Prognostic value of growth differentiation factor-15 in Chinese patients with heart failure: A prospective observational study

  Background: Growth differentiation factor-15 (GDF-15), a biomarker associated with remodeling, oxidative stress and inflammation, has been used to stratify heart failure (HF) patients. However, its prognostic value in Chinese HF patients is still unknown. Methods: GDF-15 levels were examined on admission in 272 consecutive HF patients in Beijing Hospital (a Chinese tertiary medical center) by a commercial enzyme-linked immunosorbent assay. We recorded the incidence of all-cause mortality and/or readmission for HF during a median follow-up period of 558 days. Patients were stratified according to the tertiles of GDF-15. Results: Fifty-three (19.5%) patients died and 103 (37.9%) patients had major adverse cardiac events (MACE) which included the composite outcome of all-cause mortality or readmission for HF at the end of follow-up. Kaplan-Meier survival curves showed that the third tertile of GDF-15 was associated with increased rate of all-cause mortality (compared with the first and second tertiles, log rank p = 0.001 and 0.001, respectively) or MACE (compared with the first and second tertiles, log rank p = 0.002 and p < 0.001, respectively). In addition, multivariate Cox regression model showed that the highest tertile of GDF-15 was independently associated with increased risk of all-cause death (hazard ratio = 5.95, 95% confidence interval 1.88–18.78, p = 0.002) compared with the lowest tertile after adjustment for related clinical variables such as age, renal function or N-terminal pro-B-type natriuretic peptide.  Conclusions: Plasma GDF-15 is an independent predictor of all-cause mortality in Chinese patients with HF. It may potentially be used to stratify and prognosticate HF patients

VEGF Is Involved in the Increase of Dermal Microvascular Permeability Induced by Tryptase

Tryptases are predominantly mast cell-specific serine proteases with pleiotropic biological activities and play a critical role in skin allergic reactions, which are manifested with rapid edema and increases of vascular permeability. The exact mechanisms of mast cell tryptase promoting vascular permeability, however, are unclear and, therefore, we investigated the effect and mechanism of tryptase or human mast cells (HMC-1) supernatant on the permeability of human dermal microvascular endothelial cells (HDMECs). Both tryptase and HMC-1 supernatant increased permeability of HDMECs significantly, which was resisted by tryptase inhibitor APC366 and partially reversed by anti-VEGF antibody and SU5614 (catalytic inhibitor of VEGFR). Furthermore, addition of tryptase to HDMECs caused a significant increase of mRNA and protein levels of VEGF and its receptors (Flt-1 and Flk-1) by Real-time RT-PCR and Western blot, respectively. These results strongly suggest an important role of VEGF on the permeability enhancement induced by tryptase, which may lead to novel means of controlling allergic reaction in skin

Deep Learning for 3D Point Clouds : A Survey

AbstractPoint cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.Abstract Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames. However, they neither fully exploit the 3D point-wise geometric correspondences, nor effectively tackle the ambiguities in the photometric warping caused by occlusions or illumination inconsistency. To address these problems, this work proposes Density Volume Construction Network (DevNet), a novel self-supervised monocular depth learning framework, that can consider 3D spatial information, and exploit stronger geometric constraints among adjacent camera frustums. Instead of directly regressing the pixel value from a single image, our DevNet divides the camera frustum into multiple parallel planes and predicts the pointwise occlusion probability density on each plane. The final depth map is generated by integrating the density along corresponding rays. During the training process, novel regularization strategies and loss functions are introduced to mitigate photometric ambiguities and overfitting. Without obviously enlarging model parameters size or running time, DevNet outperforms several representative baselines on both the KITTI-2015 outdoor dataset and NYU-V2 indoor dataset. In particular, the root-mean-square-deviation is reduced by around 4% with DevNet on both KITTI-2015 and NYU-V2 in the task of depth estimation. Code is available at https://github.com/gitkaichenzhou/DevNet.Comment: Accepted by European Conference on Computer Vision 2022 (ECCV2022

Effect of the combination of cognitive behavioral therapy and oral paroxetine hydrochloride in patients with post-stroke depression

Purpose: To determine the effects of combined use of cognitive behavioral therapy (CBT) and paroxetine hydrochloride tablets in patients with post-stroke depression (PSD), and its effect on scores on Hamilton Rating Scale for Depression (HAMD) and Stroke Specific Quality of Life Scale (SS-QOL). Methods: Clinical data for 96 patients with PSD who were treated in Dongying Traditional Chinese Hospital, Dongying City, China from June 2018 to June 2019 were retrospectively analyzed. Patients who met the inclusion criteria were divided into treatment group (TG, n = 48) and reference group (RG, n = 48) based on odd and even hospitalization numbers. Both groups received conventional treatment, but RG patients were in addition given clopidogrel, while TG received CBT in combination with paroxetine hydrochloride tablets. Clinical indices were evaluated in both groups before and after treatment. Moreover, therapeutic effects in the two different treatment methods on PSD, as well as on Hamilton Rating Scale for Depression (HAMD) and Stroke Specific Quality of Life Scale (SS-QOL) scores were analyzed. Results: After treatment, TG had lower HAMD score (p < 0.001), lower scores on modified Rankin scale, and few incidences of adverse reactions at 3, 7, 15 and 30 days of treatment (p < 0.05), but higher total clinical effectiveness and mean SS-QOL score (p < 0.05), when compared with RG. Conclusion: Combined use of CBT and oral paroxetine hydrochloride tablets may be a promising strategy for treating depression and enhancing the quality of life of PSD patients, as it greatly improves neurological deficit and prognosis. However, further clinical trials should be carried out prior to introducing it in clinical practice

An integrated texture and depth isomorphic imaging and cross‐modal network for rail surface defect detection and measurement

Rail defects significantly impact train operations, even posing serious safety risks. Existing methods can automatically collect images from the rail surface and identify apparent defects while facing challenges such as high false positive rates, visually subtle defects omit errors, and quantitative defect size measurement. To address these issues, an integrated 2D&3D rail surface defect detection and measurement framework is proposed. Initially, this framework introduces an isomorphic imaging system with a long–short exposure mechanism, which uses a single camera to capture pixel‐level registered 2D texture and 3D depth images in a single imaging procedure. Subsequently, a cross‐modal defect detection network is developed to explore complementary semantic and structural information from 2D and 3D images hierarchically, enhancing defect identification capability. Finally, considering the physical curvature changes of the railhead, a partition projection‐based 3D measurement method is established to provide accurate quantitative measurements for defect depth, width, and length. This study collects 2045 operational rail surface images with visible defects and establishes a standard 2D&3D defect detection dataset to validate model performance. Experimental results show that this technology achieves improvements of 7.26% and 9.17% in maximum F1‐score and recall, compared to prevalent SAINet. The defect depth measurement accuracy reached 0.18 mm. Extensive experiments on publicly available non‐service rail surface defect datasets also demonstrate the effectiveness of the proposed method