课程文献中心 更多>>

关键词： Alternative animal models   Angiogenesis assay   Biomaterials screening   Chorioallantoic membrane   Drug testing   In ovo and ex ovo model   Preclinical research  

摘要： The chicken embryo chorioallantoic membrane (CAM) model has emerged as a valuable tool in the fields of biomaterials, drug testing, and angiogenesis. It offers a cost-effective, ethical, and biologically relevant alternative to traditional in vivo mammalian models, aligning with the 3R principle. The CAM model is extensively used in vascular disease research, drug screening, and biomaterial biocompatibility studies, enabling a real-time analysis of angiogenic responses. Furthermore, it serves as an effective platform for evaluating tumor growth, xenografts, including photodynamic therapy (PDT), photodynamic diagnosis (PDD) applications, and blood-brain barrier permeability. Recent advancements have expanded its role in nanomedicine and regenerative medicine, demonstrating its versatility in preclinical research. Despite its advantages, challenges such as scalability and standardization still remain unresolved. This review explores CAM's vasculogenesis, angiogenesis, and lymphangiogenesis, highlighting its structural characteristics and accessibility for research applications. Furthermore, in ovo and ex ovo methodologies are discussed, detailing their advantages and limitations in different experimental contexts. Moreover, this review addresses current gaps and proposes future directions to enhance the applicability of the CAM model in biomedical sciences. Following methodological improvements and the incorporation of cutting-edge imaging and molecular approaches, the CAM assay continues advancing into a powerful alternative to the study of vascular biology, cancer, drug testing, and biomaterial interactions.

关键词： Volumetric neutron source   Remote handling   Conceptual design   CAD   FEM  

摘要： A feasibility study of a new testing facility, i.e., the Volumetric Neutron Source (VNS) in a tokamak configuration, was initiated in 2023 and is now under way to address potential showstoppers. Key outcomes of the engineering activities are presented in [1] and the latest improvements in [2]. The main goal of the VNS is to provide a realistic environment to test the in-vessel components of fusion reactors, in particular the tritium-breeding blanket. Lessons learned during previous years of DEMO development point to the need for thorough nuclear design integration already at the level of the basic machine design; hence, issues related to the maintenance of the in-vessel components have been addressed at a basic level. The work presented here describes the remote maintenance strategy for VNS blanket segments, which is an adaptation of the concept proposed for DEMO in previous years [3]. The work discusses the basic approach for the development of an integrated remote blanket strategy for the VNS blanket. Consistent with the initial requirements of the machine, the remote replacement sequence of the blankets, the potential list of the required tools and transporter, and the conceptual design of some of the main tools are presented and discussed in this article.

关键词： Computer-aided design/computer-aided manufacturing   axiopulpal line angle design   cement space setting   indirect ceramic inlay   milling accuracy  

摘要： This study investigated the effects of the axiopulpal line angle design and different cement space settings on the adaptation of indirect ceramic inlays.

关键词： CAD/CAM ceramics   glazing   lithium-disilicate   polishing paste   profilometry   surface roughness  

摘要： The aim of this study was to investigate the effects of chairside finishing methods, including the use of a specialized polishing paste suitable for clinical conditions, on the surface roughness of lithium disilicate-based CAD/CAM ceramics.

关键词： ACSS1   ACSS2   ACLY   CAD   DHODH   Acetate metabolism   Oncometabolite   Cancer metabolism  

摘要： Acetate serves as an alternative carbon source in nutrient-limited tumors, yet its role in supporting nucleotide biosynthesis remains poorly understood. Here, we identify the mitochondrial enzyme ACSS1 as a key metabolic driver in mantle cell lymphoma (MCL), diffuse large B-cell lymphoma (DLBCL), and chronic lymphocytic leukemia (CLL). ACSS1 is frequently overexpressed and catalyzes the conversion of acetate to mitochondrial acetyl-CoA, sustaining oxidative metabolism and biosynthesis under nutrient stress. Genetic silencing of ACSS1 impairs mitochondrial respiration and disrupts acetate incorporation into acetyl-CoA, TCA cycle intermediates, glutamate, and aspartate, while markedly reducing ¹³C-acetate labeling of dihydroorotate and orotate, intermediates in de novo pyrimidine synthesis. Untargeted metabolomics reveal enrichment of pyrimidine biosynthesis pathways in ACSS1-high cells. Notably, acetate or uridine supplementation rescues the growth of ACSS1-deficient cells, confirming a functional link between acetate metabolism and nucleotide synthesis. Importantly, in vivo studies using two different MCL xenografts demonstrate that ACSS1 knockdown profoundly suppresses tumor growth, indicating that ACSS1 is required not only for metabolic adaptation of lymphoma cells in vitro but also in vivo. Collectively, our results uncover an ACSS1-dependent mitochondrial acetate–pyrimidine axis that sustains lymphoma growth and represents a previously unrecognized therapeutic vulnerability.

关键词： Radiation hardness   Content addressable memory   Approximate search   Voltage scaling   Low-power design  

摘要： This paper presents the design and first experimental validation under heavy ion (HI) irradiation of a Content Addressable Memory (HardCAM) fabricated in 65 nm CMOS technology. HardCAM was experimentally tested alongside a standard SRAM array to assess data integrity and proper function under HI beam irradiation. While the SRAM exhibited increasing error rates and consequent data corruption due to single event effects, radhard HardCAM maintained correct operation even under extreme conditions that induced several bit flips per memory row. HardCAM requires no error correction mechanism. It applies similarity, or Hamming distance tolerant search, to endure and tolerate errors rather than correct them. Our experimental results establish HardCAM as a viable and robust CAM solution for radiation-sensitive applications, such as space and aerospace systems.

关键词： Artificial intelligence applications   Computer-aided design   Design automation   Natural Language Processing   Engineering computation  

摘要： This study explores the automation of engineering design tasks using Generative Artificial Intelligence, focusing on the dimensioning machine elements and generation of their respective CAD models, specifically bolted connections, in the open-source software FreeCAD. Three system architectures were developed and evaluated: All-in-One, Chatbot-Designbot, and Chatbot-Calculator-Code Generator. These frameworks integrate Large Language Models, such as GPT-2 and CodeGen, which were fine-tuned using Parameter-Efficient Fine-Tuning and Low-Rank Adaptation. While the All-in-One architecture consolidates all tasks into a single model, the Chatbot-Designbot and Chatbot-Calculator-Code Generator architectures decompose the process into specialized modules for dialogue interaction, parameter extraction, part dimensioning, and CAD code generation. The evaluation results show that the Chatbot-Calculator-Code Generator configuration achieves the lowest overall error rate for the four steps combined (2%) with a total training time of 63.4 min. This configuration outperforms the Chatbot-Designbot (3.96%, 117.7 min) and All-in-One (53%, 24.2 min) architectures. These findings demonstrate that compact, fine-tuned Large Language Models can enable accurate and efficient design automation, even with limited data. This work establishes a methodological foundation for scalable, Generative Artificial Intelligence -driven CAD systems and interactive engineering design workflows.

关键词： Attention mechanisms   Dual-phase CT   Dual-task learning   Lymph node metastasis   Rectal cancer  

摘要： Accurate preoperative prediction of lymph node metastasis and precise tumor segmentation are critical for treatment planning in rectal cancer. However, existing approaches often rely on single-task models or handcrafted features, limiting generalization and diagnostic robustness. In this work, we propose CAD-Net, a novel cross-phase attention-driven dual-task learning framework that jointly performs tumor segmentation and LNM classification from dual-phase contrast-enhanced CT images. CAD-Net introduces a Cross-phase Attention Feature Interaction Unit to dynamically fuse arterial and portal venous phase features, and a Dual-path Hierarchical Fusion Codec to integrate multi-scale contextual and morphological cues. Additionally, a dual-task consistency loss enforces spatial alignment between segmentation and classification outputs. We evaluate CAD-Net on the public LNMCR dataset, achieving state-of-the-art performance with a Dice score of 87.24% for segmentation and 87.4% accuracy for LNM diagnosis. Ablation and hyperparameter studies demonstrate the contribution of each module, while cosine similarity visualizations confirm improved feature coherence. The findings indicate that CAD-Net is a promising approach for improving preoperative decision-making in rectal cancer management.

关键词： Machine learning   Helical distillation column   Additive manufacturing   Number of stages   Process intensification  

摘要： Traditional workflows in chemical engineering, from design to characterization, have served the field well. Emerging technologies, such as machine learning, now offer opportunities for faster design iterations and better integration of the various development stages. This study demonstrates how machine learning (ML) can reduce this gap by directly integrating experimental data into predictive models. Focusing on a Additive Manufactured helical micro-distillation column design for separating liquid mixtures, several ML models were trained to predict the number of theoretical stages based on geometric parameters and operating conditions. 11 variants were designed, built, and tested to collect 197 experimental data sets to assess the feasibility of five predictive ML models from experimental data. Among the algorithms tested, the Gradient Boosting Regressor achieved the best performance with a coefficient of determination $R^2=0.7934$. The work highlights the behavior of the model across different regimes, identifies key sources of error, and proposes a hybrid experimental–ML workflow for rapid screening of distillation designs. This approach accelerates process development and reduces the need for extensive experimentation, especially in time-consuming tasks such as distillation.