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Left atrial gateway perform: A short evaluation.
BACKGROUND Hip osteoarthritis is a major musculoskeletal disorder in the elderly. Evidence is given for the efficacy of exercise interventions in terms of self-reported physical functioning and pain. However, it has not yet been fully examined whether exercises influence gait. RESEARCH QUESTION The purpose of this RCT therefore was to evaluate effects of a 12-week exercise program on kinematic gait variables in subjects with hip osteoarthritis. METHODS 210 participants were randomly assigned to exercise, non-treated control, or placebo ultrasound groups. The 12-week exercise intervention combined a weekly group session with home-based exercises (2/week), which entailed exercises for motor learning, flexibility, strengthening, and balance. Placebo ultrasound was given once a week. A 6-camera motion capture system was used for data collection. Data were derived from shod walking at self-selected speeds. Spatio-temporal and hip and knee joint angles of the stance phase were calculated. Data were averaged across five trials. Measurements were taken prior to and immediately after the intervention period. ANOVA/Kruskall-Wallis-Tests were used to analyze between-group effects for differences between test days. Pairwise comparisons were subsequently conducted in case of significant model effects. Data were analyzed per protocol (n = 185). RESULTS No statistically significant differences were detected for any of the outcome measures. Selleckchem Cyclosporine SIGNIFICANCE Although hip muscle strength and gait quality are related, strength training of the hip-surrounding musculature without specific gait training elements cannot improve spatio-temporal gait characteristics or hip and knee joint angles in subjects with mild to moderate hip osteoarthritis. link2 If gait should explicitly be improved through exercise, interventions must incorporate a relevant portion of gait-related tasks. BACKGROUND Induced pluripotent stem cells (iPSCs) are regarded as the best potential cell source for cell-based regenerative medicine. To develop a safe and efficient iPSC-based cell therapy, it is very important to avoid possible teratoma formation, which can arise from undifferentiated iPSCs (USCs) remaining among differentiated cell products. Dried bark of Magnolia officinalis (Magnolia cortex, MC) has long been used in traditional medicine to treat gastrointestinal ailments and allergic diseases, and has shown have various pharmacological activities, including anti-bacterial, anti-inflammatory, and anti-cancer effects. However, its effects on iPSCs have not yet been examined. PURPOSE In this study, we investigated the selective cytotoxic effects of ethanol extract of MC (EEMC) on undifferentiated iPSCs and elucidated the underlying apoptotic mechanisms in detail. We also investigated the inhibitory effects of EEMC on teratoma formation via in ovo experiments. RESULTS We found that EEMC greatly reduced cell growth and induced apoptotic cell death in USCs, but not in differentiated or normal cells. EEMC caused G2/M cell cycle arrest, mitochondrial damage, and caspase activation of USCs, accompanied by p53 accumulation. In p53KO human iPSCs, EEMC had no cytotoxicity, reinforcing that EEMC-mediated apoptosis of USCs is p53-dependent. EEMC did not cause DNA damage in iPSC-derived differentiated cells. In ovo teratoma formation assay revealed that EEMC treatment before injection efficiently eliminated USCs and prevented teratoma formation. CONCLUSIONS These results collectively indicate that EEMC has potent anti-teratoma activity, and therefore can be used for the development of safe iPSC-based therapy. With the rapid development and wide application of computer, camera device, network and hardware technology, 3D object (or model) retrieval has attracted widespread attention and it has become a hot research topic in the computer vision domain. Deep learning features already available in 3D object retrieval have been proven to be better than the retrieval performance of hand-crafted features. However, most existing networks do not take into account the impact of multi-view image selection on network training, and the use of contrastive loss alone only forcing the same-class samples to be as close as possible. In this work, a novel solution named Multi-view Discrimination and Pairwise CNN (MDPCNN) for 3D object retrieval is proposed to tackle these issues. It can simultaneously input multiple batches and multiple views by adding the Slice layer and the Concat layer. Furthermore, a highly discriminative network is obtained by training samples that are not easy to be classified by clustering. Lastly, we deploy the contrastive-center loss and contrastive loss as the optimization objective that has better intra-class compactness and inter-class separability. Large-scale experiments show that the proposed MDPCNN can achieve a significant performance over the state-of-the-art algorithms in 3D object retrieval. Rectified activation units make an important contribution to the success of deep neural networks in many computer vision tasks. In this paper, we propose a Parametric Deformable Exponential Linear Unit (PDELU) and theoretically verify its effectiveness for improving the convergence speed of learning procedure. By means of flexible map shape, the proposed PDELU could push the mean value of activation responses closer to zero, which ensures the steepest descent in training a deep neural network. We verify the effectiveness of the proposed method in the image classification task. Extensive experiments on three classical databases (i.e., CIFAR-10, CIFAR-100, and ImageNet-2015) indicate that the proposed method leads to higher convergence speed and better accuracy when it is embedded into different CNN architectures (i.e., NIN, ResNet, WRN, and DenseNet). Meanwhile, the proposed PDELU outperforms many existing shape-specific activation functions (i.e., Maxout, ReLU, LeakyReLU, ELU, SELU, SoftPlus, Swish) and the shape-adaptive activation functions (i.e., APL, PReLU, MPELU, FReLU). Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for the treatment of several nervous system disorders. In particular, transcranial direct current stimulation (tDCS) is widely used as an affordable clinical application that is applied through electrodes attached to the scalp. However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability. Personalized tDCS is an emerging clinical procedure that is used to tolerate electrode montage for accurate targeting. This procedure is guided by computational head models generated from anatomical images such as MRI. Distribution of the EF in segmented head models can be calculated through simulation studies. Therefore, fast, accurate, and feasible segmentation of different brain structures would lead to a better adjustment for customized tDCS studies. In this study, a single-encoder multi-decoders convolutional neural network is proposed for deep brain segmentation. The proposed architecture is trained to segment seven deep brain structures using T1-weighted MRI. Network generated models are compared with a reference model constructed using a semi-automatic method, and it presents a high matching especially in Thalamus (Dice Coefficient (DC) = 94.70%), Caudate (DC = 91.98%) and Putamen (DC = 90.31%) structures. Electric field distribution during tDCS in generated and reference models matched well each other, suggesting its potential usefulness in clinical practice. BACKGROUND Four Appalachian states including Pennsylvania (PA) have the highest drug overdose rates in the country, calling for better understanding of the social and economic drivers of opioid use in the region. Using key informant interviews, we explored the social and community drivers of opioid use in a non-urban Appalachian Pennsylvania community. METHODS In 2017, we conducted qualitative interviews with 20 key stakeholders from a case community selected using the results from quantitative spatial models of hospitalizations for opioid use disorders. In small town located 10 miles outside Pittsburgh, PA, we asked participants to share their perceptions of contextual factors that influence opioid use among residents. We then used qualitative thematic analysis to organize and generate the results. RESULTS Participants identified several contextual factors that influence opioid use among residents. Three cross-cutting thematic topics emerged 1) acceptance and denial of use through familial and peer influences, community environments, and social norms; 2) impacts of economic shifts and community leadership on availability of programs and opportunities; and 3) the role of coping within economic disadvantage and social depression. CONCLUSION Uncovering multi-level, contextual drivers of opioid use can benefit the development of future public health interventions. These results suggest that social and community-level measures of structural deprivation, acceptance and/or denial of the opioid epidemic, community engagement and development, social support, and social depression are important for future research and programmatic efforts in the Appalachian region. The current study explores the formation of active eco-friendly materials capable of preventing microbial contamination using in situ ultrasonic grafting of vanillin, curcumin and a curcumin-vanillin mixture on the surfaces of carboxymethylcellulose (CMC) and chitosan films. Spectroscopic, microscopic, physical and mechanical studies revealed that the films grafted with curcumin-vanillin mixture demonstrate improved mechanical properties and higher degree of order. The bioactivity of the prepared films was tested on food model, fresh-cut melons and films with a deposited curcumin-vanillin mixture showed superior antibacterial properties. For instance, this mixture-grafted on CMC films demonstrated a total inhibition of yeast/mold proliferation during 12 days. link3 The HR-SEM studies of the mixture-grafted films revealed the presence of crystalline structures. Cooperative crystallization effect between the curcumin (the crystal maker) and the volatile vanillin is suggested to be responsible for the observed effects. According to our knowledge, this is the first usage of co-crystallization method in surface deposition. The results point out to a general strategy of combining a crystal maker agent with a volatile active agent during in situ sonochemical deposition to form bioactive materials that can be further used for food packaging, agriculture, pharmacology and more. In this work, we proposed a facile strategy to prepare molecularly imprinted polymers (MIPs) modified Mn-doped ZnS quantum dots (QDs) as optosensing materials via sol-gel polymerization for specific recognition of celastrol (Cel) in traditional Chinese medicine (TCM). Firstly, L-Cysteine (L-Cys) modified Mn-doped ZnS QDs (L-Cys@Mn-ZnS) was used as imprinting substrate. The amino and carboxyl groups on the surface of Mn-ZnS QDs can provide more binding sites for imprinting polymerization. Then, the fluorescent MIPs was synthesized in the presence of L-Cys@Mn-ZnS QDs, template celastrol, 3-aminopropyl triethoxysilane (APTES) and ammonium hydroxide in the ethanol-water (9/1, v/v) solution. The morphology and structure of the products were characterized by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffractometer (XRD) and X-ray photoelectron spectroscopy (XPS). The resulting MIPs functionalized Mn-doped ZnS QDs (denoted as MIPs@L-Cys@Mn-ZnS QDs) had higher imprinting factor of 14.
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