Anti-Müllerian Hormone Accelerates Pathological Process of Insulin Resistance in Polycystic Ovary Syndrome Patients.

Insulin resistance (IR) is one of the most common features of polycystic ovary syndrome (PCOS), which is related to obesity. Whether increased anti-Müllerian hormone (AMH) levels in PCOS are involved in the pathogenesis of insulin resistance remains unclear. We investigated serum levels of leptin and AMH along with basic clinical and metabolic parameters in 114 PCOS patients and 181 non-PCOS women. PCOS patients presented higher fasting blood glucose, insulin concentrations and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) in addition to body mass index (BMI), lipids profiles and hormone levels. HOMA-IR showed a positive correlation with BMI, AMH, leptin, and low-density lipoprotein-cholesterol (LDL-c) levels. Interestingly, AMH is strongly positively correlated with HOMA-IR and insulin concentrations for 1st and 2nd hours of glucose treatment after fasting. Among PCOS women with BMI≥25 kg/m2, high AMH level group showed an increased HOMA-IR when compared to normal AMH level. However, among PCOS women with normal BMI, women with high AMH presented an elevated fasting insulin levels but not HOMA-IR when compared to normal AMH group. In vitro treatment of isolated islet cells with high concentration of leptin (200 ng/ml) or high leptin plus high concentration of AMH (1 ng/ml) significantly enhanced insulin secretion. Importantly, co-treatment of AMH plus leptin upregulates the expression of pro-apoptotic proteins, such as Bax, caspase-3, and caspase-8 after incubating with a high level of glucose. These results suggest that AMH may involve in the pathological process of pancreatic ß-cells in obese PCOS women.

[A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection].

With high-resolution spatial information and continuous spectrum information, hyperspectral remote sensing image -has a unique advantage in the field of target detection. Traditional hyperspectral remote sensing image target detection methods emphasis on using spectral information to determine deterministic algorithm and statistical algorithms. Deterministic algorithms find the target by calculating the distance between the target spectrum and detected spectrum however, they are unable to detect sub-pixel target and are easily affected by noise. Statistical methods which calculate background statistical characteristics to detect abnormal point as target. It can detect subpixel target targets and small targets better thanbig size target,. With the spatial resolution increasing, subpixel target detection target has gradually grown to a single pixel and multi-pixel target. At this point, hyperspectral image usually has large homogeneous regions where the neighboring pixels wihin the regions consist of the same type of materials and have a similar spectral characteristics, therefore, the spatial information should be needed to incorporate into the algorithm for targe detection. This paper proposes an algorithm for hyperspectral target detection combined spectrum characteristics and spatial characteristics. The algorithm is based on traditional target detection operator and combined neighborhood clustering statistics. Firstly, the algorithm uses target detection operator to divided hyperspectral image into a potential target region and background region. Then, it calculates the centroid of the potential target area. Finally, as the centroid for neighborhood clustering center to clust data in order to exclud background from potential target area, through iterative calculation to obtain the final results of the target detection. The traditional statistics algorithms defines the total image as background area in order to extract background statistics features, and the algorithm propsed devided the total image into background part and potential target part, which cut off the target interference for background statistics feature extraction. Compared with CEM operators and ACE operators, the algorithm proposed outperforms than traditional operators in big target detection .

[A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation].

Traditional hyperspectral image classification algorithms focus on spectral information application, however, with the increase of spatial resolution of hyperspectral remote sensing images, hyperspectral imaging presents clustering properties on spatial domain for the same category. It is critical for hyperspectral image classification algorithms to use spatial information in order to improve the classification accuracy. However, the marginal differences of different categories display more obviously. If it is introduced directly into the spatial-spectral sparse representation for image classification without the selection of neighborhood pixels, the classification error and the computation time will increase. This paper presents a spatial-spectral joint sparse representation classification algorithm based on neighborhood segmentation. The algorithm calculates the similarity with spectral angel in order to choose proper neighborhood pixel into spatial-spectral joint sparse representation model. With simultaneous subspace pursuit and simultaneous orthogonal matching pursuit to solve the model, the classification is determined by computing the minimum reconstruction error between testing samples and training pixels. Two typical hyperspectral images from AVIRIS and ROSIS are chosen for simulation experiment and results display that the classification accuracy of two images both improves as neighborhood segmentation threshold increasing. It concludes that neighborhood segmentation is necessary for joint sparse representation classification.

Proteomics of Uterosacral Ligament Connective Tissue from Women with and without Pelvic Organ Prolapse.

PURPOSE: Damage to the uterosacral ligaments is an important contributor to uterine and vaginal prolapse. The aim of this study is to identify differentially expressed proteins (DEPs) in the uterosacral ligaments of women with and without pelvic organ prolapse (POP) and analyze their relationships to cellular mechanisms involved in the pathogenesis of POP. EXPERIMENTAL DESIGN: Uterosacral ligament connective tissue from four patients with POP and four control women undergo iTRAQ analysis followed by ingenuity pathway analysis (IPA) of DEPs. DEPs are validated using Western blot analysis. RESULTS: A total of 1789 unique protein sequences are identified in the uterosacral ligament connective tissues. The expression levels of 88 proteins are significantly different between prolapse and control groups (≥1.2-fold, p < 0.05). IPA demonstrates the association of 14 DEPs with "Connective Tissue Function." Among them, fibromodulin, collagen alpha-1 (XIV) chain, calponin-1, tenascin, and galectin-1 appear most likely to play a role in the etiology of POP. CONCLUSIONS AND CLINICAL RELEVANCE: At least six proteins not previously associated with the pathogenesis of POP with biologic functions that suggest a plausible relationship to the disorder are identified. These results may be helpful for furthering the understanding of the pathophysiological mechanisms of POP.

Human leukocyte antigen-G (HLA-G) expression in cervical cancer lesions is associated with disease progression.

The immunotolerant human leukocyte antigen (HLA)-G has direct inhibitory effects on natural killer cells, dendritic cells, T cells and can indirectly induce tolerant regulatory cells. The significance of the aberrant HLA-G expression in malignant contexts has been intensively investigated. In the current study, HLA-G expression in 22 normal cervical tissues, 14 cervical intraepithelial neoplasia (CIN) patients and 129 patients with squamous cell cervical cancer were examined using immunohistochemistry. The association of HLA-G expression with disease progression was calculated with the Pearson Chi-square test. It was found that HLA-G expression was absent in normal cervical tissues, and that HLA-G expression was increased from patients with CIN III (35.7%, 4/14) to patients with cervical cancer (62.8%, 81/129). Among the cervical cancer patients, HLA-G expression in FIGO stage I, II, and stage III+IV was 53.6% (45/84), 76.3% (29/38), and 100.0% (7/7), respectively. Taken together, our findings indicated that HLA-G expression was associated with the disease progression in patients with cervical cancer.