1. Label-free microfluidic cell sorting and detection for rapid blood analysis.
- Author
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Lu, Nan, Tay, Hui Min, Petchakup, Chayakorn, He, Linwei, Gong, Lingyan, Maw, Kay Khine, Leong, Sheng Yuan, Lok, Wan Wei, Ong, Hong Boon, Guo, Ruya, Li, King Ho Holden, and Hou, Han Wei
- Subjects
BLOOD testing ,BLOOD cell count ,CELL analysis ,CELL morphology ,MACHINE learning ,VESICLES (Cytology) ,CELL anatomy - Abstract
Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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