Current Applications of Microfluidics for Biosensing and Diagnostics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: 25 September 2025 | Viewed by 96
Special Issue Editors
Interests: organ on a chip; microfluidic systems; biosensors; CTCs/CTM diagnosis; single cell analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Microfluidics is rapidly emerging as a powerful tool in biosensing and diagnostics due to its ability to manipulate small volumes of fluids with high precision. Microfluidic devices equipped with sensors provide a platform to track physiological signals such as heart rate, blood glucose levels, temperature, oxygen saturation, and so on. In particular, biosensors incorporated into in vivo models can be used for the accurate and reliable predictions of biological responses to various stimuli, including drugs and toxins. Moreover, advanced data analysis techniques and AI-based decision-making algorithms play an essential role in interpreting biosensor data. By leveraging machine learning and artificial intelligence, these technologies can analyze vast amounts of data, recognize patterns, and make predictive recommendations. This improves the efficiency and reliability of diagnostics, allowing for earlier detection and more effective treatments.
Accordingly, this Special Issue seeks to showcase research papers, short communications, and review articles focusing on all aspects of point-of-care models, in vitro sensors, biomedical device fabrication, and diagnostic applications, including the incorporation of data analysis and AI-based decision-making in biosensor technologies.
Prof. Dr. Fan-Gang Tseng
Guest Editor
Dr. Koyel Dey
Guest Editor Assistant
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Keywords
- in vitro sensing
- biomedical device
- point of care
- microfluidics for biosensing
- biomedical applications
- point-of-care testing
- diagnostic devices
- lab-on-a-chip
- biomarkers
- disease detection
- real-time monitoring
- machine learning
- AI-based decision-making
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