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Dou J, Yang Z, Singh B, Ma B, Lu Z, Xu J, He Y. Discussion: Embracing microfluidics to advance environmental science and technology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 937:173597. [PMID: 38810741 DOI: 10.1016/j.scitotenv.2024.173597] [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: 02/24/2024] [Revised: 05/25/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024]
Abstract
Microfluidics, also called lab-on-a-chip, represents an emerging research platform that permits more precise and manipulation of samples at the microscale or even down to the nanoscale (nanofluidic) including picoliter droplets, microparticles, and microbes within miniaturized and highly integrated devices. This groundbreaking technology has made significant strides across multiple disciplines by providing an unprecedented view of physical, chemical, and biological events, fostering a holistic and an in-depth understanding of complex systems. The application of microfluidics to address the challenges in environmental science is likely to contribute to our better understanding, however, it's not yet fully developed. To raise researchers' interest, this discussion first delineates the valuable and underutilized environmental applications of microfluidic technology, ranging from environmental surveillance to acting as microreactors for investigating interfacial dynamic processes, and facilitating high-throughput bioassays. We highlight, with examples, how rationally designed microfluidic devices lead to new insights into the advancement of environmental science and technology. We then critically review the key challenges that hinder the practical adoption of microfluidic technologies. Specifically, we discuss the extent to which microfluidics accurately reflect realistic environmental scenarios, outline the areas to be improved, and propose strategies to overcome bottlenecks that impede the broad application of microfluidics. We also envision new opportunities and future research directions, aiming to provide guidelines for the broader utilization of microfluidics in environmental studies.
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Affiliation(s)
- Jibo Dou
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhugen Yang
- School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, United Kingdom
| | - Baljit Singh
- MiCRA Biodiagnostics Technology Gateway and Health, Engineering & Materials Science (HEMS) Research Hub, Technological University Dublin (TU Dublin), Dublin D24 FKT9, Ireland
| | - Bin Ma
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhijiang Lu
- Department of Environmental Science and Geology, Wayne State University, Detroit, MI 48201, United States
| | - Jianming Xu
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yan He
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, Hangzhou 310058, China.
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2
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Haghayegh F, Norouziazad A, Haghani E, Feygin AA, Rahimi RH, Ghavamabadi HA, Sadighbayan D, Madhoun F, Papagelis M, Felfeli T, Salahandish R. Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400595. [PMID: 38958517 DOI: 10.1002/advs.202400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
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Affiliation(s)
- Fatemeh Haghayegh
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Alireza Norouziazad
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Elnaz Haghani
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Ariel Avraham Feygin
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Reza Hamed Rahimi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Hamidreza Akbari Ghavamabadi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Deniz Sadighbayan
- Department of Biology, Faculty of Science, York University, Toronto, ON, M3J 1P3, Canada
| | - Faress Madhoun
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Manos Papagelis
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Tina Felfeli
- Department of Ophthalmology and Vision Sciences, University of Toronto, Ontario, M5T 3A9, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Ontario, M5T 3M6, Canada
| | - Razieh Salahandish
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
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3
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Apoorva S, Nguyen NT, Sreejith KR. Recent developments and future perspectives of microfluidics and smart technologies in wearable devices. LAB ON A CHIP 2024; 24:1833-1866. [PMID: 38476112 DOI: 10.1039/d4lc00089g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.
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Affiliation(s)
- Sasikala Apoorva
- UKF Centre for Advanced Research and Skill Development(UCARS), UKF College of Engineering and Technology, Kollam, Kerala, India, 691 302
| | - Nam-Trung Nguyen
- Queensland Micro and Nanotechnology Centre, Griffith University, 170 Kessels Road, Nathan, 4111, Queensland, Australia.
| | - Kamalalayam Rajan Sreejith
- Queensland Micro and Nanotechnology Centre, Griffith University, 170 Kessels Road, Nathan, 4111, Queensland, Australia.
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4
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Zhang N, Sun T, Liu Z, Zhang Y, Xu Y, Wang J. A universal inverse design methodology for microfluidic mixers. BIOMICROFLUIDICS 2024; 18:024102. [PMID: 38560343 PMCID: PMC10977039 DOI: 10.1063/5.0185494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/11/2024] [Indexed: 04/04/2024]
Abstract
The intelligent design of microfluidic mixers encompasses both the automation of predicting fluid performance and the structural design of mixers. This article delves into the technical trajectory of computer-aided design for micromixers, leveraging artificial intelligence algorithms. We propose an automated micromixer design methodology rooted in cost-effective artificial neural network (ANN) models paired with inverse design algorithms. Initially, we introduce two inverse design methods for micromixers: one that combines ANN with multi-objective genetic algorithms, and another that fuses ANN with particle swarm optimization algorithms. Subsequently, using two benchmark micromixers as case studies, we demonstrate the automatic derivation of micromixer structural parameters. Finally, we automatically design and optimize 50 sets of micromixer structures using the proposed algorithms. The design accuracy is further enhanced by analyzing the inverse design algorithm from a statistical standpoint.
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Affiliation(s)
- Naiyin Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Taotao Sun
- School of Integrated Circuit Science and Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Zhenya Liu
- School of Integrated Circuit Science and Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Yidan Zhang
- School of Integrated Circuit Science and Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Ying Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Junchao Wang
- School of Integrated Circuit Science and Engineering, Hangzhou Dianzi University, Hangzhou, China
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5
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Zhou J, Dong J, Hou H, Huang L, Li J. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications. LAB ON A CHIP 2024; 24:1307-1326. [PMID: 38247405 DOI: 10.1039/d3lc01012k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.
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Affiliation(s)
- Jianhua Zhou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jianpei Dong
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Hongwei Hou
- Beijing Life Science Academy, Beijing 102209, China
| | - Lu Huang
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jinghong Li
- Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Tsinghua University, Beijing 100084, China.
- New Cornerstone Science Laboratory, Shenzhen 518054, China
- Beijing Life Science Academy, Beijing 102209, China
- Center for BioAnalytical Chemistry, Hefei National Laboratory of Physical Science at Microscale, University of Science and Technology of China, Hefei 230026, China
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6
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Qi X, Zhou Q, Li X, Hu G. Generation of Multiple Concentration Gradients Using a Two-Dimensional Pyramid Array. Anal Chem 2024; 96:856-865. [PMID: 38104274 DOI: 10.1021/acs.analchem.3c04496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Concentration heterogeneity of diffusible reactants is a prevalent phenomenon in biochemical processes, requiring the generation of concentration gradients for the relevant experiments. In this study, we present a high-density pyramid array microfluidic network for the effective and precise generation of multiple concentration gradients. The complex gradient distribution in the 2D array can be adaptively adjusted by modulating the reactant velocities and concentrations at the inlets. In addition, the unique design of each reaction chamber and mixing block in the array ensures uniform concentrations within each chamber during dynamic changes, enabling large-scale reactions with low reactant volumes. Through detailed numerical simulation of mass transport within the complex microchannel networks, the proposed method allows researchers to determine the desired number of reaction chambers within a given concentration range based on experimental requirements and to quickly obtain the operating conditions with the help of machine learning-based prediction. The effectiveness in generating a multiple concentration gradient environment was further demonstrated by concentration-dependent calcium carbonate crystallization experiments. This device provides a highly efficient mixing and adaptable concentration platform that is well suited for high-throughput and multiplexed reactions.
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Affiliation(s)
- Xinlei Qi
- Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
| | - Qin Zhou
- Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
| | - Xuejin Li
- Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
| | - Guoqing Hu
- Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
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7
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Lashkaripour A, McIntyre DP, Calhoun SGK, Krauth K, Densmore DM, Fordyce PM. Design automation of microfluidic single and double emulsion droplets with machine learning. Nat Commun 2024; 15:83. [PMID: 38167827 PMCID: PMC10761910 DOI: 10.1038/s41467-023-44068-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/29/2023] [Indexed: 01/05/2024] Open
Abstract
Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (<8%) of the desired diameter, facilitating tailored droplet-based platforms and accelerating their utility in life sciences.
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Affiliation(s)
- Ali Lashkaripour
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - David P McIntyre
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | | | - Karl Krauth
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Douglas M Densmore
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, Boston, MA, USA
| | - Polly M Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Chan-Zuckerberg Biohub, San Francisco, CA, USA.
- Sarafan ChEM-H Institute, Stanford University, Stanford, CA, USA.
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8
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Patil PD, Salokhe S, Karvekar A, Suryavanshi P, Phirke AN, Tiwari MS, Nadar SS. Microfluidic based continuous enzyme immobilization: A comprehensive review. Int J Biol Macromol 2023; 253:127358. [PMID: 37827414 DOI: 10.1016/j.ijbiomac.2023.127358] [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: 08/14/2023] [Revised: 10/05/2023] [Accepted: 10/09/2023] [Indexed: 10/14/2023]
Abstract
Conventional techniques for enzyme immobilization suffer from suboptimal activity recovery due to insufficient enzyme loading and inadequate stability. Furthermore, these techniques are time-consuming and involve multiple steps which limit the applicability of immobilized enzymes. In contrast, the use of microfluidic devices for enzyme immobilization has garnered significant attention due to its ability to precisely control immobilization parameters, resulting in highly active immobilized enzymes. This approach offers several advantages, including reduced time and energy consumption, enhanced mass-heat transfer, and improved control over the mixing process. It maintains the superior structural configuration in immobilized form which ultimately affects the overall efficiency. The present review article comprehensively explains the design, construction, and various methods employed for enzyme immobilization using microfluidic devices. The immobilized enzymes prepared using these techniques demonstrated excellent catalytic activity, remarkable stability, and outstanding recyclability. Moreover, they have found applications in diverse areas such as biosensors, biotransformation, and bioremediation. The review article also discusses potential future developments and foresees significant challenges associated with enzyme immobilization using microfluidics, along with potential remedies. The development of this advanced technology not only paves the way for novel and innovative approaches to enzyme immobilization but also allows for the straightforward scalability of microfluidic-based techniques from an industrial standpoint.
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Affiliation(s)
- Pravin D Patil
- Department of Basic Science & Humanities, SVKM'S NMIMS Mukesh Patel School of Technology Management & Engineering, Mumbai, Maharashtra 400056, India
| | - Sakshi Salokhe
- Department of Biotechnology Engineering, Kolhapur Institute of Technology's College of Engineering, Kolhapur 416 234, India
| | - Aparna Karvekar
- Department of Biotechnology Engineering, Kolhapur Institute of Technology's College of Engineering, Kolhapur 416 234, India
| | - Prabhavati Suryavanshi
- Department of Biotechnology Engineering, Kolhapur Institute of Technology's College of Engineering, Kolhapur 416 234, India
| | - Ajay N Phirke
- Department of Basic Science & Humanities, SVKM'S NMIMS Mukesh Patel School of Technology Management & Engineering, Mumbai, Maharashtra 400056, India
| | - Manishkumar S Tiwari
- Department of Data Science, SVKM'S NMIMS Mukesh Patel School of Technology Management & Engineering, Mumbai, Maharashtra 400056, India
| | - Shamraja S Nadar
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga (E), Mumbai 400019, India.
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9
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McIntyre D, Lashkaripour A, Arguijo D, Fordyce P, Densmore D. Versatility and stability optimization of flow-focusing droplet generators via quality metric-driven design automation. LAB ON A CHIP 2023; 23:4997-5008. [PMID: 37909215 PMCID: PMC10694034 DOI: 10.1039/d3lc00189j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Droplet generation is a fundamental component of droplet microfluidics, compartmentalizing biological or chemical systems within a water-in-oil emulsion. As adoption of droplet microfluidics expands beyond expert labs or integrated devices, quality metrics are needed to contextualize the performance capabilities, improving the reproducibility and efficiency of operation. Here, we present two quality metrics for droplet generation: performance versatility, the operating range of a single device, and stability, the distance of a single operating point from a regime change. Both metrics were characterized in silico and validated experimentally using machine learning and rapid prototyping. These metrics were integrated into a design automation workflow, DAFD 2.0, which provides users with droplet generators of a desired performance that are versatile or flow stable. Versatile droplet generators with stable operating points accelerate the development of sophisticated devices by facilitating integration of other microfluidic components and improving the accuracy of design automation tools.
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Affiliation(s)
- David McIntyre
- Biomedical Engineering Department, Boston University, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA
| | - Ali Lashkaripour
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Diana Arguijo
- Biomedical Engineering Department, Boston University, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA
| | - Polly Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA
- Electrical & Computer Engineering Department, Boston University, Boston, MA, USA
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10
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Chang Y, Sheng L, Wang J, Deng J, Luo G. A general neural network model co-driven by mechanism and data for the reliable design of gas-liquid T-junction microdevices. LAB ON A CHIP 2023; 23:4888-4900. [PMID: 37873702 DOI: 10.1039/d3lc00355h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
In recent years, many models have been developed to describe the gas-liquid microdispersion process, which mainly rely on mechanistic analysis and may not be universally applicable. In order to provide a more comprehensive model and, most significantly, to provide a model for design, we have established a general database of microbubble generation in T-junction microdevices, including 854 data points from 12 pieces of literature. A neural network model that combines mechanistic and data modeling is developed. By transfer learning, more accurate results can be obtained. Additionally, we have proposed a design method that enables a relative deviation of less than 5% from the expected bubble size. A new device was designed and prepared to confirm the reliability of the method, which can prepare smaller bubbles than other common T-junction devices. In this way, a general and universal database and model are established and a design method for a gas-liquid T-junction microreactor is developed.
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Affiliation(s)
- Yu Chang
- The State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
| | - Lin Sheng
- The State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
| | - Junjie Wang
- The State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
| | - Jian Deng
- The State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
| | - Guangsheng Luo
- The State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
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11
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Schrier J, Norquist AJ, Buonassisi T, Brgoch J. In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science. J Am Chem Soc 2023; 145:21699-21716. [PMID: 37754929 DOI: 10.1021/jacs.3c04783] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.
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Affiliation(s)
- Joshua Schrier
- Department of Chemistry, Fordham University, The Bronx, New York 10458, United States
| | - Alexander J Norquist
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - Tonio Buonassisi
- Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Jakoah Brgoch
- Department of Chemistry and Texas Center for Superconductivity, University of Houston, Houston, Texas 77204, United States
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12
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Murray DS, Stickel L, Boutelle M. Computational Modeling as a Tool to Drive the Development of a Novel, Chemical Device for Monitoring the Injured Brain and Body. ACS Chem Neurosci 2023; 14:3599-3608. [PMID: 37737666 PMCID: PMC10557062 DOI: 10.1021/acschemneuro.3c00063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023] Open
Abstract
Real-time measurement of dynamic changes, occurring in the brain and other parts of the body, is useful for the detection and tracked progression of disease and injury. Chemical monitoring of such phenomena exists but is not commonplace, due to the penetrative nature of devices, the lack of continuous measurement, and the inflammatory responses that require pharmacological treatment to alleviate. Soft, flexible devices that more closely match the moduli and shape of monitored tissue and allow for surface microdialysis provide a viable alternative. Here, we show that computational modeling can be used to aid the development of such devices and highlight the considerations when developing a chemical monitoring probe in this way. These models pave the way for the development of a new class of chemical monitoring devices for monitoring neurotrauma, organs, and skin.
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Affiliation(s)
- De-Shaine Murray
- Department
of Bioengineering, Imperial College London SW7 2AZ, London, U.K.
- School
of Engineering and Applied Sciences, Yale
University, 06520, New Haven, Connecticut United States
| | - Laure Stickel
- Department
of Bioengineering, Imperial College London SW7 2AZ, London, U.K.
- Laboratoire
Physico-Chimie Curie, Institut Curie, 26 rue d’Ulm, 75005, Paris, France
| | - Martyn Boutelle
- Department
of Bioengineering, Imperial College London SW7 2AZ, London, U.K.
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13
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Deng S, Li C, Cao J, Cui Z, Du J, Fu Z, Yang H, Chen P. Organ-on-a-chip meets artificial intelligence in drug evaluation. Theranostics 2023; 13:4526-4558. [PMID: 37649608 PMCID: PMC10465229 DOI: 10.7150/thno.87266] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/02/2023] [Indexed: 09/01/2023] Open
Abstract
Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-a-chip (OoC) technology, which simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Meanwhile, the advancement in artificial intelligence (AI) provides more improvements for the design and data processing of OoCs. Here, we review the current progress that has been made to generate OoC platforms, and how human single and multi-OoCs have been used in applications, including drug testing, disease modeling, and personalized medicine. Moreover, we discuss issues facing the field, such as large data processing and reproducibility, and point to the integration of OoCs and AI in data analysis and automation, which is of great benefit in future drug evaluation. Finally, we look forward to the opportunities and challenges faced by the coupling of OoCs and AI. In summary, advancements in OoCs development, and future combinations with AI, will eventually break the current state of drug evaluation.
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Affiliation(s)
- Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jiang Du
- Yunnan Biovalley Pharmaceutical Co., Ltd, Kunming 650503, China
| | - Zheng Fu
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Yunnan Biovalley Pharmaceutical Co., Ltd, Kunming 650503, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
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14
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Ferguson C, Zhang Y, Palego C, Cheng X. Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5990. [PMID: 37447838 DOI: 10.3390/s23135990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/17/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.
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Affiliation(s)
- Caroline Ferguson
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Cristiano Palego
- Department of Computer Science and Electronic Engineering, Bangor University, Bangor LL57 2DG, UK
| | - Xuanhong Cheng
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
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15
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Sun H, Xie W, Mo J, Huang Y, Dong H. Deep learning with microfluidics for on-chip droplet generation, control, and analysis. Front Bioeng Biotechnol 2023; 11:1208648. [PMID: 37351472 PMCID: PMC10282949 DOI: 10.3389/fbioe.2023.1208648] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023] Open
Abstract
Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.
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Affiliation(s)
- Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Wantao Xie
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Jin Mo
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Yi Huang
- Centre for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, China
| | - Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
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16
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Vasina M, Kovar D, Damborsky J, Ding Y, Yang T, deMello A, Mazurenko S, Stavrakis S, Prokop Z. In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning. Biotechnol Adv 2023; 66:108171. [PMID: 37150331 DOI: 10.1016/j.biotechadv.2023.108171] [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: 01/24/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - David Kovar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Yun Ding
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Tianjin Yang
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland; Department of Biochemistry, University of Zurich, 8057 Zurich, Switzerland
| | - Andrew deMello
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
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17
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Khan M, Zhao B, Wu W, Zhao M, Bi Y, Hu Q. Distance-based microfluidic assays for instrument-free visual point-of-care testing. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.117029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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18
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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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19
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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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20
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Lu N, Tay HM, Petchakup C, He L, Gong L, Maw KK, Leong SY, Lok WW, Ong HB, Guo R, Li KHH, Hou HW. Label-free microfluidic cell sorting and detection for rapid blood analysis. LAB ON A CHIP 2023; 23:1226-1257. [PMID: 36655549 DOI: 10.1039/d2lc00904h] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics.
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Affiliation(s)
- Nan Lu
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
- HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, 65 Nanyang Drive, Block N3, 637460, Singapore
| | - Hui Min Tay
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Chayakorn Petchakup
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Linwei He
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Lingyan Gong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Kay Khine Maw
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Sheng Yuan Leong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Wan Wei Lok
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Hong Boon Ong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Ruya Guo
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China
| | - King Ho Holden Li
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
- HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, 65 Nanyang Drive, Block N3, 637460, Singapore
| | - Han Wei Hou
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
- HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, 65 Nanyang Drive, Block N3, 637460, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Clinical Sciences Building, 308232, Singapore
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21
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Closed-loop Control Systems for Pumps used in Portable Analytical Systems. J Chromatogr A 2023; 1695:463931. [PMID: 37011525 DOI: 10.1016/j.chroma.2023.463931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/27/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023]
Abstract
The demand for accurate control of the flowrate/pressure in chemical analytical systems has given rise to the adoption of mechatronic approaches in analytical instruments. A mechatronic device is a synergistic system which combines mechanical, electronic, computer and control components. In the development of portable analytical devices, considering the instrument as a mechatronic system can be useful to mitigate compromises made to decrease space, weight, or power consumption. Fluid handling is important for reliability, however, commonly utilized platforms such as syringe and peristaltic pumps are typically characterized by flow/pressure fluctuations and slow responses. Closed loop control systems have been used effectively to decrease the difference between desired and realized fluidic output. This review discusses the way control systems have been implemented for enhanced fluidic control, categorized by pump type. Advanced control strategies used to enhance the transient and the steady state responses are discussed, along with examples of their implementation in portable analytical systems. The review is concluded with the outlook that the challenge in adequately expressing the complexity and dynamics of the fluidic network as a mathematical model has yielded a trend towards the adoption of experimentally informed models and machine learning approaches.
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22
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Jeyasountharan A, Giudice FD. Viscoelastic Particle Encapsulation Using a Hyaluronic Acid Solution in a T-Junction Microfluidic Device. MICROMACHINES 2023; 14:563. [PMID: 36984969 PMCID: PMC10053877 DOI: 10.3390/mi14030563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/24/2023] [Accepted: 02/25/2023] [Indexed: 06/18/2023]
Abstract
The encapsulation of particles and cells in droplets is highly relevant in biomedical engineering as well as in material science. So far, however, the majority of the studies in this area have focused on the encapsulation of particles or cells suspended in Newtonian liquids. We here studied the particle encapsulation phenomenon in a T-junction microfluidic device, using a non-Newtonian viscoelastic hyaluronic acid solution in phosphate buffer saline as suspending liquid for the particles. We first studied the non-Newtonian droplet formation mechanism, finding that the data for the normalised droplet length scaled as the Newtonian ones. We then performed viscoelastic encapsulation experiments, where we exploited the fact that particles self-assembled in equally-spaced structures before approaching the encapsulation area, to then identify some experimental conditions for which the single encapsulation efficiency was larger than the stochastic limit predicted by the Poisson statistics.
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23
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Ahmadi F, Simchi M, Perry JM, Frenette S, Benali H, Soucy JP, Massarweh G, Shih SCC. Integrating machine learning and digital microfluidics for screening experimental conditions. LAB ON A CHIP 2022; 23:81-91. [PMID: 36416045 DOI: 10.1039/d2lc00764a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Digital microfluidics (DMF) has the signatures of an ideal liquid handling platform - as shown through almost two decades of automated biological and chemical assays. However, in the current state of DMF, we are still limited by the number of parallel biological or chemical assays that can be performed on DMF. Here, we report a new approach that leverages design-of-experiment and numerical methodologies to accelerate experimental optimization on DMF. The integration of the one-factor-at-a-time (OFAT) experimental technique with machine learning algorithms provides a set of recommended optimal conditions without the need to perform a large set of experiments. We applied our approach towards optimizing the radiochemistry synthesis yield given the large number of variables that affect the yield. We believe that this work is the first to combine such techniques which can be readily applied to any other assays that contain many parameters and levels on DMF.
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Affiliation(s)
- Fatemeh Ahmadi
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada.
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
- Centre for Applied Synthetic Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Mohammad Simchi
- Department of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, Ontario, M5S 3G8, Canada
| | - James M Perry
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Stephane Frenette
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Habib Benali
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada.
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Jean-Paul Soucy
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, Québec, H3A 2B4, Canada
| | - Gassan Massarweh
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, Québec, H3A 2B4, Canada
| | - Steve C C Shih
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada.
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
- Centre for Applied Synthetic Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
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24
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Garcia Eijo PM, Duriez T, Cabaleiro JM, Artana G. A machine learning-based framework to design capillary-driven networks. LAB ON A CHIP 2022; 22:4860-4870. [PMID: 36377409 DOI: 10.1039/d2lc00843b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We present a novel approach for the design of capillary-driven microfluidic networks using a machine learning genetic algorithm (ML-GA). This strategy relies on a user-friendly 1D numerical tool specifically developed to generate the necessary data to train the ML-GA. This 1D model was validated using analytical results issued from a Y-shaped capillary network and experimental data. For a given microfluidic network, we defined the objective of the ML-GA to obtain the set of geometric parameters that produces the closest matching results against two prescribed curves of delivered volume against time. We performed more than 20 generations of 10 000 simulations to train the ML-GA and achieved the optimal solution of the inverse design problem. The optimisation took less than 6 hours, and the results were successfully validated using experimental data. This work establishes the utility of the presented method for the fast and reliable design of complex capillary-driven devices, enabling users to optimise their designs via an easy-to-use 1D numerical tool and machine learning technique.
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Affiliation(s)
- Pedro Manuel Garcia Eijo
- Laboratorio de Fluidodinámica, Facultad de Ingeniería, Universidad de Buenos Aires, C1063ACV, Buenos Aires, Argentina.
| | - Thomas Duriez
- Laboratorio de Fluidodinámica, Facultad de Ingeniería, Universidad de Buenos Aires, C1063ACV, Buenos Aires, Argentina.
| | - Juan Martín Cabaleiro
- Laboratorio de Fluidodinámica, Facultad de Ingeniería, Universidad de Buenos Aires, C1063ACV, Buenos Aires, Argentina.
| | - Guillermo Artana
- Laboratorio de Fluidodinámica, Facultad de Ingeniería, Universidad de Buenos Aires, C1063ACV, Buenos Aires, Argentina.
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Zhang N, Liang K, Liu Z, Sun T, Wang J. ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics. MICROMACHINES 2022; 13:2100. [PMID: 36557399 PMCID: PMC9781979 DOI: 10.3390/mi13122100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/24/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories.
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Affiliation(s)
- Naiyin Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Kaicong Liang
- Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Zhenya Liu
- Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Taotao Sun
- Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Junchao Wang
- Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China
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Zhang N, Liu Z, Wang J. Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator. MICROMACHINES 2022; 13:1810. [PMID: 36363832 PMCID: PMC9697332 DOI: 10.3390/mi13111810] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
Microfluidics concentration gradient generators have been widely applied in chemical and biological fields. However, the current gradient generators still have some limitations. In this work, we presented a microfluidic concentration gradient generator with its corresponding manipulation process to generate an arbitrary concentration gradient. Machine-learning techniques and interpolation algorithms were implemented to help researchers instantly analyze the current concentration profile of the gradient generator with different inlet configurations. The proposed method has a 93.71% accuracy rate with a 300× acceleration effect compared to the conventional finite element analysis. In addition, our method shows the potential application of the design automation and computer-aided design of microfluidics by leveraging both artificial neural networks and computer science algorithms.
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Affiliation(s)
- Naiyin Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Zhenya Liu
- Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Junchao Wang
- Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China
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