Rapid quantification of thiocyanate in milk samples using a universal paper-based SERS sensor.

Sodium thiocyanate (NaSCN) is a naturally antibacterial component in milk, but excessive consumption of thiocyanate may cause potential risks to human health. Currently available methods for the detection of thiocyanate have some disadvantages such as poor sensitivity and high price. Here, we report a robust and cost-effective method to detect NaSCN based on paper substrates deposited with in situ reduced Ag nanoparticles by surface-enhanced Raman spectroscopy (SERS). Densely packed multilayer AgNPs provide uniform narrow nanogaps, which exponentially enhance the Raman signals. Moreover, these homogeneous narrow hotspots ensure that this method has high sensitivity (the limit of detection is 10-12 mol L-1), a wide linear range (from 10-9 mol L-1 to 10-4 mol L-1), and remarkable reproducibility (the coefficient of variation within a SERS sensor is 6.5%). Spiked milk samples were detected and the recovery rates of NaSCN were in the range of 95.1%-108.0%. This paper-based SERS sensor offers great potential for sensitive NaSCN detection and milk analysis.

Recent Progress in Detection and Profiling of Cancer Cell-Derived Exosomes.

Exosomes, known as nanometer-sized vesicles (30-200 nm), are secreted by many types of cells. Cancer-derived exosomes have great potential to be biomarkers for early clinical diagnosis and evaluation of cancer therapeutic efficacy. Conventional detection methods are limited to low sensitivity and reproducibility. There are hundreds of papers published with different detection methods in recent years to address these challenges. Therefore, in this review, pioneering researches about various detection strategies are comprehensively summarized and the analytical performance of these tests is evaluated. Furthermore, the exosome molecular composition (protein and nucleic acid) profiling, a single exosome profiling, and their application in clinical cancer diagnosis are reviewed. Finally, the principles and applications of machine learning method in exosomes researches are presented.