2′-OMe-phosphorodithioate-modified siRNAs show increased loading into the RISC complex and enhanced anti-tumour activity

Improving small interfering RNA (siRNA) efficacy in target cell populations remains a challenge to its clinical implementation. Here, we report a chemical modification, consisting of phosphorodithioate (PS2) and 2′-O-Methyl (2′-OMe) MePS2 on one nucleotide that significantly enhances potency and resistance to degradation for various siRNAs. We find enhanced potency stems from an unforeseen increase in siRNA loading to the RNA-induced silencing complex, likely due to the unique interaction mediated by 2′-OMe and PS2. We demonstrate the therapeutic utility of MePS2 siRNAs in chemoresistant ovarian cancer mouse models via targeting GRAM domain containing 1B (GRAMD1B), a protein involved in chemoresistance. GRAMD1B silencing is achieved in tumours following MePS2-modified siRNA treatment, leading to a synergistic anti-tumour effect in combination with paclitaxel. Given the previously limited success in enhancing siRNA potency with chemically modified siRNAs, our findings represent an important advance in siRNA design with the potential for application in numerous cancer types

Protective effects of N-acetylcysteine on acetic acid-induced colitis in a porcine model

BACKGROUND: Ulcerative colitis is a chronic inflammatory disease and involves multiple etiological factors. Acetic acid (AA)-induced colitis is a reproducible and simple model, sharing many characteristics with human colitis. N-acetylcysteine (NAC) has been widely used as an antioxidant in vivo and in vitro. NAC can affect several signaling pathways involving in apoptosis, angiogenesis, cell growth and arrest, redox-regulated gene expression, and inflammatory response. Therefore, NAC may not only protect against the direct injurious effects of oxidants, but also beneficially alter inflammatory events in colitis. This study was conducted to investigate whether NAC could alleviate the AA-induced colitis in a porcine model. METHODS: Weaned piglets were used to investigate the effects of NAC on AA-induced colitis. Severity of colitis was evaluated by colon histomorphology measurements, histopathology scores, tissue myeloperoxidase activity, as well as concentrations of malondialdehyde and pro-inflammatory mediators in the plasma and colon. The protective role of NAC was assessed by measurements of antioxidant status, growth modulator, cell apoptosis, and tight junction proteins. Abundances of caspase-3 and claudin-1 proteins in colonic mucosae were determined by the Western blot method. Epidermal growth factor receptor, amphiregulin, tumor necrosis factor-alpha (TNF-α), and toll-like receptor 4 (TLR4) mRNA levels in colonic mucosae were quantified using the real-time fluorescent quantitative PCR. RESULTS: Compared with the control group, AA treatment increased (P < 0.05) the histopathology scores, intraepithelial lymphocyte (IEL) numbers and density in the colon, myeloperoxidase activity, the concentrations of malondialdehyde and pro-inflammatory mediators in the plasma and colon, while reducing (P < 0.05) goblet cell numbers and the protein/DNA ratio in the colonic mucosa. These adverse effects of AA were partially ameliorated (P < 0.05) by dietary supplementation with NAC. In addition, NAC prevented the AA-induced increase in caspase-3 protein, while stimulating claudin-1 protein expression in the colonic mucosa. Moreover, NAC enhanced mRNA levels for epidermal growth factor and amphiregulin in the colonic mucosa. CONCLUSION: Dietary supplementation with NAC can alleviate AA-induced colitis in a porcine model through regulating anti-oxidative responses, cell apoptosis, and EGF gene expression

About the Relevance of Defect Features in As-Cut Multicrystalline Silicon Wafers on Solar Cell Performance

Recombination-active defects e.g. dislocations in multicrystalline silicon (mc-Si) wafers impact the quality of solar cells. These defects can be quantified during the incoming control of silicon wafers with Photoluminescence (PL) imaging and used to rate the solar cell quality. In this work, we analyze the relevance of defect patterns in PL images and grain-boundary (GB) data for current-voltage (IV) prediction by means of image processing algorithms. Based on a large set of empirical data of passivated emitter and rear cells (PERC), a sparse prediction model is trained for each IV-parameter. Our results include both, the prediction of different quality parameters and the relevance of the features extracted from PL images and GB images. We achieve mean absolute prediction errors as low as 2.72 mV and 0.18 mA/cm2 for open circuit voltage (Voc) and short circuit current density (Jsc) respectively, and 0.18% for efficiency as combined parameter. In this evaluation, the wafer data set is split into training group and test group. Therefore the results show the prediction of unknown material. This makes the prediction more challenging but represents a realistic use case for production. The comparative overview of the relevant feature set shows a difference between the prediction of short-circuit current and open-circuit voltage prediction

Learning Quality Rating of As-Cut mc-Si Wafers via Convolutional Regression Networks

This paper investigates deep convolutional neural networks (CNNs) for the assessment of defects in multicrystalline silicon (mc-Si) and high-performance mc-Si wafers for solar cell production based on photoluminescence (PL) images. We identify and train a CNN regression model to forecast the I - V parameters of passivated emitter and rear cells from given PL images of the as-cut wafers. The presented end-to-end model directly processes the PL image and does not rely on the human-designed image feature. Domain knowledge is replaced by a model based on a huge variety of empirical data. The comprehensive dataset allows for the evaluation of the generalizability of the model with test wafers from bricks and manufacturers not presented in the training set. We achieve mean absolute prediction errors as low as 0.11 %abs in efficiency for test wafers from 'unknown' bricks, which improves handcrafted feature-based methods by 35%rel at simultaneously lower computational costs for prediction. Samples with high prediction errors are investigated in detail showing an increased iron point defect concentration

Visualizing Material Quality and Similarity of mc-Si Wafers Learned by Convolutional Regression Networks

Convolutional neural networks can be trained to assess the material quality of multicrystalline silicon wafers. A successful rating model has been presented in a related work, which directly evaluates the photoluminescence (PL) image of the wafer to predict the current-voltage parameters after solar cell production. This paper presents the results of two visualization techniques to understand what has been learned in the network. First, we reveal what has been learned in the PL image by visualizing the spatial quality distribution of the wafers based on the activation maps of the network. The method is denoted as regression activation mapping. We compare regression activation maps with j0 images of solar cells to show the semantically meaningful representation of the trained features. Second, we show what has been learned in the data by mapping the learned network representation of all wafers into a low-dimensional subspace. Visualizations reveal the smoothness of our representation with respect to the PL input and measured quality. This technique can be used to detect material anomalies or process faults for samples with high prediction errors