研究目的
To develop a portable biosensor for rapid and sensitive detection of sepsis-related inflammatory biomarkers, procalcitonin (PCT) and C-reactive protein (CRP) directly from blood serum, aiming to assist in the early diagnosis and management of sepsis.
研究成果
The portable digital nanoparticle-enhanced plasmonic imager (DENIS) provides rapid, sensitive, and accurate detection of PCT and CRP biomarkers, equivalent to gold-standard laboratory tests. Its compact and low-cost design makes it a promising tool for on-site sepsis diagnosis, potentially improving patient outcomes through timely intervention.
研究不足
The study acknowledges the complexity of sepsis diagnosis and the potential for overlapping biomarker levels among patient groups. The device's performance in larger and more diverse populations needs further validation.
1：Experimental Design and Method Selection
The study employs a novel portable biosensor based on nanoparticle-enhanced digital plasmonic imaging for the detection of PCT and CRP biomarkers. The detection mechanism leverages gold nanoparticle (Au-NP) binding to plasmonic gold nanohole array (Au-NHA), enabling quantification of individual molecule binding on the sensor surface in complex media.
2：Sample Selection and Data Sources
Clinical samples from patients with sepsis, noninfectious systemic inflammatory response syndrome (SIRS), and healthy subjects were provided by Vall d’Hebron University Hospital in Spain. Samples were tested in a blind manner to eliminate bias.
3：List of Experimental Equipment and Materials
Au-NHA sensor chips,Portable optical reader comprising a narrowband LED source, custom-built aluminum holder for the nanoplasmonic chip, a 50× objective, and a CMOS camera,Antibody-conjugated Au-NPs,Microarray printer for antibody spotting
4：Experimental Procedures and Operational Workflow
The bioassay involves mixing antibody functionalized Au-NP suspension directly with blood serum and injecting the mixture into the measurement chamber. The plasmonic imager records the binding of Au-NPs to the Au-NHA surface, with images acquired and processed for quantification.
5：Data Analysis Methods
Image processing was performed using custom Matlab functions to quantify bound NPs. The percentage number of pixels in an area darker than a fixed intensity threshold was used for quantification, with signals corrected by subtracting background signals.
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