Differentiation of definitive endoderm from human induced pluripotent stem cells on hMSCs feeder in a defined medium

Background: The Definitive Endoderm (DE) differentiation using the undefined media and non-human feeders can cause contaminations in the generated cells for therapeutic applications. Therefore, generating safer and more appropriate DE cells is needed. This study compared five different methods to establish an appropriate method for inducing an efficient DE differentiation from Human Induced Pluripotent Stem Cells (hiPSCs) on an appropriate feeder in a more defined medium. Methods: Human Induced Pluripotent Stem Cells (hiPSCs) were cultured on inactivated feeders. Passaged hiPSCs, without feeder, were incubated for three days with Activin-A and different endodermal differentiation media including 1-FBS, 2-B27, 3- ITS and albumin fraction-V, 4-B27 and ITS and 5-like the third medium. The feeder cells in the first four methods were Mouse Embryonic Fibroblasts (MEFs) and in the fifth method were human adult bone marrow Mesenchymal Stem Cells (hMSCs). DE markers FOXA2, SOX17 and CXCR4 and also pluripotency marker OCT4 were evaluated using qRT-PCR, as well as FOXA2 by the immunocytochemistry. Results: QRT-PCR analysis showed that after three days, the expression levels of DE and pluripotency markers in the differentiated hiPSCs among all five groups did not have any significant differences. Similarly, the immunocytochemistry analysis demonstrated that the differentiated hiPSCs expressed FOXA2, with no significant differences. Conclusion: Despite this similarity in the results, the third differentiation medium has more defined and cost effective components. Furthermore, hMSC, a human feeder, is safer than MEF. Therefore, the fifth method is preferable among other DE differentiation methods and can serve as a fundamental method helping the development of regenerative medicine. © 2016, Avicenna Journal of Medical Biotechnology. All rights reserved

A multiphysics-based artificial neural networks model for atherosclerosis

Atherosclerosis is a medical condition involving the hardening and/or thickening of arteries' walls. Mathematical multi-physics models have been developed to predict the development of atherosclerosis under different conditions. However, these models are typically computationally expensive. In this study, we used machine learning techniques, particularly artificial neural networks (ANN), to enhance the computational efficiency of these models. A database of multi-physics Finite Element Method (FEM) simulations was created and used for training and validating an ANN model. The model is capable of quick and accurate prediction of atherosclerosis development. A remarkable computational gain is obtained using the ANN model compared to the original FEM simulations

On the Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography (CCERT) with Entropy Priors

Regularization with priors is an effective approach to solve the ill-posed inverse problem of electrical tomography. Entropy priors have been proven to be promising in radiation tomography but have received less attention in the literature of electrical tomography. This work aims to investigate the image reconstruction of capacitively coupled electrical resistance tomography (CCERT) with entropy priors. Four types of entropy priors are introduced, including the image entropy, the projection entropy, the image-projection joint entropy, and the cross-entropy between the measurement projection and the forward projection. Correspondingly, objective functions with the four entropy priors are developed, where the first three are implemented under the maximum entropy strategy and the last one is implemented under the minimum cross-entropy strategy. Linear back-projection is adopted to obtain the initial image. The steepest descent method is utilized to optimize the objective function and obtain the final image. Experimental results show that the four entropy priors are effective in regularization of the ill-posed inverse problem of CCERT to obtain a reasonable solution. Compared with the initial image obtained by linear back projection, all the four entropy priors make sense in improving the image quality. Results also indicate that cross-entropy has the best performance among the four entropy priors in the image reconstruction of CCERT

Application of deep neural network to the reconstruction of two-phase material imaging by capacitively coupled electrical resistance tomography

A convolutional neural network (CNN)-based image reconstruction algorithm for two-phase material imaging is presented and verified with experimental data from a capacitively coupled electrical resistance tomography (CCERT) sensor. As a contactless version of electrical resistance tomography (ERT), CCERT has advantages such as no invasion, low cost, no radiation, and rapid response for two-phase material imaging. Besides that, CCERT avoids contact error of ERT by imaging from outside of the pipe. Forward modeling was implemented based on the practical circular array sensor, and the inverse image reconstruction was realized by a CNN-based supervised learning algorithm, as well as the well-known total variation (TV) regularization algorithm for comparison. The 2D, monochrome, 2500-pixel image was divided into 625 clusters, and each cluster was used individually to train its own CNN to solve the 16 classes classification problem. Inherent regularization for the assumption of binary materials enabled us to use a classification algorithm with CNN. The iterative TV regularization algorithm achieved a close state of the two-phase material reconstruction by its sparsity-based assumption. The supervised learning algorithm established the mathematical model that mapped the simulated resistance measurement to the pixel patterns of the clusters. The training process was carried out only using simulated measurement data, but simulated and experimental tests were both conducted to investigate the feasibility of applying a multi-layer CNN for CCERT imaging. The performance of the CNN algorithm on the simulated data is demonstrated, and the comparison between the results created by the TV-based algorithm and the proposed CNN algorithm with the real-world data is also provided

A Wideband Contactless Electrical Impedance Tomography System

This work focuses on the development of a wideband contactless electrical impedance tomography (EIT) system. The system is developed from the aspects of the multifrequency capacitively coupled electrical impedance tomography (CCEIT) hardware, the impedance calculation model and the system evaluation. The hardware includes a 12-electrode CCEIT sensor, 6 sensing modules, a data acquisition module, and a personal computer (PC). The impedance calculation model is established by combining the mechanism modeling of the integrated circuits (ICs) and the empirical modeling of the measurement data with the least squares (LS) method. Experiments were carried out to evaluate the developed system, including the signal-to-noise ratio (SNR), the impedance measurement accuracy and the imaging performance. Experimental results show that the system achieves an SNR above 65.00 dB for the frequencies up to 20 MHz. Impedance measurement results indicate that the system has good impedance measurement accuracy at frequencies below 10 MHz and acceptable impedance measurement accuracy at 10 MHz - 20 MHz. It has particularly good performance at several specific frequencies, which can also serve as a high-performance single-frequency contactless EIT device. Imaging results show that the spectroscopic images reconstructed by the developed system are consistent with the actual distributions. Few types of research on contactless multifrequency EIT systems have been reported. So, this work is of great significance for further development and practical application of the newly emerged contactless EIT technique

Design of heat sinks for wearable thermoelectric generators to power personal heating garments: A numerical study

To mitigate climate change attributed to the built environments, there have been tremendous efforts to improve air conditioning systems in the buildings. The possibility of harvesting body heat as a renewable energy source to power a wearable personal heating system is investigated. The aim of this study is to integrate a wearable personal heating system with a thermoelectric generator (TEG) that harvests the body heat which is used to convert it into electricity. Moreover, the interaction between the TEG configuration and power output is studied. The power generation of TEG system is obtained by COMSOL Multiphysics software. The simulation results concluded that all the four proposed heat sink configurations can improve the power output of the wearable TEG at 1.4 m/s and 3m/s compared to that of the reference model. Furthermore, the perforated and trapezium shapes of heat sinks have a significantly better performance in comparison to conventional heat sinks

N-acetylcysteine compared to metformin, improves the expression profile of growth differentiation factor-9 and receptor tyrosine kinase c-kit in the oocytes of patients with polycystic ovarian syndrome

Background: Paracrine disruption of growth factors in women with polycystic ovarian syndrome (PCOS) results in production of low quality oocyte, especially following ovulation induction. The aim of this study was to investigate the effects of metformin (MET), N-acetylcysteine (NAC) and their combination on the hormonal levels and expression profile of GDF-9, BMP-15 and c-kit, as hallmarks of oocyte quality, in PCOS patients. Materials and Methods: This prospective randomized, double-blind, placebo controlled trial aims to study the effects of MET, NAC and their combination (MET+NAC) on expression of GDF-9, BMP-15 and c-kit mRNA in oocytes [10 at the germinal vesicle (GV) stage, 10 at the MI stage, and 10 at the MII stage from per group] derived following ovulation induction in PCOS. Treatment was carried out for six weeks, starting on the third day of previous cycle until oocyte aspiration. The expression of GDF9, BMP15 and c-kit were determined by quantitative real time polymerase chain reaction (RT-qPCR) and western blot analysis. Data were analyzed with one-way ANOVA. Results: The follicular fluid (FF) level of c-kit protein significantly decreased in the NAC group compared to the other groups. Significant correlations were observed between the FF soluble c-kit protein with FF volume, androstenedione and estradiol. The GDF-9 expression in unfertilized mature oocytes were significantly higher in the NAC group compared to the other groups (P<0.001). Similar difference was not observed between the MET, NAC+MET and control groups. The c-kit expression in unfertilized mature oocytes were significantly lower in the NAC group compared to the other groups (P<0.001). Similar difference was not observed between the MET, NAC+MET and control groups (Registration number: IRCT201204159476N1). Conclusion: We concluded that NAC can improve the quality of oocytes in PCOS. © 2017, Royan Institute (ACECR). All rights reserved

An Image Reconstruction Algorithm for A 12-electrode Capacitively Coupled Electrical Resistance Tomography System Under 2-electrode Excitation Pattern

An image reconstruction algorithm, which is developed for a 12-electrode capacitively coupled electrical resistance tomography (CCERT) system under 2-electrode excitation strategy, is proposed. Based on L-curve and Reginska’s method, truncated singular value decomposition (TSVD) is used to reconstruct the initial image. The algebraic reconstruction technique (ART) algorithm is used to obtain the final reconstructed image. Image reconstruction experiments are conducted by a 12-electrode CCERT system. The proposed algorithm (TSVD + ART) is compared with conventional linear back projection (LBP), Tikhonov, Landweber, ART, simultaneous iterative reconstruction technique (SIRT), total variation (TV), conjugate gradient (CG), and TSVD to evaluate its image reconstruction performance. Image reconstruction results show the proposed algorithm (TSVD + ART) can effectively exploit the advantages of 2-electrode excitation strategy and hence realize higher quality image reconstruction. Under 2-electrode excitation strategy, the proposed algorithm has an obvious advantage over conventional image reconstruction algorithms. Under 1-electrode excitation strategy, the image reconstruction performance is comparable or slightly improved compared with that of conventional image reconstruction algorithms. Image reconstruction results also indicate the TSVD is effective to obtain the initial reconstructed image. The quality of the initial reconstructed image can be significantly improved compared with that of classic LBP, either under 2-electrode excitation strategy or 1-electrode excitation strategy

Void fraction measurement of gas-liquid two-phase flow with a 12-electrode contactless resistivity array sensor under different excitation patterns

This work focuses on the void fraction measurement of gas–liquid two-phase flow by a 12-electrode contactless resistivity array sensor. Such a sensor, which can realize different excitation patterns, is developed here. Five different excitation patterns (with 1, 2, 3, 4 or 5 electrodes) and three two-phase distributions (bubble flow, stratified flow and annular flow) are investigated. Two data processing approaches, the data average method and the principal component regression (PCR) method, are used to establish models of void fraction measurement and hence to implement it. Experiments on void fraction measurement are carried out with the 12-electrode contactless resistivity array sensor. The results show that the void fraction measurement performances are different under different excitation patterns. Among the five different excitation patterns studied, the one with five electrodes has the best performance and the absolute values of void fraction measurement errors of the three two-phase distributions are all less than 5.0% (using the data average method) and 3.0% (using the PCR method). Research results indicate that the 5-electrode excitation pattern + PCR combination is a new effective way to implement void fraction measurement of gas–liquid two-phase flow with the 12-electrode contactless resistivity array sensor.<br/

Stem cell-based approach for the treatment of Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative brain disorder which is around 1.5 times more common in men than in women. Currently, drug medications, surgery, and lifestyle changes are common approaches to PD, while all of them focused on reducing the symptoms. Therefore, regenerative medicine based on stem cell (SC) therapies has raised a promising hope. Various types of SCs have been used in basic and experimental studies relevant to PD, including embryonic pluripotential stem cells, mesenchymal (MSCs) and induced pluripotent SCs (iPSCs). MSCs have several advantages over other counterparts. They are easily accessible which can be obtained from various tissues such as bone marrow, adipose tissue, peripheral blood, etc. with avoiding ethical problems. Therefore, MSCs is attractive clinically because there are no related ethical and immunological concerns . Further studies are needed to answer some crucial questions about the different issues in SC therapy. Accordingly, SC-based therapy for PD also needed more complementary evaluation in both basic and clinical study areas