Search

An electrochemical aptamer-based sensor was designed to detect tetracycline (Tet) in chicken ham. First, polyacrylonitrile (PAN) polymer was used to prepare electrospun PAN nanofibers. PAN nanofibers were heat treated to form electrospun carbon nanofiber (CNF) mat. The electrospun CNF mat electrode was modified with Au and the aptamer of Tet was placed on the mat electrode. Raman spectroscopy and scanning electron microscopy equipped with energy-dispersive spectroscopy were used to identify the structure and morphology of electrospun CNFs, respectively. The behavior of CNF mat electrode was evaluated via cyclic voltammetry. The designed aptamer biosensor had a good repeatability, reproducibility, stability, and specific function for the detection of Tet in chicken ham. This biosensor was able to detect the Tet in the linear range of 1 × 10–9 M to 1 × 10–4 M with the detection limit of 1.2 × 10–10 M and preserve about 95% of its primary response after 20 days. In addition, the recoveries of Tet were achieved ranging from 96.92 to 104.31%. [ABSTRACT FROM AUTHOR]

Chitosan/polyethylene oxide (PEO) solution makes electrospun nanofibers with decreased beads and diameters in comparison with lonely chitosan (CS). The aim of this work was to find an artificial neural network (ANN) model for predicting the chitosan/PEO blend electrospun nanofiber diameter. Chitosan/PEO concentration ratio, distance between nozzle tip and collector, applied voltage, and flow rate were considered as input variables, and chitosan/PEO blend electrospun nanofiber diameter was considered as output variable. Scanning electron microscopy images indicated that electrospun nanofiber diameter was approximately 50-185 nm. For increasing validity, k-fold cross validation method was applied to dataset. The ANN technique was used for training and testing via fivefold of dataset. The best results of prediction were obtained via network with three hidden layers including 10, 15, and 5 nodes in each layer, respectively. The mean square error (MSE) and correlation coefficient between the observed and predicted thickness of the nanofibers in the chosen model were about 0.0707 and 0.9630, respectively, indicating the ANN technique validity in the prediction procedure. For the analysis of interactions between the involved electrospinning parameters and nanofiber diameter, 3D graphs in various levels were plotted. In conclusion, the results indicated that using the prediction process via ANN could be relevant in the decision to produce nanofibers with desired shape and diameter via electrospinning. [ABSTRACT FROM AUTHOR]

Among polymers, polyaniline (PANi) has been introduced as a good candidate for muscle regeneration due to high conductivity and also biocompatibility. Herein, for the first time, we report the use of electrospun nanofibrous membrane of PAN-PANi as efficient scaffold for muscle regeneration. The prepared PAN-PANi electrospun nanofibrous membrane was characterized by scanning electron microscopy (SEM), Attenuated total reflectance fourier transform infrared spectroscopy (ATR-FTIR) and tensile examination. The softer scaffolds of non-composite electrospun nanofibrous PAN govern a higher rate of cell growth in spite of lower differentiation value. On the other hand, PAN-PANi electrospun nanofibrous membrane exposed high cell proliferation and also differentiation value. Thank to the conductive property and higher Young's modulus of composite type due to the employment of PANi, satellite cells were induced into more matured form as analyzed by Real-Time PCR. On the other hand, grafting of composite nanofibrous electrospun scaffold with gelatin increased the surface stiffness directing satellite cells into lower cell proliferation and highest value of differentiation. Our results for first time showed the significant role of combination between conductivity, mechanical property and surface modification of PAN-PANi electrospun nanofibers and provid new insights into most biocompatible scaffolds for muscle tissue engineering. Graphical abstract: The schematic figure conveys the effective combination of conductive and surface stiffness on muscle tissue engineering. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

The aim of this work was to evaluate the effective parameters for prediction of the electrospun gelatin nanofibers diameter using artificial neural network (ANN) technique. The various sets of electrospinning process including temperature, applied voltage and polymer and solvent concentrations were designed to produce pure gelatin nanofibers. The obtained results by analyzing Scanning Electron Microscopy (SEM) images indicated that the produced nanofibers diameter was in the range of 85 to 750 nm. Due to the volume of the data, k fold cross-validation method was used for data setting. Data were divided into the five categories and trained and tested using ANN technique. The results indicated that the network including 4 input variables, 3 hidden layers with 10, 18 and 9 nodes in each layers, respectively, and one output layer had the best performance in the testing sets. The mean squared error (MSE) and linear regression (R) between observed and predicted nanofibers diameter were 0.1531 and 0.9424, respectively. The obtained results demonstrated that the selected neural network model had acceptable performance for evaluating involved parameters and prediction of nanofibers diameter. [ABSTRACT FROM AUTHOR]