BackgroundGestational diabetes mellitus (GDM), characterized by the onset of glucose intolerance during pregnancy, results in a series of complications for maternal and fetal health. Oral glucose tolerance test (OGTT) for screening glucose metabolism is performed in mid-to-late pregnancy, which remains less time to optimize glycemic control. Glypican-4, an insulin-sensitive adipose hormone, exhibits correlations with metabolic indicators. This study aims to investigate the association between glypican-4 and the risk of developing GDM, as well as the effects on insulin therapy and postpartum glucose metabolism.MethodsBased on pregnancy 75-g OGTT results, 718 subjects were grouped into normal glucose tolerance (NGT, n = 345) and GDM (n = 373) groups. 373 GDM patients were divided into the diet (n = 237) and insulin (n = 136) groups according to the treatment of hyperglycemia in pregnancy. Based on postpartum 75-g OGTT results, 158 of the 373 GDM patients were further divided into the NGT after delivery (NGTd, n = 138) and abnormal glucose tolerance (AGT, n = 20) groups.ResultsGlypican-4 level was significantly higher in GDM than NGT subjects during pregnancy (P< 0.001). Glypican-4 was an independent predictor of GDM with the cut-offs were 0.40 ng/mL (5-12 weeks of gestation) and 0.79 ng/mL (13-23 weeks of gestation). Furthermore, glypican-4 level in the insulin group was higher than the diet group, which was a potential predictor of insulin therapy.ConclusionsGlypican-4 during pregnancy is associated with GDM risk, with higher levels indicating increased risk. Glypican-4 was also related to insulin therapy in GDM.

3D model retrieval based on representative views was proposed. On the view representation of the 3D model, in order to fully represent the model and reduce redundant information, we firstly adopt Light Field Descriptor (LFD) to generate 2D views, and then use K-MEANS to get representative views from the 2D views. Next, a Convolution Neural Network (CNN) is adopted to extract the view feature and classify. At the same time, a similarity metrics supporting multiple query method is proposed to realize model retrieval with sketches, pictures or 3D models as input. Results on ModelNet40 showed that the proposed method could achieve an accuracy of 100% for part of models with distinct features

AIMS:To determine the relationship between thyroid markers during pregnancy and gestational diabetes mellitus (GDM) or post-partum glucose metabolism.MATERIALS AND METHODS:Based on pregnancy 75-g oral glucose tolerance test (OGTT) results, 1467 subjects were grouped into normal glucose tolerance (NGTp; n = 768) and GDM (n = 699) groups. Furthermore, based on post-partum 75-g OGTT results, 286 GDM subjects, screened for glucose metabolism after delivery, were grouped into NGTd (n = 241) and abnormal glucose tolerance (AGT; n = 45) groups.RESULTS:Maternal age, family history of diabetes, acanthosis nigricans, previous adverse pregnancy outcomes and caesarean section incidence, and thyroid positive antibody rates were higher in the GDM group than in the NGTp group. In the first trimester, free triiodothyronine (FT3), thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TgAb) levels were higher in the GDM group than in the NGTp group. In the second trimester, free thyroxine (FT4) levels were lower and TPOAb and TgAb levels were higher in the GDM group than in the NGTp group. After adjusting for confounding factors, FT3, TPOAb and TgAb (first trimester), and FT4, TPOAb and TgAb (second trimester) were risk factors for GDM. TPOAb and TgAb levels were higher in the AGT group than in the NGTd group and were potential predictors of abnormal post-partum glucose tolerance.CONCLUSIONS:GDM risk significantly increased with increased FT3 (first trimester), TPOAb and TgAb (first and second trimesters) or with decreased FT4 (second trimester). Presence of thyroid antibodies predicted post-partum glucose abnormalities in subjects with GDM.

Automatic screening systems play an increasingly important role in the diagnosis of pathologists. Image measurement and classification are the key techniques of automatic screening systems, which directly determine the performance. The distortion in grey and texture after overlapping nuclei segmentation seriously degrades the DNA content measurement and nuclei classification. In order to solve this problem, this paper presents a new method to reconstruct the pixels in overlapping regions based on the GMM-UBM (Gaussian mixture model–universal background model). In this method, a large amount of data are first used to train a GMM (named UBM). Then, the GMM of each nucleus is derived by maximizing a posteriori adaptation with the UBM and the normal grey value of this nucleus. The grey values are randomly generated by the GMM and filled to the overlapping region, with the offset to fine-tuning the Gaussian components. Finally, the image inpainting algorithm is used to repair the connected region. Experimental results show that this method can effectively recover the nucleus features, such as texture, grey and optical density, and improve the accuracy of nucleus measurement and classification.

View-based 3D shape classification is widely used in machine vision, information retrieval and other fields. However, there are two problems in current methods. First, current 3D shape classifiers fail to make good use of pose information of 3D shapes. Secondly, many views are required to obtain good classification accuracy, which leads to low efficiency. In order to solve these problems, we propose a novel 3D shape classification method based on Convolutional Neural Network (CNN). In the training stage, this method first learns a CNN to extract features, and then uses features of views from different viewpoint groups to train six 3D shape classifiers which fully mine the pose information of 3D shapes. Meanwhile, an additional class is adopted to improve the discrimination of 3D shape classifiers. In the recognition stage, the weighted fusion of image clarity evaluation functions is used to select the most representative view for the 3D shape recognition. Experiments on the ModelNet10 and ModelNet40 show that the classification accuracy of the proposed method can reach up to 91.18% and 89.01% when only using a single view and the efficiency is improved substantially.

Recently, 3D model retrieval based on views has become a research hotspot. In this method, 3D models are represented as a collection of 2D projective views, which allows deep learning techniques to be used for 3D model classification and retrieval. However, current methods need improvements in both accuracy and efficiency. To solve these problems, we propose a new 3D model retrieval method, which includes index building and model retrieval. In the index building stage, 3D models in library are projected to generate a large number of views, and then representative views are selected and input into a well-learned convolutional neural network (CNN) to extract features. Next, the features are organized according to their labels to build indexes. In this stage, the views used for representing 3D models are reduced substantially on the premise of keeping enough information of 3D models. This method reduces the number of similarity matching by 87.8%. In retrieval, the 2D views of the input model are classified into a category with the CNN and voting algorithm, and then only the features of one category rather than all categories are chosen to perform similarity matching. In this way, the searching space for retrieval is reduced. In addition, the number of used views for retrieval is gradually increased. Once there is enough evidence to determine a 3D model, the retrieval process will be terminated ahead of time. The variable view matching method further reduces the number of similarity matching by 21.4%. Experiments on the rigid 3D model datasets ModelNet10 and ModelNet40 and the nonrigid 3D model dataset McGill10 show that the proposed method has achieved retrieval accuracy rates of 94%, 92%, and 100%, respectively.

BACKGROUND:To investigate the potential predictors of insulin treatment during pregnancy and abnormal postpartum glucose metabolism in gestational diabetes mellitus (GDM). METHODS:A total of 534 patients with GDM, who were diagnosed based on 75 g oral glucose tolerance test (OGTT) during pregnancy, were divided into the diet group (n=354) and insulin group (n=180) according to the treatment of hyperglycemia in pregnancy. Based on 75 g OGTT after delivery, 178 of the 534 patients were divided into the normal glucose tolerance (NGT; n=104) and the abnormal glucose tolerance (AGT; n=74) groups. Characteristics and metabolic indicators were compared. Logistic regression analysis was developed to assess the potential predictors of insulin treatment and abnormal postpartum glucose metabolism. Receiver operating characteristic curve was performed to determine the cut-off values. RESULTS:Fasting plasma glucose (FPG), 1 h plasma glucose, and hemoglobin A1c (HbA1c) at GDM diagnosis were higher in the insulin group compared with the diet group (P <0.05). FPG, 1 h plasma glucose, HbA1c, maternal age, pre-gestational weight and maximum weight, pre-gestational body mass index, maternal birth weight, family history of diabetes in first-degree relatives, acanthosis nigricans, and prenatal weight were risk factors for insulin treatment (P <0.05), and the cut-offs of FPG, 1 h plasma glucose and HbA1c were 5.7 mmol/L, 11.4 mmol/L and 5.3%. Simultaneously, FPG at GDM diagnosis, insulin treatment during pregnancy, maternal age, family history of diabetes in first-degree relatives, acanthosis nigricans, and prenatal weight were risk factors of abnormal postpartum glucose metabolism (P <0.05), and the cut-off of FPG was 5.7 mmol/L. CONCLUSION:Patients with FPG >5.7 mmol/L, 1 h plasma glucose >11.4 mmol/L, or HbA1c >5.3% at GDM diagnosis required insulin treatment, and patients with FPG >5.7 mmol/L had a greater risk of abnormal postpartum glucose metabolism. FPG at GDM diagnosis was the most important predictor.

Aim: To investigate the relationship of the aspartate aminotransferase to alanine aminotransferase ratio (AST/ALT) and metabolic syndrome (MetS) in adolescents in northeast China. Methods: A stratified cluster random sample of 935 students 11-16 years of age in a city in the northeast of China were enrolled in 2010-2011. Participants were given a physical examination and a laboratory evaluation, and 93 participants were followed-up after 5 years. Results: AST/ALT was negatively correlated with waist circumference (WC), waist-to-hip ratio, body mass index (BMI), diastolic blood pressure, triglycerides, low-density lipoprotein, uric acid, fasting insulin, and insulin resistance. It was positively correlated with high-density lipoprotein. Multivariate logistic regression showed that the risk of MetS was 6.02 times greater in adolescents with the lowest, compared with the highest, AST/ALT. Central obesity was the MetS component most closely associated with low AST/ALT [odds ratio (OR) =5.13, 95% CI: 2.83, 9.28]. Five years later, baseline AST/ALT was negatively correlated with WC (r=-0.21, P=0.046), BMI (r=-0.29, P=0.005) and fasting plasma glucose (r=-0.25, P= 0 .017). Conclusion: In adolescents, AST/ALT was significantly associated with MetS and its components and predicted overweight/obesity in adulthood.

PURPOSE:Free insulin-like growth factor-1 (IGF-1) ratio (the ratio of IGF-1/insulin-like growth factor binding protein-3 [IGFBP-3]) was shown to be negatively correlated with metabolic syndrome (MetS) in adults, but it was unknown in Chinese adolescents. PATIENTS AND METHODS:The cross-sectional study enrolled 701 healthy school students (aged 12-16 years, 46.1% females) and 93 of them (18-22 years old, 46.2% females) were followed after 5 years. RESULTS:In the cross-sectional study, the IGF-1/IGFBP-3 ratios were found correlated with low-density lipoprotein cholesterol (LDL-C; r= -0.071, P<0.05) and diastolic blood pressure (r= -0.077, P=0.034). A lower IGF-1/IGFBP-3 ratio was an independent risk factor for MetS (OR =2.348, 95% CI: 1.040-5.303), hypertension (OR=1.729, 95% CI: 1.040-5.303), and increased LDL-C (OR=1.841, 95% CI: 1.230-2.755). In the follow-up study, all the participants were >18 years old. We found a lower baseline ratio of IGF-1/IGFBP-3 in adolescence was an independent risk factor for MetS in adulthood (OR=10.724, 95% CI: 1.032-11.403) and also indicated a higher body mass index (β=-1.361, 95% CI: -2.513 to -0.208) after 5 years. CONCLUSION:The lower IGF-1/IGFBP-3 ratio was an independent risk factor for MetS, hypertension, and high LDL-C in adolescents of northeast China and was also a predictive marker for MetS and increased body mass index in the adulthood.

With the knowledge that the locally maximum of the dim target gray value usually shows a sudden change of the spectrum in frequency domain,a two-stage infrared dim target enhancement approach based on spectral analysis and image fusion is proposed.The algorithm extractes the spectral residual according to the spectral difference between the dim target and the image background,constructed the saliency map of the potential targets,and applies different fusion rules to potential targets and background respectively with the information redundancy of continuous frames to obtain the fused image.Experimental results showe that the method can enhance the size and the total energy of the infrared dim target effectively and efficiently.

Bag of words algorithm is an efficient object recognition algorithm based on semantic features extraction and expression. It learns the virtues of the text-based search algorithm to make images a range of visual words, extract the semantic characters and carry out the detection and recognition of interesting objects. Bag of words algorithm is extracted from gray images and discard s color information of images. We propose in this paper a method of image retrieval based on clustered domain colors and bag of words algorithm. The results of experiments show that this method can improve the precision of retrieval efficiently.

Based on the region partition and association of source images,two regional features of source images are defined according to the imaging characteristics of infrared and visible images and the requirements of fusion tasks.A regional similarity assessment index with the regional features and the information entropy and mutual information of fused images and source images is constructed.A novel non-reference quality evaluation metric for infrared and visible image fusion is proposed.Experimental results show that this metric fits the results of human visual inspection better than the recent state of the art image fusion evaluation metrics.

Compared with traditional fuzzy image enhancement algorithms,which can not enhance images with changeful grey levels well and is difficult to decide the control parameters,a new fuzzy image enhancement algorithm is proposed to overcome the drawbacks.The crossover points for each pixel are computed adaptively based on the local feature of the neighborhood of each pixel.A new fuzzy membership function is proposed of which membership function is S-shape,and can combined with the crossover points perfectly by adjusting the parameters.Road surface images with changeful grey levels can obtain satisfactory enhancement effect by the new algorithm.And the new algorithm is universal because all the parameters are computed adaptively.

The wavelet-based contourlet transform (WBCT) is a new directional transform. This transform uses the wavelet transform and the directional filter bank (DFB) to obtain a multiscale and multidirection decomposition of image. The wavelet transform and the DFB are non-redundant and perfect reconstruction. So the WBCT can be regarded as a non-redundant version of the contourlet transform. A new image fusion scheme based on the WBCT was presented. Firstly, the WBCT is used to perform a multiscale and multidirection decomposition of each image. Then the WBCT coefficients of fused image are constructed using multiple operators according to different fusion rules. The experimental results show that this new fusion scheme is effective and the fused images are better than that of using the Laplacian pyramid transform, the wavelet transform and the contourlet transform.