Multidisciplinary Role of Microfluidics for Biomedical and Diagnostic Applications: Biomedical Microfluidic Devices

Oh, Kwang W.

doi:10.3390/mi8120343

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…on one fully automated platform. Reduced in size and without the need for technical personnel to conduct many steps on separate machines, detection times are shortened [7]. One microfluidic platform which excels in its simplicity for pumping, automation of many assay steps, air-bubble free fluidics and multiplexing is the microfluidic disc, also known as microfluidic Compact Disc (CD) [8].…”

Section: Introductionmentioning

confidence: 99%

Wireless Electrochemical Detection on a Microfluidic Compact Disc (CD) and Evaluation of Redox-Amplification during Flow

et al. 2019

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Novel biomarkers and lower limits of detection enable improved diagnostics. In this paper we analyze the influence of flow on the lower limit of electrochemical detection on a microfluidic Compact Disc (CD). Implementing wireless transfer of data reduces noise during measurements and allows for real time sensing, demonstrated with the ferri-ferroyanide redox-couple in single and dual mode cyclic voltammetry. The impact of flow on redox-amplification and electrode integration for the lowest limit of detection are discussed.

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Section: Introductionmentioning

confidence: 99%

Wireless Electrochemical Detection on a Microfluidic Compact Disc (CD) and Evaluation of Redox-Amplification during Flow

et al. 2019

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“…Typically, microfluidic images have relatively low resolution compared to confocal microscopic images that are often of high resolution [ 4 ], rendering unique challenges for microfluidics image processing [ 5 ]. For instance, microfluidic device materials, device coating, device volume, and area limitations increase capturing errors such as blurring, shifting focus, and trap deformation.…”

Section: Introductionmentioning

confidence: 99%

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells

et al. 2021

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Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.

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“…The robust development of biochemical and clinical applications [ 6 , 7 , 8 ] of microfluidic technology prompts the development of novel microdevices that make the microfluidic systems even more versatile. Current microfluidic devices should not only operate on small amounts of reagents, but also be portable, precise, cost-effective, programmable, and offer new capabilities to carry out a variety of laboratory operations, either subsequently or simultaneously (in parallel) [ 2 , 9 ]. This approach is widely used in modular microfluidic systems, such as swappable fluidic modules [ 10 , 11 , 12 , 13 ], a Lego-like modular microfluidic platforms [ 13 , 14 , 15 ], and 3-D modular microfluidic devices [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Continuous Recirculation of Microdroplets in a Closed Loop Tailored for Screening of Bacteria Cultures

et al. 2018

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Emerging microfluidic technology has introduced new precision controls over reaction conditions. Owing to the small amount of reagents, microfluidics significantly lowers the cost of carrying a single reaction. Moreover, in two-phase systems, each part of a dispersed fluid can be treated as an independent chemical reactor with a volume from femtoliters to microliters, increasing the throughput. In this work, we propose a microfluidic device that provides continuous recirculation of droplets in a closed loop, maintaining low consumption of oil phase, no cross-contamination, stabilized temperature, a constant condition of gas exchange, dynamic feedback control on droplet volume, and a real-time optical characterization of bacterial growth in a droplet. The channels (tubing) and junction cubes are made of Teflon fluorinated ethylene propylene (FEP) to ensure non-wetting conditions and to prevent the formation of biofilm, which is particularly crucial for biological experiments. We show the design and operation of a novel microfluidic loop with the circular motion of microdroplet reactors monitored with optical sensors and precision temperature controls. We have employed the proposed system for long term monitoring of bacterial growth during the antibiotic chloramphenicol treatment. The proposed system can find applications in a broad field of biomedical diagnostics and therapy.

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Multidisciplinary Role of Microfluidics for Biomedical and Diagnostic Applications: Biomedical Microfluidic Devices

Abstract: Life scientists are closely working with engineers to solve biological and biomedical problems through the application of engineering tools.[...]

Cited by 15 publications

References 12 publications

Wireless Electrochemical Detection on a Microfluidic Compact Disc (CD) and Evaluation of Redox-Amplification during Flow

Wireless Electrochemical Detection on a Microfluidic Compact Disc (CD) and Evaluation of Redox-Amplification during Flow

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells

Continuous Recirculation of Microdroplets in a Closed Loop Tailored for Screening of Bacteria Cultures

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