T-cadherin deficiency increases vascular vulnerability in T2DM through impaired NO bioactivity.

BACKGROUND: Endothelial dysfunction plays a critical role in the development of type 2 diabetes (T2DM). T-cadherin (T-cad) has gained recognition as a regulator of endothelial cell (EC) function. The present study examined whether T-cad deficiency increases vascular vulnerability in T2DM. METHODS: Vascular segments were isolated from WT or T-cad knockout mice. Endothelial function, total NO accumulation, and the expression of T-cad related proteins were determined. RESULTS: Ach and acidified NaNO2 induced similar vasorelaxation in WT groups. T-cad KO mice exhibited normal response to acidified NaNO2, but manifested markedly reduced response to Ach. NO accumulation was also decreased in T-cad KO group. T-cad expression was reduced in WT mice fed 8 weeks of high fat diet (HFD). Furthermore, exacerbated reduction of vasorelaxation was observed in T-cad KO mice fed 8 weeks of HFD. CONCLUSIONS: In the current study, we provide the first in vivo evidence that T-cadherin deficiency causes endothelial dysfunction in T2DM vascular segments, suggesting the involvement of T-cad deficiency in T2DM pathogenesis

Bifurcated backbone strategy for RGB-D salient object detection

Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to devise a novel cascaded refinement network. In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS). Second, we introduce a depth-enhanced module (DEM) to excavate informative depth cues from the channel and spatial views. Then, RGB and depth modalities are fused in a complementary way. Our architecture, named Bifurcated Backbone Strategy Network (BBS-Net), is simple, efficient, and backbone-independent. Extensive experiments show that BBS-Net significantly outperforms eighteen SOTA models on eight challenging datasets under five evaluation measures, demonstrating the superiority of our approach ($\sim 4 \%$ improvement in S-measure $vs.$ the top-ranked model: DMRA-iccv2019). In addition, we provide a comprehensive analysis on the generalization ability of different RGB-D datasets and provide a powerful training set for future research.Comment: A preliminary version of this work has been accepted in ECCV 202

AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation

We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately. Combining these two designs enables us to generate promising task-oriented flows and reduce the difficulties in modeling large motions and handling occluded areas during frame interpolation. These qualities promote our model to achieve state-of-the-art performance on various benchmarks with high efficiency. Moreover, our convolution-based model competes favorably compared to Transformer-based models in terms of accuracy and efficiency. Our code is available at https://github.com/MCG-NKU/AMT.Comment: Accepted to CVPR202

Dynamic Multi-view Hashing for Online Image Retrieval

Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods

Analysis of malnutrition factors for inpatients with chronic kidney disease

ObjectiveMalnutrition is a common complication of Chronic Kidney Disease (CKD), and it is the risk factor of CKD prognosis. This study aim to evaluate the nutritional status of inpatients with CKD by using the Subjective Global Assessment (SGA), and to analyze the related factors of malnutrition; and to provide effective reference for early detection of malnutrition status in patients with CKD and timely nutrition intervention.MethodsA total of 426 patients (238 male patients, 188 female patients) aged 62.62 ± 14.61 and 61.14 ± 14.82, respectively admitted to the Nephrology Department of Wannan Medical College from February 2020 to December 2020 were selected and included in to this study by convenience sampling. 426 patients with CKD were evaluated by SGA. Human body weight, hemoglobin (Hb), total protein (TP), albumin (ALB), pre-albumin (PA), qualitative analysis of urinary protein and other laboratory indexes were collected and measured. The correlation between malnutrition and age, education, gender, diet, CKD stage and other factors was analyzed by spearman correlation analysis.ResultsThe incidence of malnutrition was 85.7% among 426 patients with CKD. Gender, age, education level, CKD stage, diabetes mellitus, weight loss and reduced food intake were related to SGA nutritional assessment (P < 0.05). The expression levels of ALB, PA and Hb in the malnutrition group were significantly lower than those in the normal group (P < 0.05). The degree of malnutrition in CKD patients was significant negatively correlated with the expression levels of ALB (r = −0.188), PA (r = −0.262) and Hb (r = −0.176) (P < 0.05). The multivariate Logistic regression analysis model showed that female (OR = 2.155), ≥60 years old (OR = 7.671), weight loss (OR = 10.691), reduced food intake (OR = 28.953), moderate and severe serum ALB expression (OR = 3.391 and 8.326) were risk factors for malnutrition in patients with CKD (P < 0.05). Malnutrition was correlated with the results of qualitative examination of urinary protein (r = 0.268, P < 0.05).ConclusionGender, age, weight loss, reduced food intake, serum ALB expression were independently associated with malnutrition in patients with chronic kidney disease, Hence, the medical staff should take timely and effective nutrition intervention for the patients with malnutrition, delay the renal function damage of patients with CKD and improve the quality of life of patients. Inpatients with CKD, especially women, should increase their dietary intake, maintain normal weight and improve their nutritional status

Human Papillomaviruses and Papillomatosis Lesions of the Female Lower Genital Tract

Objective: The objective of this study was to determine whether human papillomavirus (HPV) infections are involved in the development of papillomatosis lesions of the lower female genital tract