Dual selective sensor for exosomes in serum using magnetic imprinted polymer isolation sandwiched with aptamer/graphene oxide based FRET fluorescent ignition.

The selective and sensitive detection of cancerous exosomes in serum is critical for early disease diagnosis and improved prognosis. Previous exosome-related research has been limited by a lack of well-understanding in exosomes as well as the challenging background interference of body fluid. Molecularly imprinted polymers (MIPs) and nucleic acid aptamers can be regarded as the two alternatives to antibodies. When using imprinted polymer technology, comprehensive and precise information about the target constituents is not required. In this study, a novel kind of dual selective fluorescent nanosensor for the poorly characterized exosomes was constructed by integrating magnetic MIP selective exosome capture sandwiched with an aptamer/graphene oxide fluorescence resonance energy transfer system (FRET) based selective 'turn-on' exosome labeling heterogeneously. The overall strategy performance was successively evaluated using lysozyme and exosomes as targets. Good linearity and high sensitivity achieved were demonstrated. The LOD of exosomal detection in serum was 2.43 × 106 particles/mL, lower than other immunology based detection methods. The discrimination between serum from breast cancer patients and healthy people was also primarily studied. In conclusion, the developed sensor with outstanding selectivity, high detection sensitivity, simplicity, low cost, and wide applicability for known or unknown targets present significant potential in challenging clinical diagnosis.

Classification of red blood cell aggregation using empirical wavelet transform analysis of ultrasonic radiofrequency echo signals.

Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machine learning technique based on an adaptive analysis of ultrasonic radiofrequency (RF) echo signals in blood is proposed, and its feasibility for classifying RBC aggregation is explored. Using an adaptive empirical wavelet transform (EWT) analysis, the ultrasonic RF signals are decomposed into a series of empirical mode functions (EMFs); then, dominant empirical mode functions (DEMFs) are selected from the series. Six statistical characteristics, including the mean, variance, median, kurtosis, root mean square (RMS), and skewness are calculated for the locally normalized DEMFs, aiming to form primary feature vectors. Random forest (RDF) and support vector machine (SVM) classifiers are trained with the given feature vectors to obtain prediction models for RBC classification. Ultrasonic RF echo signals are acquired from five groups of six types of porcine blood samples with average numbers of aggregated RBCs of 1.04, 1.20, 1.83, 2.31, 2.72, and 4.28, respectively, to test the classification performance of the proposed method. The best subset with regard to the variance, kurtosis, and RMS is determined according to the maximum accuracy based on the RDF and SVM classifiers. The classification accuracies are 84.03 ±â€¯3.13% for the RDF classifier, and 85.88 ±â€¯2.99% for the SVM classifier. The mean classification accuracy of the SVM classifier is 1.85% better than that of the RDF classifier. In conclusion, the machine learning method is useful for the discrimination of varying degrees of RBC aggregation, and has potential for use in characterizing and monitoring the RBC aggregation in vessels.

[Advances in microfluidic chip-based extracellular vesicle separation].

Extracellular vesicles (EVs) are hemispherical vesicles that have a lipid bilayer. Studies have shown that EVs have important biological functions. The amount, types, and compositional changes of proteins, lipids and ribonucleic acids are closely related to diseases. The separation and capture of EVs from the complicated body fluid samples is a prerequisite for medical research and liquid biopsy based on EVs. However, presently the majority of EVs separation and capture still use the traditional separation methods with low purity and low efficiency. Therefore, efficient and highly selective EVs separation method is in urgent need. To meet this challenge, advanced microfluidic chip technology, which has the advantages of miniaturization, integration, and automation, can be utilized. The development of EV separation technology combined with microfluidic chips has become the focus of research. This paper summarizes the latest research progress in this area.

Microfluidic chip coupled with optical biosensors for simultaneous detection of multiple analytes: A review.

This article reviews the recent advances in microfluidic-chip integrated optical biosensors for simultaneous detection of multiple analytes. In particular, the principles and recent progress in different kinds of multiplex optical biosensors and their biological application were reviewed comprehensively. Sensors based on multiplexed detection have absolute advantages in analysis throughput than single assay. The microfluidic chip, a type of micro-total analysis system (µTAS), provides an ideal platform for integration of high-throughput biosensors. Compared with electronic biosensors, benefitted from the technical development in Micro-Electro-Mechanical System, there have been greater advances in the fabrication of optical sensors and microfluidic chip, and then promoting microfluidic-chip integrated optical biosensors for simultaneous detection of multiple analytes.

Recent advances in microfluidic chip integrated electronic biosensors for multiplexed detection.

Utilizing biosensors for multiplexed detection can greatly increase analysis throughput and thus, the amount of information obtained in a single assay. The microfluidic chip, a type of micro-total analysis system (µTAS), has provided a necessary platform for portable and high-throughput biosensors. Biosensors and microfluidic chips are powerful individually, and their super combination is very meaningful for analytical especially for biological applications. In this paper, every kind of microfluidic-chip-integrated electronic biosensors including some emerging technologies for simultaneous detection of multiple analytes are reviewed. Different ways to reduce or avoid cross-talking and more efforts to achieve lab on chip multisensors were also introduced to help readers form a general idea of current developments in different angles.