DNA Sensor for the Detection of Brucella spp. Based on Magnetic Nanoparticle Markers.

Bibliographic Details
Title: DNA Sensor for the Detection of Brucella spp. Based on Magnetic Nanoparticle Markers.
Authors: Abuawad, Abdalhalim1,2 (AUTHOR) a.abuawad@fz-juelich.de, Ashhab, Yaqoub3 (AUTHOR), Offenhäusser, Andreas1,2 (AUTHOR), Krause, Hans-Joachim1 (AUTHOR) h.-j.krause@fz-juelich.de
Source: International Journal of Molecular Sciences. Dec2023, Vol. 24 Issue 24, p17272. 14p.
Subject Terms: *BRUCELLA, *NANOPARTICLES, *DETECTION limit
Abstract: Due to the limitations of conventional Brucella detection methods, including safety concerns, long incubation times, and limited specificity, the development of a rapid, selective, and accurate technique for the early detection of Brucella in livestock animals is crucial to prevent the spread of the associated disease. In the present study, we introduce a magnetic nanoparticle marker-based biosensor using frequency mixing magnetic detection for point-of-care testing and quantification of Brucella DNA. Superparamagnetic nanoparticles were used as magnetically measured markers to selectively detect the target DNA hybridized with its complementary capture probes immobilized on a porous polyethylene filter. Experimental conditions like density and length of the probes, hybridization time and temperature, and magnetic binding specificity, sensitivity, and detection limit were investigated and optimized. Our sensor demonstrated a relatively fast detection time of approximately 10 min, with a detection limit of 55 copies (0.09 fM) when tested using DNA amplified from Brucella genetic material. In addition, the detection specificity was examined using gDNA from Brucella and other zoonotic bacteria that may coexist in the same niche, confirming the method's selectivity for Brucella DNA. Our proposed biosensor has the potential to be used for the early detection of Brucella bacteria in the field and can contribute to disease control measures. [ABSTRACT FROM AUTHOR]
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  Data: DNA Sensor for the Detection of Brucella spp. Based on Magnetic Nanoparticle Markers.
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  Data: Due to the limitations of conventional Brucella detection methods, including safety concerns, long incubation times, and limited specificity, the development of a rapid, selective, and accurate technique for the early detection of Brucella in livestock animals is crucial to prevent the spread of the associated disease. In the present study, we introduce a magnetic nanoparticle marker-based biosensor using frequency mixing magnetic detection for point-of-care testing and quantification of Brucella DNA. Superparamagnetic nanoparticles were used as magnetically measured markers to selectively detect the target DNA hybridized with its complementary capture probes immobilized on a porous polyethylene filter. Experimental conditions like density and length of the probes, hybridization time and temperature, and magnetic binding specificity, sensitivity, and detection limit were investigated and optimized. Our sensor demonstrated a relatively fast detection time of approximately 10 min, with a detection limit of 55 copies (0.09 fM) when tested using DNA amplified from Brucella genetic material. In addition, the detection specificity was examined using gDNA from Brucella and other zoonotic bacteria that may coexist in the same niche, confirming the method's selectivity for Brucella DNA. Our proposed biosensor has the potential to be used for the early detection of Brucella bacteria in the field and can contribute to disease control measures. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Molecular Sciences is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/ijms242417272
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        Text: English
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              Text: Dec2023
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