: 12/11/2022] Open
Abstract
The brain is an intricate network with complex organizational principles facilitating a concerted communication between single-neurons, distinct neuron populations, and remote brain areas. The communication, technically referred to as connectivity, between single-neurons, is the center of many investigations aimed at elucidating pathophysiology, anatomical differences, and structural and functional features. In comparison with bulk analysis, single-neuron analysis can provide precise information about neurons or even sub-neuron level electrophysiology, anatomical differences, pathophysiology, structural and functional features, in addition to their communications with other neurons, and can promote essential information to understand the brain and its activity. This review highlights various single-neuron models and their behaviors, followed by different analysis methods. Again, to elucidate cellular dynamics in terms of electrophysiology at the single-neuron level, we emphasize in detail the role of single-neuron mapping and electrophysiological recording. We also elaborate on the recent development of single-neuron isolation, manipulation, and therapeutic progress using advanced micro/nanofluidic devices, as well as microinjection, electroporation, microelectrode array, optical transfection, optogenetic techniques. Further, the development in the field of artificial intelligence in relation to single-neurons is highlighted. The review concludes with between limitations and future prospects of single-neuron analyses.
Collapse
Affiliation(s)
- Pallavi Gupta
- Department of Engineering Design, Indian Institute of Technology Madras, Tamil Nadu 600036, India; (P.G.); (N.B.)
| | - Nandhini Balasubramaniam
- Department of Engineering Design, Indian Institute of Technology Madras, Tamil Nadu 600036, India; (P.G.); (N.B.)
| | - Hwan-You Chang
- Department of Medical Science, National Tsing Hua University, Hsinchu 30013, Taiwan;
| | - Fan-Gang Tseng
- Department of Engineering and System Science, National Tsing Hua University, Hsinchu 30013, Taiwan;
| | - Tuhin Subhra Santra
- Department of Engineering Design, Indian Institute of Technology Madras, Tamil Nadu 600036, India; (P.G.); (N.B.)
| |
Collapse
|
11
|
Editorial for the Special Issue on Microfluidics for Cell and Other Organisms. MICROMACHINES 2019; 10:mi10080520. [PMID: 31387332 PMCID: PMC6723602 DOI: 10.3390/mi10080520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 07/30/2019] [Indexed: 12/02/2022]
|