An Ultraminiaturized Inflammation Monitoring Platform Implemented by Long Afterglow Lateral Flow Immunoassay
The abnormal fluctuations of inflammatory factors may reflect the immune response and pathological processes of the body. The real-time monitoring of these indicators is critical for the treatment and prognosis of infectious or autoimmune diseases. Among numerous point-of-care testing methods, later...
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Veröffentlicht in: | IEEE sensors journal 2024-11, Vol.24 (21), p.34618-34624 |
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Zusammenfassung: | The abnormal fluctuations of inflammatory factors may reflect the immune response and pathological processes of the body. The real-time monitoring of these indicators is critical for the treatment and prognosis of infectious or autoimmune diseases. Among numerous point-of-care testing methods, lateral flow immunoassay (LFIA) has a high impact due to its unique superiority in convenience and rapidity. However, traditional colloidal gold-based LFIAs suffer from qualitative or semiquantitative detection, while most of the optical nanoprobes enabled quantitative LFIAs require expensive equipment for signal reading, not benefiting widespread needs for personal or home use. To confront these challenges, in this work, we propose a new generation of LFIA by using ultralong afterglow nanoprobes (UANPs) and a self-developed miniaturized sensing device for simultaneous detection of C-reactive protein (CRP) and serum amyloid A (SAA). Hydrophilic and surface-modified UANPs are prepared in uniform size and good dispersibility by a two-step method. The afterglow sensing strategy effectively avoids biological endogenous background and excitation light interference. We therefore develop an ultraminiaturized low-cost device with conventional optical components, omitting complicated filtering modules. As a result, the UANP-LFIA achieved detection linear ranges of 0.7-250 ng/mL for CRP and 1-200 ng/mL for SAA, with detection limits of 0.67 and 0.81 ng/mL, respectively. Moreover, the palm-size sensing device can be interconnected with smart terminals and is capable of uploading medical data, providing a more accurate strategy for identifying infection types and assessing disease severity. |
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ISSN: | 1530-437X 1558-1748 |
DOI: | 10.1109/JSEN.2024.3458995 |