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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 May 2026 | Viewed by 547

Special Issue Editors


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Guest Editor
Department of Engineering and System Science, National Tsing Hua University (NTHU), Academia Sinica, Hsinchu, Taiwan
Interests: organ on a chip; microfluidic systems; biosensors; CTCs/CTM diagnosis; single cell analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Department of Engineering and System Science, National Tsing Hua University (NTHU), Hsinchu, Taiwan
Interests: SERS sensors; BioMEMS; in-vitro sesning; 3D cell culture; electroporation; organ-on-chip

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

Manuscript Submission Information

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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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Published Papers (1 paper)

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Research

20 pages, 5977 KB  
Article
Assessment of Continuous Flow-Dependent Red Cell Aggregation Using a Microfluidic Chip
by Yang Jun Kang
Appl. Sci. 2025, 15(21), 11481; https://doi.org/10.3390/app152111481 - 27 Oct 2025
Viewed by 217
Abstract
Measuring RBC aggregation can be considered as a valuable tool for detecting pathological diseases. Most previous methods need to stop and run blood flows periodically. Thus, it is impossible to probe RBC aggregation in continuously varying infusion flow. To resolve the issues, a [...] Read more.
Measuring RBC aggregation can be considered as a valuable tool for detecting pathological diseases. Most previous methods need to stop and run blood flows periodically. Thus, it is impossible to probe RBC aggregation in continuously varying infusion flow. To resolve the issues, a novel bifurcated continuous-flow mechanism is suggested to probe RBC aggregation without periodic interruption of blood flow. A microfluidic chip is then designed to split single flow into two branches (low flow rate and high flow rate). RBC aggregation occurs in the low flow-rate channel, whereas it is dispersed fully in the high flow-rate channel. Using a syringe pump, blood is infused into a microfluidic chip at constant and sinusoidal pattern. RBC aggregation index (AI) is calculated from time-lapse imaging intensity within each channel. From fluidic circuit analysis and experimental results, the optimal infusion flow rate is determined as Qsp = 0.5~2 mL/h. The AI is higher at Hct = 30% than at Hct = 50%. The high concentration of dextran solution increases AI considerably. The period of pulsatile infusion flow rate has a strong influence on time-lapse AI. In conclusion, the present method can be capable of measuring time-lapse AI consistently, without interrupting infusion flow. Full article
(This article belongs to the Special Issue Current Applications of Microfluidics for Biosensing and Diagnostics)
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