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关键词： 3D printing   CAD/CAM   complete dentures   elderly population   edentulous patients   oral rehabilitation   patient satisfaction  

摘要： Objectives: This systematic review compares computer-aided design and computer-aided manufacturing (CAD/CAM) 3D-printed complete dentures (CDs) with conventional ones in terms of patient satisfaction. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting criteria for systematic reviews were followed in conducting this systematic review. The study question was "What are the patient satisfaction outcomes of 3D-printed versus conventional CDs in edentulous patients?" according to the population, intervention, comparison, and outcome (PICO) framework. A comprehensive electronic search was conducted across three databases (PubMed/Medline, Web of Science core collection, and Scopus;last update: 18 August 2024) to obtain clinical trials that compared traditional and 3D-printed CDs. The retrieved articles were screened, their data were extracted, and their quality was evaluated. Results: The initial search retrieved 803 publications;12 were chosen for a thorough review, and 5 of them-4 randomized cross-over studies and 1 randomized three-parallel arm study-met the requirements for this systematic review. One study showed significant differences in five of nine patient denture satisfaction domains, positively favoring the conventional CDs. Two studies showed non-significant differences in satisfaction domains between the conventional and 3D-printed groups, except for aesthetics and pronunciation. On the contrary, the satisfaction scores in two other studies showed no significant difference between the conventional and 3D-printed denture groups. Conclusions: The analysis of the included studies and evidence gathered demonstrates that CAD/CAM 3D-printed CDs seem to be comparable with conventional CDs in terms of overall patient satisfaction;however, 3D-printed CDs generate some concerns related to aesthetics and speech.

关键词： Lung   Lung cancer   Validation study   Early detection of cancer   Computer-assisted detection  

摘要： ObjectivesThis study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans.MethodsThis retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1's detection sensitivity was also assessed.ResultsA total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD's detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2% 42/47 (89.4% and 25/42 (59.5% respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8% 16/42 (38.1% and 18/25 (72.0% respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9% 123/149 (82.6% and 141/149 (94.6%) for calcification, spiculation, and location, respectively.ConclusionsAlthough the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader's detection rate. While promising characterization performances are obtained, the tool's performance in terms of texture classification remains a subject of concern.Clinical relevance statementNumerous lung nodul

关键词： italic>Helicobacter pylori   CAD   infection   stroke   CagA  

摘要： Coronary artery disease (CAD) is one of the leading causes of death worldwide, significantly contributing to mortality in both developed and developing nations. CAD arises from a combination of risk factors, including atherosclerosis, dyslipidemia, hypertension, diabetes, and smoking. In recent years, growing evidence has suggested a potential link between infectious agents and cardiovascular diseases. Among these, Helicobacter pylori (H. pylori) infection has been hypothesized for over a decade to play a role in the pathogenesis of CAD. This hypothesis is based on the bacterium's ability to trigger host inflammatory or autoimmune responses, potentially contributing to the progression of atherosclerotic plaques and coronary events. The association between H. pylori infection and CAD is of considerable interest as it opens new avenues for prevention and management strategies in cardiovascular health. Understanding this relationship could lead to innovative approaches to reducing the burden of CAD, particularly in populations with a high prevalence of H. pylori. In this review, we aim to provide a comprehensive overview of the most recent evidence on the involvement of H. pylori in the development and prognosis of CAD. By analyzing and synthesizing current findings, we seek to shed light on unresolved questions and clarify the ambiguous aspects of this potential connection. Our goal is to contribute to a deeper understanding of how H. pylori, may influence cardiovascular disease and to inspire further research in this critical area.

关键词： dihydroorotase   CAD   anticancer   loop   5-fluoroorotate   plumbagin   5-aminouracil   5-fluorouracil  

摘要： Dihydroorotase (DHOase;EC 3.***.2.3) is a zinc-dependent metalloenzyme that plays a key role in the de novo pyrimidine biosynthesis pathway, catalyzing the reversible cyclization of N-carbamoyl aspartate to dihydroorotate. This reaction is essential for the production of uridine monophosphate, the precursor of all pyrimidine nucleotides required for DNA and RNA synthesis. Despite its conserved enzymatic function, DHOase exhibits significant structural diversity across species, particularly in its oligomeric states, gene fusion patterns, and active site architecture. A crucial structural feature of DHOase is its flexible active site loop, which undergoes dynamic conformational changes during catalysis. Previously, the loop-in conformation was associated with substrate binding, whereas the loop-out conformation was linked to product release and non-substrate ligand binding. However, recent crystallographic studies challenge this paradigm, revealing that certain non-substrate ligands and inhibitors, including malate, 5-fluoroorotate, plumbagin, 5-aminouracil, and 5-fluorouracil, interact with DHOase via a loop-in binding mechanism rather than the previously assumed loop-out mode. These findings necessitate a reassessment of the catalytic mechanism of DHOase and underscore the active site loop as a potential target for drug development. This review revisits the structural and biochemical mechanisms of DHOase, with a focus on recent crystallographic insights that redefine the loop-in binding mode for ligand interaction. By leveraging the unique conformational dynamics of the active site loop, novel inhibitors may be developed to selectively target pyrimidine biosynthesis in cancer cells and microbial pathogens. These insights emphasize the crucial role of structural biology in therapeutic design and highlight DHOase as a promising drug target.

关键词： 3D point cloud   Decommissioning   Deep learning   Semantic segmentation  

摘要： This paper describes a 3D point cloud segmentation pipeline that contributes to the efficiency of decommissioning works at the Fukushima Daiichi Nuclear Power Station. For decommissioning works, simulations and calculations for preliminary work planning using 3D structural models are crucial from a safety and efficiency viewpoint. However, 3D modeling works typically require high costs. Therefore, we aim to improve the efficiency of 3D modeling by segmenting geometric shape regions into categories in a 3D point cloud state using deep learning. Our pipeline uses 3D computer-aided design semantics to create a training dataset that reduces annotation costs and helps learn human knowledge. Performance evaluation results show that the discriminator can discriminate major structural categories with high accuracy using deep learning models. However, we confirm that even the state-of-the-art model has limitations in discriminating structures containing similar shapes between categories and structures in categories with a small number of training data. In the analysis of evaluation results, we discuss challenges encountered by our pipeline for practical applications.

关键词： 工程制图  

ISBN: (纸本)9787113317560

摘要： 本书是以AutoCAD、Visio两个工程技术类常用软件作为授课载体的一本基于工作任务的学习手册。本书由4个教学情境12个工作任务组成，包括CAD制图初体验、简单二维图形全掌握、复杂二维图形掌握、Visio绘图全掌握四个教学情境。

关键词： image classification   algae   computer vision   object detection   deep learning  

摘要： The natural occurrence of harmful algal blooms (HABs) adversely affects the quality of clean and fresh water. They pose increased risks to human health, aquatic ecosystems, and water bodies. Continuous monitoring and appropriate measures must be taken to combat HABs. Deep learning models that utilize computer vision play a vital role in identifying and classifying harmful algal blooms in aquatic environments and water storage facilities. Inspecting algal blooms using conventional methods, such as algae detection under microscopes, is difficult, expensive, and time-consuming. Deep learning algorithms have shown a notable and remarkable performance in the image classification domain and its applications, including microscopic algae species classification and detection. In this study, we propose a deep learning-based approach for classifying microscopic images of algae using computer vision. This approach employs a convolutional neural network (CNN) model integrated with two additional blocks-squeeze and dense blocks-to determine the presence of algae, followed by adding Grad-CAM to the proposed model to ensure interpretability and transparency. We performed several experiments on our custom dataset of microscopic algae images. Data augmentation techniques were employed to increase the number of images in the dataset, whereas pre-processing techniques were implemented to elevate the overall data quality. Our proposed model was trained on 3200 images consisting of four classes. We also compared our proposed model with the other transfer learning models, i.e., ResNet50 and Vgg16. Our proposed model outperformed the other two deep learning models. The proposed model demonstrated 96.7% accuracy, while Resnet50, EfficientNetB0, and VGG16 showed accuracy of 85.0%, 92.96%, and 93.5%, respectively. The results of this research demonstrate the potential of deep learning-based approaches for algae classification. This deep learning-based algorithm can be deployed in real-time ap

关键词： cosmology: observations   galaxies: general   gravitational lensing: strong  

摘要： Galaxy-scale strong gravitational lenses are valuable objects for a variety of astrophysical and cosmological applications. Strong lensing galaxies are rare, so efficient search methods, such as convolutional neural networks (CNNs are often used on large imaging datasets. In this work, we apply a new technique to improve the performance of supervised neural networks by subtracting the central (lensing) galaxy light from both the training and test datasets. We use multiband imaging data from the Hyper Suprime-Cam Subaru Strategic Program as our training and test datasets. By subtracting the lensing galaxy light, we increase the contrast of the lensed source compared to the original imaging data. We also apply the light subtraction to non-lenses in order to compare them to the light-subtracted lenses. Residual features resulting from poor light subtraction can adversely affect the performance of networks trained on the subtracted images alone. We find that combining the light-subtracted images with the original $gri$-band images for training and classification can overcome this and improve the overall classification accuracy. We find the area under the receiver operating characteristic curve can be improved to 0.841 using the combination of the fiducial images and light-subtracted images, compared to 0.808 for the fiducial imaging dataset alone. This may be a promising technique for improving future lens searches using CNNs.

关键词： line structured light vision   cam base circle radius   point cloud collection  

摘要： Using line structured light vision measurement technology, it is possible to perform non-contact measurement of the cam's base circle radius. To address the issue of the structured light plane not being perpendicular to the axis of the cam being measured, this paper obtains the axis equation of the cam being measured through calibration, and based on the axis equation, it obtains the spatial equation of a virtual light plane that is perpendicular to the axis. The point cloud on the surface of the cam is projected onto the virtual plane, and the base circle radius of the cam is obtained using the coordinates of the projected points. To improve the measurement accuracy of the cam's base circle radius, this paper designs a base revision model to address measurement errors caused by the non-coincidence of bases. The measurement results of the base circle radii of four cams on the engine camshaft show that the line structured light vision measurement technology meets the measurement requirements and is feasible for non-contact measurement. The innovation presented herein lies in the development of a cloud acquisition model predicated on line structured light vision technology, which effectively converts a complex spatial curve fitting challenge into a simpler planar curve fitting problem. This transformation not only streamlines the measurement process for the cam base circle radius but also significantly enhances the measurement precision of engine camshafts.