Insulin resistance: vascular function and exercise

AbstractInsulin resistance associated with metabolic syndrome and Type 2 diabetes mellitus is an epidemic metabolic disorder, which increases the risk of cardiovascular complications. Impaired vascular endothelial function is an early marker for atherosclerosis, which causes cardiovascular complications. Both experimental and clinical studies indicate that endothelial dysfunction in vasculatures occurs with insulin resistance. The associated physiological mechanisms are not fully appreciated yet, however, it seems that augmented oxidative stress, a physiological imbalance between oxidants and antioxidants, in vascular cells is a possible mechanism involved in various vascular beds with insulin resistance and hyperglycemia. Regardless of the inclusion of resistance exercise, aerobic exercise seems to be beneficial for vascular endothelial function in both large conduit and small resistance vessels in both clinical and experimental studies with insulin resistance. In clinical cases, aerobic exercise over 8 weeks with higher intensity seems more beneficial than the cases with shorter duration and lower intensity. However, more studies are needed in the future to elucidate the physiological mechanisms by which vascular endothelial function is impaired in insulin resistance and improved with aerobic exercise

The relationship between arterial stiffness and maximal oxygen consumption in healthy young adults

Objective: Arterial stiffness is associated with an increased risk of cardiovascular diseases in various populations. There was little research on the relationship between arterial stiffness and maximal aerobic capacity (VO2max) in healthy young adults. The aim of this study was to investigate the relationship between VO2max and arterial stiffness in young adults. Methods: The subjects were 13 men and 10 women with mean age of 22.9 ± 0.7, 23.6 ± 0.4 years, respectively. Height, weight, body mass index, body fat (%), waist to hip ratio, total/high density lipoprotein (HDL)/low density lipoprotein (LDL) cholesterol, triglycerides, fasting glucose, blood pressure, heart rate, glycated hemoglobin and blood lactate were measured. In addition, peripheral arterial stiffness was assessed by measuring brachial-ankle pulse wave velocity (baPWV) and VO2max was determined using graded exercise test. Results: VO2max had no significant correlation with baPWV (r = 0.2, p = 0.2). Total cholesterol correlated significantly to variables such as HDL (r = 0.6, p = 0.0015) and LDL cholesterol (r = −0.6, p = 0.0018). VO2max had a significant association with triglyceride (r = −0.5, p = 0.0033). Conclusions: This study suggests that there is no relationship between arterial stiffness and aerobic capacity in healthy young adults. Keywords: Arterial stiffness, Aerobic capacity, baPWV, VO2ma

Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines.

We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user's role while achieving good segmentation accuracy

SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose SDC-UDA, a simple yet effective volumetric UDA framework for slice-direction continuous cross-modality medical image segmentation which combines intra- and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies.Comment: 10 pages, 7 figures, CVPR 202

Fine-Grain Segmentation of the Intervertebral Discs from MR Spine Images Using Deep Convolutional Neural Networks: BSU-Net

We propose a new deep learning network capable of successfully segmenting intervertebral discs and their complex boundaries from magnetic resonance (MR) spine images. The existing U-network (U-net) is known to perform well in various segmentation tasks in medical images; however, its performance with respect to details of segmentation such as boundaries is limited by the structural limitations of a max-pooling layer that plays a key role in feature extraction process in the U-net. We designed a modified convolutional and pooling layer scheme and applied a cascaded learning method to overcome these structural limitations of the max-pooling layer of a conventional U-net. The proposed network achieved 3% higher Dice similarity coefficient (DSC) than conventional U-net for intervertebral disc segmentation (89.44% vs. 86.44%, respectively; p < 0.001). For intervertebral disc boundary segmentation, the proposed network achieved 10.46% higher DSC than conventional U-net (54.62% vs. 44.16%, respectively; p < 0.001)

Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines

We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user’s role while achieving good segmentation accuracy

METABOLOMIC AND CLINICAL BIOMARKERS ANALYSIS OF THE IMPACT OF SANGHUANG-DANSHEN ON PLATELET HYPERACTIVITY IN HUMANS

The postprandial condition is recognized as a procoagulant state. Postprandial platelet hyperactivity plays a crucial role in the pathogenesis of metabolic syndrome. Botanical foods typically contain a multi-component composition derived from herbal practices. Therefore, it is difficult to determine which components regulate platelet hyperactivity. Metabolomic analysis is a valid and powerful tool with which to further define the mechanisms. We therefore aimed to assess the effect of Sanghuang-Danshen (SD) in the metabolome and clinical biomarkers on platelet hyperactivity after an acute challenge with high-lipid/glucose formula in healthy subjects. In a crossover randomized and placebo-controlled intervention study, fifty-six subjects received three doses of SD or placebo. Platelet aggregation and mRNA expression of cyclooxygenase-1 and vascular cell adhesion molecule-1 were all decreased in the SD group. SD administration appears to reduce the platelet hyperactivity associated with a postprandial condition. These changes are accompanied by alterations of the metabolomics. A total of 13 metabolites were found to be significantly different between placebo and the high-dose SD group, which were strongly involved in linoleic acid metabolism, alanine, aspartate and glutamate metabolism, arachidonic acid metabolism, and glycine, serine and threonine metabolism. By integrating components in SD, markers, and phenotype, we find that SD may modulate the platelet hyperactivity occurring in the postprandial state