Centrifugation-assisted lateral flow assay platform: enhancing bioassay sensitivity with active flow control
Abstract Lateral flow assays (LFAs) are widely used in point-of-care testing (POCT) due to their simplicity and rapid operation. However, their reliance on passive capillary flow limits sensitivity, making it challenging to detect low-abundance biomarkers accurately. Approaches such as computer sign...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-05-01
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| Series: | Microsystems & Nanoengineering |
| Online Access: | https://doi.org/10.1038/s41378-025-00923-5 |
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| Summary: | Abstract Lateral flow assays (LFAs) are widely used in point-of-care testing (POCT) due to their simplicity and rapid operation. However, their reliance on passive capillary flow limits sensitivity, making it challenging to detect low-abundance biomarkers accurately. Approaches such as computer signal processing, chemical modification, and physical regulation have been explored to improve LFA sensitivity, but they remain limited by passive capillary-driven flow and uncontrollable flow rate. An alternative approach is to actively regulate fluid dynamics to optimize analyte binding and signal generation. The key challenge is to enhance LFA sensitivity while preserving compatibility with existing lateral flow strips (LFSs). Here, this study introduces a centrifugation-assisted LFA (CLFA) platform with smartphone-based result processing. This platform applies centrifugal force opposite to capillary flow, actively regulating fluid movement to optimize incubation time at the reaction zone and enhance detection performance. This approach increases signal intensity while maintaining a rapid detection process (5 min) and ensuring integration with traditional LFSs. As a proof-of-concept, the CLFA platform successfully detected human chorionic gonadotropin (hCG) and hemoglobin (Hb) in artificial urine without requiring custom-designed centrifugal discs or modified chromatography membranes. Its adaptability to diverse biomarkers and smartphone-based quantification make it a promising POCT tool, particularly in resource-limited settings. |
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| ISSN: | 2055-7434 |