Facile preparation of biocompatible and antibacterial water-soluble films using polyvinyl alcohol/carboxymethyl chitosan blend fibers via centrifugal spinning.

Water-soluble polyvinyl alcohol/carboxymethyl chitosan (PVA/CMCS) blend fiber films were successfully prepared using a plane-collection centrifugal spinning machine. The addition of CMCS significantly increased the shear viscosity of the PVA/CMCS blend solution. The effects of spinning temperature on the shear viscosity and the centrifugal spinnability of PVA/CMCS blend solution were discussed. The PVA/CMCS blend fibers were uniform, and their average diameters ranged from 1.23 µm to 29.01 µm. It was found that the CMCS was distributed evenly in the PVA matrix and increased the crystallinity of PVA/CMCS blend fiber films. The hydrogen bonds between the hydroxyl group of PVA and the carboxymethyl group of CMCS were also detected. An in vitro cell study of human skin fibroblast cells on the PVA/CMCS blend fiber films confirmed biocompatibility. The maximum tensile strength and elongation at break of PVA/CMCS blend fiber films could reach 3.28 MPa and 29.52 %, respectively. The colony-plate-count tests indicated that the PVA16-CMCS2 presented 72.05 % and 21.36 % antibacterial rates against Staphylococcus aureus (104 CFU/mL) and Escherichia coli (103 CFU/mL), respectively. These values indicated that the newly prepared PVA/CMCS blend fiber films are promising materials for cosmetic and dermatological applications.

Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM.

In practical electrocardiogram (ECG) monitoring, there are some challenges in reducing the data burden and energy costs. Therefore, compressed sensing (CS) which can conduct under-sampling and reconstruction at the same time is adopted in the ECG monitoring application. Recently, deep learning used in CS methods improves the reconstruction performance significantly and can removes of some of the constraints in traditional CS. In this paper, we propose a deep compressive-sensing scheme for ECG signals, based on modified-Inception block and long short-term memory (LSTM). The framework is comprised of four modules: preprocessing; compression; initial; and final reconstruction. We adaptively compressed the normalized ECG signals, sequentially using three convolutional layers, and reconstructed the signals with a modified Inception block and LSTM. We conducted our experiments on the MIT-BIH Arrhythmia Database and Non-Invasive Fetal ECG Arrhythmia Database to validate the robustness of our model, adopting Signal-to-Noise Ratio (SNR) and percentage Root-mean-square Difference (PRD) as the evaluation metrics. The PRD of our scheme was the lowest and the SNR was the highest at all of the sensing rates in our experiments on both of the databases, and when the sensing rate was higher than 0.5, the PRD was lower than 2%, showing significant improvement in reconstruction performance compared to the comparative methods. Our method also showed good recovering quality in the noisy data.