Graphene quantum dots as anti-inflammatory therapy for colitis

While graphene and its derivatives have been suggested as a potential nanomedicine in several biomimetic models, their specific roles in immunological disorders still remain elusive. Graphene quantum dots (GQDs) may be suitable for treating intestinal bowel diseases (IBDs) because of their low toxicity in vivo and ease of clearance. Here, GQDs are intraperitoneally injected to dextran sulfate sodium (DSS)-induced chronic and acute colitis model, and its efficacy has been confirmed. In particular, GQDs effectively prevent tissue degeneration and ameliorate intestinal inflammation by inhibiting T(H)1/T(H)17 polarization. Moreover, GQDs switch the polarization of macrophages from classically activated M1 to M2 and enhance intestinal infiltration of regulatory T cells (T-regs). Therefore, GQDs effectively attenuate excessive inflammation by regulating immune cells, indicating that they can be used as promising alternative therapeutic agents for the treatment of autoimmune disorders, including IBDs.

A human iPSC-derived inducible neuronal model of Niemann-Pick disease, type C1

BACKGROUND: Niemann-Pick disease, type C (NPC) is a childhood-onset, lethal, neurodegenerative disorder caused by autosomal recessive mutations in the genes NPC1 or NPC2 and characterized by impaired cholesterol homeostasis, a lipid essential for cellular function. Cellular cholesterol levels are tightly regulated, and mutations in either NPC1 or NPC2 lead to deficient transport and accumulation of unesterified cholesterol in the late endosome/lysosome compartment, and progressive neurodegeneration in affected individuals. Previous cell-based studies to understand the NPC cellular pathophysiology and screen for therapeutic agents have mainly used patient fibroblasts. However, these do not allow modeling the neurodegenerative aspect of NPC disease, highlighting the need for an in vitro system that permits understanding the cellular mechanisms underlying neuronal loss and identifying appropriate therapies. This study reports the development of a novel human iPSC-derived, inducible neuronal model of Niemann-Pick disease, type C1 (NPC1). RESULTS: We generated a null i3Neuron (inducible × integrated × isogenic) (NPC1 CONCLUSION: Our data demonstrate the utility of this new cell line in high-throughput drug/chemical screens to identify potential therapeutic agents. The NPC

Nanogrooved microdiscs for bottom-up modulation of osteogenic differentiation

Grooved topographical features have effectively modulated cell differentiation on two-dimensional substrates. To transpose patterning into a 3D environmment, nanogrooved microdiscs, "topodiscs", are produced as cell carriers for bottom-up cell-mediated assembly. While enhancing cell proliferation, topodiscs led to the formation of bone-like aggregates, even in culture medium lacking osteoinductive factors.publishe

Blind channel identification for sparse multipath channels

Blind identification of a digital wireless communication channel is an important issue of communication system design especially in a frequency selective multipath fading channel environment. The essential parameters to be identified include the multipath delays and gains, the number of multipath propagations, the frequency offset between the carrier frequency and the reference frequency of the receiver, and the sampling phase. The problem of blindly identifying the unknown parameters is separated into two parts: identifying the multipath channel parameters including the sampling phase and identifying the frequency offset. First I present a novel approach based on the mode estimation algorithm to efficiently estimate the multipath channel parameters under the assumption that the frequency offset is exactly known. Then I present a joint blind estimation algorithm of the frequency offset and the multipath channel parameters. These algorithms are particularly useful in sparse multipath channels. Conventional methods do not fully exploit inherent structure present in the combined channel response; the excess number of parameters to be estimated by conventional methods makes the identification difficult. By utilizing a priori knowledge of the transmission data pulse, parameters directly related to the multipath propagations are extracted, and hence the number of estimation parameters are significantly reduced. The channel identification problem is then posed in the standard modal analysis framework to enable the use of well-established high-resolution harmonic retrieval techniques. The mode parameters are unraveled to obtain the multipath channel parameters. Simulation results show significant improvement over existing approaches

Robust Localization System Using Vector Combination in Wireless Sensor Networks

This paper proposes a vector-based localization system that uses both distance and angle information. In wireless sensor networks, the positions of nodes are commonly determined by a range-based localization system using distance information. If both distance and angle information are available, it is possible to improve the accuracy of estimating the positions of nodes compared to a positioning system with only distance information. Existing studies using distance and angle information assume that all the nodes are directly connected to one another and do not consider a method for measuring angle information between the nodes that are not directly connected. However, this assumption may not be valid for real-world wireless sensor networks especially with a large number of nodes having a limited communication range. The proposed localization algorithm solves this problem by a vector combination that transforms the vectors on the local coordinate system to the network-wide global coordinate system. The proposed algorithm is shown to be robust especially even in a network with 1-edge connectivity. Simulation results show that the proposed algorithm has up to 70% higher positioning accuracy compared to the existing iterative range-based algorithm such as MDS-MAP(C,R)

Therapeutic Features and Updated Clinical Trials of Mesenchymal Stem Cell (MSC)-Derived Exosomes

Identification of the immunomodulatory and regenerative properties of mesenchymal stem cells (MSCs) have made them an attractive alternative therapeutic option for diseases with no effective treatment options. Numerous clinical trials have followed; however, issues such as infusional toxicity and cellular rejection have been reported. To address these problems associated with cell-based therapy, MSC exosome therapy was developed and has shown promising clinical outcomes. MSC exosomes are nanosized vesicles secreted from MSCs and represent a non-cellular therapeutic agent. MSC exosomes retain therapeutic features of the cells from which they originated including genetic material, lipids, and proteins. Similar to MSCs, exosomes can induce cell differentiation, immunoregulation, angiogenesis, and tumor suppression. MSC exosomes have therefore been employed in several experimental models and clinical studies. Here, we review the therapeutic potential of MSC-derived exosomes and summarize currently ongoing clinical trials according to disease type. In addition, we propose several functional enhancement strategies for the effective clinical application of MSC exosome therapy

Therapeutic Features and Updated Clinical Trials of Mesenchymal Stem Cell (MSC)-Derived Exosomes

Identification of the immunomodulatory and regenerative properties of mesenchymal stem cells (MSCs) have made them an attractive alternative therapeutic option for diseases with no effective treatment options. Numerous clinical trials have followed; however, issues such as infusional toxicity and cellular rejection have been reported. To address these problems associated with cell-based therapy, MSC exosome therapy was developed and has shown promising clinical outcomes. MSC exosomes are nanosized vesicles secreted from MSCs and represent a non-cellular therapeutic agent. MSC exosomes retain therapeutic features of the cells from which they originated including genetic material, lipids, and proteins. Similar to MSCs, exosomes can induce cell differentiation, immunoregulation, angiogenesis, and tumor suppression. MSC exosomes have therefore been employed in several experimental models and clinical studies. Here, we review the therapeutic potential of MSC-derived exosomes and summarize currently ongoing clinical trials according to disease type. In addition, we propose several functional enhancement strategies for the effective clinical application of MSC exosome therapy

Artificial Neural Network–Based Control of a Variable Refrigerant Flow System in the Cooling Season

This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next control cycle, was embedded into the control algorithm. By comparing the predicted energy for the different set-point combinations of the control variables, the control algorithm can determine the most energy-effective set-points to optimally operate the cooling system. Two major processes were conducted in the development process. The first process was to develop the predictive control algorithm which embedded the ANN model. The second process involved performance tests of the control algorithm in terms of prediction accuracy and energy efficiency in computer simulation programs. The results revealed that the prediction accuracy between simulated and predicted outcomes proved to have a low coefficient of variation root mean square error (CVRMSE) value (10.30%). In addition, the predictive control algorithm markedly saved the cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings suggest that the ANN model and the control algorithm showed potential for the prediction accuracy and energy-effectiveness of VRF cooling systems

Quality Assessment Method Based on a Spectrometer in Laser Beam Welding Process

For the automation of a laser beam welding (LBW) process, the weld quality must be monitored without destructive testing, and the quality must be assessed. A deep neural network (DNN)-based quality assessment method in spectrometry-based LBW is presented in this study. A spectrometer with a response range of 225–975 nm is designed and fabricated to measure and analyze the light reflected from the welding area in the LBW process. The weld quality is classified through welding experiments, and the spectral data are thus analyzed using the spectrometer, according to the welding conditions and weld quality classes. The measured data are converted to RGB (red, green, blue) values to obtain standardized and simplified spectral data. The weld quality prediction model is designed based on DNN, and the DNN model is trained using the experimental data. It is seen that the developed model has a weld-quality prediction accuracy of approximately 90%