Glycine and N-Acetylcysteine (GlyNAC) Combined with Body Weight Support Treadmill Training Improved Spinal Cord and Skeletal Muscle Structure and Function in Rats with Spinal Cord Injury.

Skeletal muscle atrophy is a frequent complication after spinal cord injury (SCI) and can influence the recovery of motor function and metabolism in affected patients. Delaying skeletal muscle atrophy can promote functional recovery in SCI rats. In the present study, we investigated whether a combination of body weight support treadmill training (BWSTT) and glycine and N-acetylcysteine (GlyNAC) could exert neuroprotective effects, promote motor function recovery, and delay skeletal muscle atrophy in rats with SCI, and we assessed the therapeutic effects of the double intervention from both a structural and functional viewpoint. We found that, after SCI, rats given GlyNAC alone showed an improvement in Basso-Beattie-Bresnahan (BBB) scores, gait symmetry, and results in the open field test, indicative of improved motor function, while GlyNAC combined with BWSTT was more effective than either treatment alone at ameliorating voluntary motor function in injured rats. Meanwhile, the results of the skeletal muscle myofiber cross-sectional area (CSA), hindlimb grip strength, and acetylcholinesterase (AChE) immunostaining analysis demonstrated that GlyNAC improved the structure and function of the skeletal muscle in rats with SCI and delayed the atrophication of skeletal muscle.

Automatic liver vessel segmentation using 3D region growing and hybrid active contour model.

This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms.

Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts.

BACKGROUND AND OBJECTIVE: Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method. METHODS: Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein. RESULTS: The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction. CONCLUSIONS: The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.