The use of cationic lipids, stearylamine, or 1,2-di-(9Z-octadecenoyl)-3-trimethylammonium-propane (DOTAP) in liposomes promoted iloprost encapsulation to
at least 50%. The addition of cholesterol modestly reduced iloprost encapsulation. The liposomal nanoparticle formulations were tested for toxicity and pharmacologic efficacy in vivo and ex vivo, respectively. Src inhibitor The liposomes did not affect the viability of human pulmonary artery smooth muscle cells. Compared with an equivalent concentration of free iloprost, four out of the six polymer-coated liposomal formulations exhibited significantly enhanced vasodilation of mouse pulmonary arteries. Iloprost that was encapsulated in liposomes containing the polymer polyethylene glycol exhibited concentration-dependent relaxation of arteries. Strikingly, selleck chemicals half the concentration of iloprost in liposomes elicited similar pharmacologic efficacy as nonencapsulated iloprost. Cationic liposomes can encapsulate iloprost with high efficacy and can serve as potential iloprost carriers to improve its therapeutic efficacy.”
“Unsupervised image segmentation is a fundamental but challenging problem in computer vision. In this paper, we propose a novel
unsupervised segmentation algorithm, which could find diverse applications in pattern recognition, particularly in computer vision. The algorithm, named Two-stage Fuzzy c-means Hybrid Approach (TFHA), adaptively clusters image pixels according to their multichannel Gabor responses taken at multiple scales and orientations. In the first stage, the fuzzy c-means (FCM) algorithm is applied for intelligent estimation of centroid number and initialization of cluster centroids,
which endows the novel segmentation algorithm with adaptivity. To improve the efficiency of the algorithm, we utilize the Gray Level Co-occurrence Matrix (GLCM) feature extracted at the hyperpixel level instead of the pixel level to estimate centroid number and hyperpixel-cluster memberships, which are used as initialization AZD6244 cell line parameters of the following main clustering stage to reduce the computational cost while keeping the segmentation performance in terms of accuracy close to original one. Then, in the second stage, the FCM algorithm is utilized again at the pixel level to improve the compactness of the clusters forming final homogeneous regions. To examine the performance of the proposed algorithm, extensive experiments were conducted and experimental results show that the proposed algorithm has a very effective segmentation results and computational behavior, decreases the execution time and increases the quality of segmentation results, compared with the state-of-the-art segmentation methods recently proposed in the literature.