Extensive experiments are performed to qualitatively and quantitatively evaluate the effectiveness of our proposed method. In the meanwhile, it outperforms state-of-the-art methods on three different nuclei segmentation datasets.A holistic multitask regression approach was implemented to tackle the limitations of clinical image analysis. Standard practice requires identifying multiple anatomic structures in multiple planes from multiple anatomic regions using multiple modalities. The proposed novel holistic multitask regression network (HMR-Net) formulates organ segmentation as a multitask learning problem. Multitask learning leverages the strength of joint task problem solving from capturing task correlations. HMR-Net performs multitask regression by estimating an organ's class, regional location, and precise contour coordinates. The estimation of each coordinate point also corresponds to another regression task. HMR-Net leverages hierarchical multiscale and fused organ features to handle nonlinear relationships between image appearance and distinct organ properties. Simultaneously, holistic shape information is captured by encoding coordinate correlations. The multitask pipeline enables the capturing of holistic organ information (e.g. class, location, shape) to perform shape regression for medical image segmentation. HMR-Net was validated on eight representative datasets obtained from a total of 222 subjects. A mean average precision and dice score reaching up to 0.81 and 0.93, respectively, was achieved on the representative multiapplication database. The generalized model demonstrates comparable or superior performance compared to state-of-the-art algorithms. The high-performance accuracy demonstrates our model as an effective general framework to perform organ shape regression in multiple applications. This method was proven to provide high-contrast sensitivity to delineate even the smallest and oddly shaped organs. HMR-Net's flexible framework holds great potential in providing a fully automatic preliminary analysis for multiple types of medical images.Improving the quality of image-guided radiation therapy requires the tracking of respiratory motion in ultrasound sequences. However, the low signal-to-noise ratio and the artifacts in ultrasound images make it difficult to track targets accurately and robustly. In this study, we propose a novel deep learning model, called a Cascaded One-shot Deformable Convolutional Neural Network (COSD-CNN), to track landmarks in real time in long ultrasound sequences. Specifically, we design a cascaded Siamese network structure to improve the tracking performance of CNN-based methods. https://www.selleckchem.com/products/zinc05007751.html We propose a one-shot deformable convolution module to enhance the robustness of the COSD-CNN to appearance variation in a meta-learning manner. Moreover, we design a simple and efficient unsupervised strategy to facilitate the network's training with a limited number of medical images, in which many corner points are selected from raw ultrasound images to learn network features with high generalizability. The proposed COSD-CNN has been extensively evaluated on the public Challenge on Liver UltraSound Tracking (CLUST) 2D dataset and on our own ultrasound image dataset from the First Affiliated Hospital of Sun Yat-sen University (FSYSU). Experiment results show that the proposed model can track a target through an ultrasound sequence with high accuracy and robustness. Our method achieves new state-of-the-art performance on the CLUST 2D benchmark set, indicating its strong potential for application in clinical practice. An in vitro physiologically relevant test based on the standard Unified Bioaccessibility Method (UBM) combined with inductively coupled plasma mass spectrometry was performed in this study to ascertain the elemental bioaccessibility pools of tape tea as emerging low-cost abuse drug under fasted conditions. Elemental quantification in tape tea and body fluid extracts was performed by an inductively coupled plasma quadrupole mass spectrometer - ICP-MS, and for sample preparation of the bioaccessibility extracts prior to ICP-MS analysis, a microwave-assisted acid decomposition was applied by using a microwave oven. The Unified Bioaccessibility Method (UBM) was considered for investigation of elemental bioaccessibility in tape tea, required a full set of organic compounds, salts, and enzymes. Considering total element evaluation through ICP-MS, Co, Ni, Mn, and Zn are found at the highest concentrations in the sample, namely 415 ± 36, 202 ± 55, 1389 ± 225 and 2397 ± 197 μg L , respectively. Regarding the or expected by their high total concentrations.Chronic lung disease has become a leading cause of death in recent years. Despite several attempts to discover and develop new therapeutic approaches, patients often suffer a poor quality of life, and are faced with an increased risk of developing lung cancer. Lung cancer often occurs as an end-stage after years of chronic lung disease. An increased understanding of the pathophysiology of chronic lung disease may be obtained from studying the role of autophagy in its initiation and progression. MicroRNAs (miRNAs) play a critical role in the modulation of autophagy, and their deregulation could be associated with the initiation and progression of several chronic lung diseases. Herein, we documented that up/down regulation of miRNAs can activate or inhibit autophagy in chronic lung diseases including lung cancer. Therefore, theses miRNAs could be a promising therapeutic tool for lung cancer specially in drug-resistance lung cancer cells.Autologous platelet sequestration pattern is associated with post-splenectomy platelet response in patients with immune thrombocytopenia (ITP). However, published results are contradictory, and have not been systematically reviewed. Our aim is to systematically review and meta-analyse the association between sequestration pattern and post-splenectomy platelet response. Articles were selected from MEDLINE when they a) included ITP patients, b) performed scintigraphy, and c) included post-splenectomy platelet response. The 23 included studies (published between 1969-2018) represented 2966 ITP-patients. Response to splenectomy occurred most frequently in patients with a splenic pattern (87.1 % in splenic versus 47.1 % in mixed and 25.5 % in hepatic patterns). A pooled analysis of 8 studies showed an odds ratio of 14.21 (95 % CI 3.65-55.37) for platelet response in the splenic versus the hepatic group. Our findings indicate that a splenic sequestration pattern is associated with better response after splenectomy.