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Background and objective Biometric measurements of fetal head are important indicators for maternal and fetal health monitoring during pregnancy. 3D ultrasound (US) has unique advantages over 2D scan in covering the whole fetal head and may promote the diagnoses. However, automatically segmenting the whole fetal head in US volumes still pends as an emerging and unsolved problem. The challenges that automated solutions need to tackle include the poor image quality, boundary ambiguity, long-span occlusion, and the appearance variability across different fetal poses and gestational ages. In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes. Methods The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features in a composite and hierarchical way. With little computation overhead, HAS proves to be effective in addressing boundary ambiguity and deficiency. To enhance the spatial consistency in segmentation, we further organize multiple segmentors in a cascaded fashion to refine the results by revisiting context in the prediction of predecessors. Results Validated on a large dataset collected from 100 healthy volunteers, our method presents superior segmentation performance (DSC (Dice Similarity Coefficient), 96.05%), remarkable agreements with experts (-1.6±19.5 mL). With another 156 volumes collected from 52 volunteers, we ahieve high reproducibilities (mean standard deviation 11.524 mL) against scan variations. Conclusion This is the first investigation about whole fetal head segmentation in 3D US. Our method is promising to be a feasible solution in assisting the volumetric US-based prenatal studies.Background and Objectives The Dual-specificity tyrosine-phosphorylation regulated kinase-1A (DYRK1A) a serine/threonine kinase that has freshly gained recognition as an essential drug target due to the discovery of its involvement in pathological diseases. The development of new potent inhibitors of DYRK1A would contribute to clarify the molecular mechanisms of associated diseases. It would administer a new lead compound for molecular-targeted protein, which was the primary focus of our study. Methods The library of in-house synthesized pyrrolone-fused benzosuberene (PBS) compounds was docked with DYRK1A receptor. Further, molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) estimations were conducted to confirm our docking outcomes and compared the stability of chosen complexes with the 2C3 (standard molecule) complex. Results This study reports Ligand15, Ligand14, and Ligand11 as potent inhibitors which showed higher ligand efficiency, binding affinity, lipophilic ligand efficiency, and favorable torsion values as compared to 2C3. Conclusion The stated methodologies revealed a unique mechanism of active site binding. The binding interactions within the active site showed that the chosen molecules had notable interactions than the standard molecule, which led to the generation of potential compounds to inhibit DYRK1A.Background and objective In lung cancer, the determination of mediastinal lymph node (MLN) status as benign or malignant influence treatment planning and survival rate. Invasive pathological tests for the classification of MLNs into benign and malignant have various shortcomings like painfulness, the risk associated with anesthesia, and depends to a large extent on skillset and preferences of the surgeon performing the test. Hence, computer-aided system for MLNs severity detection has been explored widely by the researchers. Very recently, in our earlier concluded work on non-invasive method for MLNs differential diagnosis in computed tomography (CT) images, combination of different data augmentation approaches and state-of-art fully convolutional network (FCN) were implemented to enhance the performance of malignancy detection. However, the performance of FCN network were highly depended on the selection of appropriate data augmentation approach and control of their hyperparameters. Moreover, a standard practure, respectively for MLNs severity detection. The proposed approach achieves superior results with an average accuracy, sensitivity, specificity, and area under curve of 94.95%, 93.65%, 96.67%, and 95%, respectively. Conclusion The obtained results validate the usefulness of GANs for data augmentation in the differential diagnosis of benign and malignant MLNs. The proposed Inception network based classifier for malignancy detection shows promising results compared to all investigated methods presented in various literature.Background Smartphones are convenient for college students. However, overuse of smartphone or smartphone addiction, can lead to problems related to healthy development. The cause of smartphone addiction can be traced to adverse childhood experiences such as childhood psychological maltreatment. Therefore, exploring the cause and mechanism underlying smartphone addiction in college students with a history of childhood psychological maltreatment is crucial. Objective The purpose of this study was to explore the mediating effect of neuroticism and coping style in relationship between childhood psychological maltreatment and smartphone addiction among college students. Methods The participants included 1169 (43.8 % female and 56.2 % male) college students ranging in age from 17 to 23 years (M = 19.89, SD = 1.25). All participants completed a self-report questionnaire measuring childhood psychological maltreatment, neuroticism, coping style, and smartphone addiction symptoms. A multiple mediation model was used to test the hypotheses. Results Findings from mediation analysis showed that both in parallelly and sequentially, neuroticism and negative coping style mediated the relationship between childhood psychological maltreatment and smartphone addiction. Conclusions The present study can provide an understanding of how childhood psychological maltreatment influences college students' smartphone addiction. this website This study also provides implications on how to reduce the effects of childhood psychological maltreatment on smartphone addiction in college students.
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