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Kokabi M, Tayyab M, Rather GM, Pournadali Khamseh A, Cheng D, DeMauro EP, Javanmard M. Integrating optical and electrical sensing with machine learning for advanced particle characterization. Biomed Microdevices 2024; 26:25. [PMID: 38780704 PMCID: PMC11116188 DOI: 10.1007/s10544-024-00707-0] [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] [Accepted: 04/15/2024] [Indexed: 05/25/2024]
Abstract
Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properties. In this study, we investigated the integration of electrical and optical features through a multimodal approach for particle classification. Machine learning classifier algorithms were applied to evaluate the impact of combining these measurements. Our results demonstrate the superiority of the multimodal approach over analyzing electrical or optical features independently. We achieved an average test accuracy of 94.9% by integrating both modalities, compared to 66.4% for electrical features alone and 90.7% for optical features alone. This highlights the complementary nature of electrical and optical information and its potential for enhancing classification performance. By leveraging electrical sensing and optical imaging techniques, our multimodal approach provides deeper insights into particle properties and offers a more comprehensive understanding of complex biological systems.
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Affiliation(s)
- Mahtab Kokabi
- 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
| | - Gulam M Rather
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, 08901, USA
| | | | - Daniel Cheng
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Edward P DeMauro
- Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
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Schlotheuber LJ, Lüchtefeld I, Eyer K. Antibodies, repertoires and microdevices in antibody discovery and characterization. LAB ON A CHIP 2024; 24:1207-1225. [PMID: 38165819 PMCID: PMC10898418 DOI: 10.1039/d3lc00887h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/01/2023] [Indexed: 01/04/2024]
Abstract
Therapeutic antibodies are paramount in treating a wide range of diseases, particularly in auto-immunity, inflammation and cancer, and novel antibody candidates recognizing a vast array of novel antigens are needed to expand the usefulness and applications of these powerful molecules. Microdevices play an essential role in this challenging endeavor at various stages since many general requirements of the overall process overlap nicely with the general advantages of microfluidics. Therefore, microfluidic devices are rapidly taking over various steps in the process of new candidate isolation, such as antibody characterization and discovery workflows. Such technologies can allow for vast improvements in time-lines and incorporate conservative antibody stability and characterization assays, but most prominently screenings and functional characterization within integrated workflows due to high throughput and standardized workflows. First, we aim to provide an overview of the challenges of developing new therapeutic candidates, their repertoires and requirements. Afterward, this review focuses on the discovery of antibodies using microfluidic systems, technological aspects of micro devices and small-scale antibody protein characterization and selection, as well as their integration and implementation into antibody discovery workflows. We close with future developments in microfluidic detection and antibody isolation principles and the field in general.
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Affiliation(s)
- Luca Johannes Schlotheuber
- ETH Laboratory for Functional Immune Repertoire Analysis, Institute of Pharmaceutical Sciences, D-CHAB, ETH Zürich, 8093 Zürich, Switzerland.
| | - Ines Lüchtefeld
- ETH Laboratory for Functional Immune Repertoire Analysis, Institute of Pharmaceutical Sciences, D-CHAB, ETH Zürich, 8093 Zürich, Switzerland.
- ETH Laboratory for Tumor and Stem Cell Dynamics, Institute of Molecular Health Sciences, D-BIOL, ETH Zürich, 8093 Zürich, Switzerland
| | - Klaus Eyer
- ETH Laboratory for Functional Immune Repertoire Analysis, Institute of Pharmaceutical Sciences, D-CHAB, ETH Zürich, 8093 Zürich, Switzerland.
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Meng Z, Raji H, Tayyab M, Javanmard M. Cell phone microscopy enabled low-cost manufacturable colorimetric urine glucose test. Biomed Microdevices 2023; 25:43. [PMID: 37930426 DOI: 10.1007/s10544-023-00682-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 11/07/2023]
Abstract
Glucose serves as a pivotal biomarker crucial for the monitoring and diagnosis of a spectrum of medical conditions, encompassing hypoglycemia, hyperglycemia, and diabetes, all of which may precipitate severe clinical manifestations in individuals. As a result, there is a growing demand within the medical domain for the development of rapid, cost-effective, and user-friendly diagnostic tools. In this research article, we introduce an innovative glucose sensor that relies on microfluidic devices meticulously crafted from disposable, medical-grade tapes. These devices incorporate glucose urine analysis strips securely affixed to microscope glass slides. The microfluidic channels are intricately created through laser cutting, representing a departure from traditional cleanroom techniques. This approach streamlines production processes, enhances cost-efficiency, and obviates the need for specialized equipment. Subsequent to the absorption of the target solution, the disposable device is enclosed within a 3D-printed housing. Image capture is seamlessly facilitated through the use of a smartphone camera for subsequent colorimetric analysis. Our study adeptly demonstrates the glucose sensor's capability to accurately quantify glucose concentrations within sucrose solutions. This is achieved by employing an exponential regression model, elucidating the intricate relationship between glucose concentrations and average RGB (Red-Green-Blue) values. Furthermore, our comprehensive analysis reveals minimal variation in sensor performance across different instances. Significantly, this study underscores the potential adaptability and versatility of our solution for a wide array of assay types and smartphone-based sensor systems, making it particularly promising for deployment in resource-constrained settings and undeveloped countries. The robust correlation established between glucose concentrations and average RGB values, substantiated by an impressive R-square value of 0.98709, underscores the effectiveness and reliability of our pioneering approach within the medical field.
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Affiliation(s)
- Zhuolun Meng
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, 08854, New Jersey, USA
| | - Hassan Raji
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, 08854, New Jersey, USA
| | - Muhammad Tayyab
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, 08854, New Jersey, USA
| | - Mehdi Javanmard
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, 08854, New Jersey, USA.
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Tang T, Julian T, Ma D, Yang Y, Li M, Hosokawa Y, Yalikun Y. A review on intelligent impedance cytometry systems: Development, applications and advances. Anal Chim Acta 2023; 1269:341424. [PMID: 37290859 DOI: 10.1016/j.aca.2023.341424] [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: 11/28/2022] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/10/2023]
Abstract
Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.
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Affiliation(s)
- Tao Tang
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan; Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Trisna Julian
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan
| | - Doudou Ma
- Center for Biosystems Dynamics Research (BDR), RIKEN, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yang Yang
- Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, Hainan, 572000, PR China
| | - Ming Li
- School of Engineering, Macquarie University, Sydney, 2109, Australia
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan
| | - Yaxiaer Yalikun
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan; Center for Biosystems Dynamics Research (BDR), RIKEN, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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