Wearable, Flexible, and Integrated Microfluidic-Based Biosensors for Point-of-Care Testing Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1718

Special Issue Editor


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Guest Editor
1. Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
2. Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher of Education (MAHE), Manipal, India
Interests: microfabrication; microfluidics; biomedical electronics electrochemical sensors and biosensors
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Special Issue Information

Dear Colleagues,

In recent times, there have been drastic advancements and developments in point-of-care testing. Post-COVID-19 especially, this domain of research has gained more popularity in healthcare applications including within biomedical, biochemical, and clinical areas. Several researchers and scientists are working towards the development of urgent-care devices using cutting-edge technologies such as microfluidics, biotechnology, nanotechnology, and sensor technology. Because of its vast and widespread universal applications, microfluidics has also been a hot topic in biomedical and diagnostic research. As microfluidics has potential with its unique features including its minimum sample volume, large surface-to-area ratio, fast response, and enhanced lab on a chip, this technology has opened the doors to the execution of novel ideas and turning them into reality in prognostic applications. Most objectives in microfluidics focus on designing approaches that enhance investigators' capabilities in nanomaterials, biomedicine, and biochemistry. Till now, microfluidic techniques have been widely used for a range of biological applications such as single-cell analysis, nucleic acid sequencing, microflow cytometry, efficient sample pretreatment, and biosensing in integrated wearable devices. Compared with conventional approaches, microfluidics offers substantial advantages, including high throughput, a small footprint, high proficiency, and multipurpose integration. It also covers a broad area of topics such as wearable sensors, implantable sensors, and integrated biosensors used for numerous applications.

Dr. Madhusudan B. Kulkarni
Guest Editor

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Keywords

  • biomedical electronic devices
  • health-monitoring wearables
  • implantable sensors
  • bioelectronic medicine
  • personalized healthcare

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Published Papers (1 paper)

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Research

25 pages, 9045 KiB  
Article
Deep Learning-Enhanced Portable Chemiluminescence Biosensor: 3D-Printed, Smartphone-Integrated Platform for Glucose Detection
by Chirag M. Singhal, Vani Kaushik, Abhijeet Awasthi, Jitendra B. Zalke, Sangeeta Palekar, Prakash Rewatkar, Sanjeet Kumar Srivastava, Madhusudan B. Kulkarni and Manish L. Bhaiyya
Bioengineering 2025, 12(2), 119; https://doi.org/10.3390/bioengineering12020119 - 27 Jan 2025
Cited by 1 | Viewed by 1345
Abstract
A novel, portable chemiluminescence (CL) sensing platform powered by deep learning and smartphone integration has been developed for cost-effective and selective glucose detection. This platform features low-cost, wax-printed micro-pads (WPµ-pads) on paper-based substrates used to construct a miniaturized CL sensor. A 3D-printed black [...] Read more.
A novel, portable chemiluminescence (CL) sensing platform powered by deep learning and smartphone integration has been developed for cost-effective and selective glucose detection. This platform features low-cost, wax-printed micro-pads (WPµ-pads) on paper-based substrates used to construct a miniaturized CL sensor. A 3D-printed black box serves as a compact WPµ-pad sensing chamber, replacing traditional bulky equipment, such as charge coupled device (CCD) cameras and optical sensors. Smartphone integration enables a seamless and user-friendly diagnostic experience, making this platform highly suitable for point-of-care (PoC) applications. Deep learning models significantly enhance the platform’s performance, offering superior accuracy and efficiency in CL image analysis. A dataset of 600 experimental CL images was utilized, out of which 80% were used for model training, with 20% of the images reserved for testing. Comparative analysis was conducted using multiple deep learning models, including Random Forest, the Support Vector Machine (SVM), InceptionV3, VGG16, and ResNet-50, to identify the optimal architecture for accurate glucose detection. The CL sensor demonstrates a linear detection range of 10–1000 µM, with a low detection limit of 8.68 µM. Extensive evaluations confirmed its stability, repeatability, and reliability under real-world conditions. This deep learning-powered platform not only improves the accuracy of analyte detection, but also democratizes access to advanced diagnostics through cost-effective and portable technology. This work paves the way for next-generation biosensing, offering transformative potential in healthcare and other domains requiring rapid and reliable analyte detection. Full article
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