Weiming Wang

Introduction: Cigarette smoking extract (CSE) can cause endothelial cell (EC) dysfunction, and then promote the occurrence and development of atherosclerosis. However, the molecular mechanisms underlying CSE-induced EC dysfunction are unknown. Sirt1, as a deacetylase, is involved in various biological processes of ECs. Therefore, this study investigated whether CSE induces apoptosis and mitochondrial dysfunction in human umbilical vein endothelial cells (HUVECs) via Sirt1-dependent mechanisms. Methods: HUVEC activity was assessed using MTT and crystal violet staining following treatment with different concentrations of CSE. Lentiviral transfection technology was used to generate HUVECs overexpressing Sirt1. Apoptosis was detected by Tunnel staining. MitoTracker™ Deep Red FM and JC-1 were used to assess mitochondrial structure and membrane potential. ELISA was used to detect the expression of superoxide dismutase (SOD) and malondialdehyde (MDA). qPCR was used to determine mRNA expression. Atherosclerosis was evaluated by oil red O staining in ApoE-KO mice after cigarette smoke exposure. Results: CSE decreased Sirt1 and sonic hedgehog (SHH) expression, leading to mitochondrial dysfunction and apoptosis in HUVECs. Overexpressing Sirt1 or activating the SHH signaling pathway attenuated CSE-induced apoptosis and mitochondrial dysfunction. However, inhibiting the SHH signaling axis attenuated the protective effect of Sirt1 overexpression on CSE-induced apoptosis and mitochondrial dysfunction. In vivo studies also showed that cigarette smoke exacerbated atherosclerosis in ApoE-KO mice, downregulating Sirt1, SHH, and Gli1 expression in the aorta. Additionally, cigarette smoke increased Bax expression and decreased Bcl-2 expression in ApoE-KO mice aortas. Discussions: Smoking can affect all stages of the atherosclerosis process, and the specific mechanism remains unclear. This study confirms that CSE can induce mitochondrial dysfunction and apoptosis of HUVECs by reducing Sirt1 expression and inhibiting SHH signaling activation. These findings provide new insights into the prevention and treatment of smoking-induced atherosclerosis.

Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings. This paper presents to amplify the subtle texture difference between camouflaged objects and the background for camouflaged object detection by formulating multiple texture-aware refinement modules to learn the texture-aware features in a deep convolutional neural network. The texture-aware refinement module computes the biased co-variance matrices of feature responses to extract the texture information, adopts an affinity loss to learn a set of parameter maps that help to separate the texture between camouflaged objects and the background, and leverages a boundary-consistency loss to explore the structures of object details. We evaluate our network on the benchmark datasets for camouflaged object detection both qualitatively and quantitatively. Experimental results show that our approach outperforms various state-of-the-art methods by a large margin.

Mesh denoising is a fundamental component of many disparate reverse engineering applications of measurement surfaces. This article presents a cascaded normal filtering neural network (termed a CNF-Net) for geometry-aware mesh denoising of measurement surfaces. CNF-Net leverages the geometry domain knowledge (GDK) that, a mesh approximates to its underlying surface compactly if all mesh facets at most lie on the surface intersections while not crossing them. Benefiting from the well-estimated underlying geometry of noisy mesh facets, a multiscale guidedly filtered normal descriptor (M-GFND) is formulated, and multiple height maps are constructed from the M-GFND. The height maps can be effectively fed into CNF-Net for learning the transformation matrices between the M-GFND and the ground-truth facet normal. CNF-Net can automatically handle meshes with multiscale geometric features yet corrupted by the noise of different distributions, while existing learning-based wisdoms commonly pursue an overall normal estimation accuracy yet fail to preserve surface significant features. Both visual and numerical evaluations on synthetic and real noise data sets consistently show the clear improvements of CNF-Net over the state-of-the-arts.

Advanced glycation end products (AGEs) have been widely regarded as an important inducing factor in the pathogenesis of diabetic arteriosclerosis, and the proliferation and migration of vascular smooth muscle cells (VSMCs) are also involved in this process. However, it is not clear whether AGEs promote atherosclerosis by inducing the proliferation and migration of VSMCs. To figure out this question, this study investigated the effects of AGEs on the proliferation and migration of human aorta vascular smooth muscle cells (HASMCs) and the underlying mechanisms. This study evaluated the effects of different concentrations of AGEs on cell proliferation and migration. CCK8, transwell, and western blotting assays demonstrated that AGEs significantly increased cell proliferation and migration in a concentration‐dependent manner and that the optimal proproliferative and promigratory concentrations of AGEs were 10 mg/L and 20 mg/L, respectively. AGE‐induced cell proliferation, migration, and expression of filament actin (F‐actin) were markedly attenuated by a PI3K inhibitor (LY2940002). Additionally, the phosphorylation of AKT was reduced when the receptor of advanced glycation end product (RAGE) gene was silenced by lentivirus transfection, which led to a concomitant reduction of the expression of proliferation and migration‐related proteins. These data indicate that AGEs may activate the PI3K/AKT pathway through RAGE and thus facilitate the proliferation and migration of HASMCs.