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Graphene is another allotrope of carbon with two-dimensional monolayer honeycomb. Owing to its special characteristics including electrical, physical and optical properties, graphene is known as a more suitable candidate compared to other materials to be used in the sensor application. It is possible, moreover, to use biosensor by using electrolyte-gated field effect transistor based on graphene (GFET) to identify the alterations in charged lipid membrane properties. The current article aims to show how thickness and charges of a membrane electric can result in a monolayer graphene-based GFET while the emphasis is on the conductance variation. It is proposed that the thickness and electric charge of the lipid bilayer (LLP and QLP) are functions of carrier density, and to find the equation relating these suitable control parameters are introduced. Artificial neural network algorithm as well as support vector regression has also been incorporated to obtain other models for conductance characteristic. The results comparison between analytical models, artificial neural network and support vector regression with the experimental data extracted from previous work show an acceptable agreement. [ABSTRACT FROM AUTHOR]

Different forms of CNT delivery have been discovered with several biomedical functions during past decades. The mechanisms of the cellular uptake of CNTs are mainly maintained due to the chemical nature, the cell type, and the features of the molecules, which are used to functionalize the nanotube exterior. Since single-wall carbon Nanotube (SWCNT) has unique chemical and physical properties, it is a great applicant for pH sensing. In addition, ion sensitive FET (ISFET) base on nanostructured SWCNT have covered a new method to help genetic investigators restructure metabolic pathways in cells, recognize the progression of disease, and expand diagnostics and therapeutics. Particularly, because PH sensing is very crucial for the constancy of enzymes, it is essential to extend the cost efficient types of this sensing. In this research, the conductance changes of the CNT-based ISFET device with different pH values can be modelled by ion concentration of the solution. In addition, the electrical current of channel is imagined as a function of pH levels, which can be controlled by a control factor (α). Thus, ISFET based nanostructured SWCNT is proposed focusing on the area of electrical detection of hydrogen ions of the electrolyte membrane. Besides, electrical detection of hydrogen ion applications is suggested to be used by modelling the delivery of SWCNT sheets. In the end, after comparing the proposed model and experimental data, it has been reported that there is a good compatibility between them. [ABSTRACT FROM AUTHOR]

Alzheimer's disease, a progressive neurological condition, is associated with various internal and external risk factors in the disease's early stages. Early diagnosis of Alzheimer's disease is essential for treatment management. Circulating exosomal microRNAs could be a new class of valuable biomarkers for early Alzheimer's disease diagnosis. Different kinds of biosensors have been introduced in recent years for the detection of these valuable biomarkers. Isolation of the exosomes is a crucial step in the detection process which is traditionally carried out by multi-step ultrafiltration. Microfluidics has improved the efficiency and costs of exosome isolation by implementing various effects and forces on the nano and microparticles in the microchannels. This paper reviews recent advancements in detecting Alzheimer's disease related exosomal microRNAs based on methods such as electrochemical, fluorescent, and SPR. The presented devices' pros and cons and their efficiencies compared with the gold standard methods are reported. Moreover, the application of microfluidic devices to detect Alzheimer's disease related biomarkers is summarized and presented. Finally, some challenges with the performance of novel technologies for isolating and detecting exosomal microRNAs are addressed. [ABSTRACT FROM AUTHOR]