Mechanical and architectural features play an important role in designing biomedical devices. The use of materials (i.e., Ti6Al4V) with Young's modulus higher than those of natural tissues generally cause stress shielding effects, bone atrophy, and implant loosening. However, porous devices may be designed to reduce the implant stiffness and, consequently, to improve its stability by promoting tissue ingrowth. If porosity increases, mass transport properties, which are crucial for cell behavior and tissue ingrowth, increase, whereas mechanical properties decrease. As reported in the literature, it is always possible to tailor mass transport and mechanical properties of additively manufactured structures by varying the architectural features, as well as pore shape and size. Even though many studies have already been made on different porous structures with controlled morphology, the aim of current study was to provide only a further analysis on Ti6Al4V lattice structures manufactured by selective laser melting. Experimental and theoretical analyses also demonstrated the possibility to vary the architectural features, pore size, and geometry, without dramatically altering the mechanical performance of the structure.
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http://dx.doi.org/10.1155/2019/3212594 | DOI Listing |
Breast Cancer Res
January 2025
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging in clinical practice. In this study, we validated the correlation between EMT status and resistance to endocrine therapy in ER+ breast cancer from a transcriptomic perspective.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
January 2025
Tianjin University, School of Chemical Engineering and Technology, Yaguan Road #135, Jinnan District, Tianjin 300354, P. R. China, CHINA.
In this study, we developed new chiral hybrid perovskites, (R/S-MBA)(GA)PbI4, by incorporating achiral guanidinium (GA+) and chiral R/S-methylbenzylammonium (R/S-MBA+) into the perovskite framework. The resulting materials possess a distinctive structural configuration, positioned between 1D and 2D perovskites, which we describe as 1.5D.
View Article and Find Full Text PDFComput Biol Med
January 2025
Thai Nguyen University of Information and Communication Technology, Thai Nguyen City, Viet Nam. Electronic address:
Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and progression of various diseases, including liver, cardiac, pulmonary, and neurological disorders. However, identifying succinylation sites through experimental methods is often labor-intensive, costly, and technically challenging.
View Article and Find Full Text PDFNeural Netw
January 2025
State Key Laboratory of Public Big Data, Guizhou University, 550025, China; Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Guizhou University, 550025, China; College of Computer Science and Technology, Guizhou University, 550025, China. Electronic address:
Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic structure. To distinguish semantic expressions between relation instances, manually designed rules or elaborate deep architectures are usually applied to learn task-relevant representations.
View Article and Find Full Text PDFJ Phys Chem Lett
January 2025
Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.
Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments.
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