Literature DB >> 15936314

Evaluation of fluorescence hs-CRP immunoassay for point-of-care testing.

Sang Wook Oh1, Jung Dae Moon, Sang Yeol Park, Heuk Jae Jang, Jae Hoon Kim, Ki Bong Nahm, Eui Yul Choi.   

Abstract

BACKGROUND: C-reactive protein (CRP) is one of acute phase respondents that has been used to monitor infection and inflammation episodes. Recent studies have shown that high-sensitivity C-reactive protein (hs-CRP) is a potential risk predictor for future atherosclerosis and cardiovascular diseases (CVD).
METHODS: We previously developed a fluorescence-based immunochromatographic method for measuring hs-CRP concentrations (i-CHROMAtrade mark hs-CRP assay) in blood. Whole blood was mixed with detector buffer, and then loaded onto a test cartridge. After 10 min of incubation, the test cartridge was inserted and scanned for acquisition of fluorescence intensity in a laser fluorescence reader (i-CHROMAtrade mark reader). The fluorescence intensity was microprocessed and converted into the concentration of CRP in blood. The test result of 150 samples by the i-CHROMAtrade mark hs-CRP assay method was compared and evaluated with those by TBA 200FR turbidimetry and BN II nephelometry method. The Deming regression and the Bland-Altman difference plot analysis were used for comparison of hs-CRP test result.
RESULTS: The i-CHROMAtrade mark hs-CRP assay system exhibited a good linearity with in the whole measuring range (R=0.997). The imprecision of intra- and the inter-assay CVs (coefficient of variation) of assay system were CVs< 3% and < 5% in the range of 0.5-20 mg/l, respectively. The i-CHROMAtrade mark hs-CRP assay method correlated well with TBA 200FR turbidimetry and BN II nephelometry assay method (R=0.988, N=143 and R=0.989, N=143).
CONCLUSION: The i-CHROMAtrade mark hs-CRP assay system is comparable to those of other well-known fully automated hs-CRP assay and is suitable for point-of-care testing (POCT) in detection and quantification of hs-CRP.

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Year:  2005        PMID: 15936314     DOI: 10.1016/j.cccn.2005.01.026

Source DB:  PubMed          Journal:  Clin Chim Acta        ISSN: 0009-8981            Impact factor:   3.786


  9 in total

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8.  Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors.

Authors:  Zachary S Ballard; Hyou-Arm Joung; Artem Goncharov; Jesse Liang; Karina Nugroho; Dino Di Carlo; Omai B Garner; Aydogan Ozcan
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  9 in total

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