Cationic liposomes have been successfully used as an alternative approach to viral systems to deliver nucleic acids. However, high toxicity and inconsistent transfection efficiency have been associated with the currently available liposomes. Therefore, a novel cationic liposome was developed based on a synthetic cationic cardiolipin analogue (CCLA) to test the DNA transfection efficiency. This CCLA-based liposome was also used to determine the therapeutic efficacy of c-raf small interfering RNA (siRNA) in mice. In this report, we showed that the CCLA-based liposome was less toxic and effectively transfected reporter genes in vitro and in vivo. The transfection efficiency in mice was seven-fold higher than the commercially available DOTAP-based liposome. In addition, craf siRNA in the presence of CCLA-based liposome induced up to 62% of growth inhibition in cancer cells. Treatment of c-raf siRNA/CCLA complex in SCID mice bearing human breast xenograft tumors resulted in 73% of tumor growth suppression as compared to free c-raf siRNA group. In conclusion, a novel CCLA-based liposome showed less toxicity and broad usage both in vitro and in vivo with DNA and siRNA.
A series of alkyl acyl carnitine esters (alkyl 3-acyloxy-4-trimethylammonium butyrate chloride) were synthesized as potential biocompatible cationic lipids for use in gene transfer. The physicochemical properties of the lipids, liposomes prepared from them, and their complexes with DNA were characterized by differential scanning calorimetry (DSC), particle size, zeta potential, and surface monolayer measurements. The transition temperatures and behavior at an air-water interface for this series are similar to phosphatidylcholines with the same hydrocarbon chain length. The physical properties of the l derivatives were not significantly different from the dl derivatives. At 70 degrees C, the acyl chains were readily hydrolyzed at pH 7. The influence of the aliphatic chain length (n = 12-18) on transfection efficiency in vitro was determined using cationic liposomes prepared from these lipids or their mixtures with the helper lipids, dioleoylphosphatidylethanolamine (DOPE), dioleoylphosphatidylcholine, monooleoylglycerol, and cholesterol (Chol). The mixture of myristyl 3-myristoyloxy-4-trimethylammonium butyrate chloride (MMCE, 4d) with DOPE at a 1:1 molar ratio mediated the highest transfection efficiency in cell culture. The mixture of oleyl 3-oleoyloxy-4-trimethylammonium butyrate chloride (OOCE, 4f) with Chol at a 1:1 molar ratio gave the highest transfection efficiency after intravenous administration in mice. In vivo gene expression using 4f was comparable to values obtained with the best cationic lipids reported to date.
Bridge crack detection is essential to ensure bridge safety. The introduction of deep learning technology has made it possible to detect bridge cracks automatically and accurately. In this study, the Inception-Resnet-v2 algorithm was systematically improved and applied to the real-time detection of bridge cracks. We propose an end-to-end bridge crack detection model based on a convolutional neural network. This model combines the advantages of Inception convolution and residual networks, broadening the network width and alleviating the training problem of the deep network. The calculation speed is improved while still ensuring accuracy. Multi-scale feature fusion enables the network to extract contextual information of different scales, which improves the accuracy of crack recognition. The GKA (K-means clustering method based on a genetic algorithm) realizes the accurate segmentation of the target area, greatly enhances the clustering effect, and effectively improves the detection speed. In this model, large fracture datasets are used for training and testing without pre-training. The experimental results show that the performance of this method was improved in all aspects: accuracy, 99.24%; recall, 99.03%; F-measure, 98.79%; and FPS(Frames Per Second), 196.
INDEX TERMS bridge crack detection, Inception-Resnet-v2, multiscale feature fusion, GKAThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.
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