Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.; Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.
Published: December 2015
AI Article Synopsis
Article Abstract
Natural materials, such as bone, teeth, shells, and wood, exhibit outstanding properties despite being porous and made of weak constituents. Frequently, they represent a source of inspiration to design strong, tough, and lightweight materials. Although many techniques have been introduced to create such structures, a long-range order of the porosity as well as a precise control of the final architecture remain difficult to achieve. These limitations severely hinder the scale-up fabrication of layered structures aimed for larger applications. We report on a bidirectional freezing technique to successfully assemble ceramic particles into scaffolds with large-scale aligned, lamellar, porous, nacre-like structure and long-range order at the centimeter scale. This is achieved by modifying the cold finger with a polydimethylsiloxane (PDMS) wedge to control the nucleation and growth of ice crystals under dual temperature gradients. Our approach could provide an effective way of manufacturing novel bioinspired structural materials, in particular advanced materials such as composites, where a higher level of control over the structure is required.
Background: Radium-223 (Ra-223) received U.S. Food and Drug Administration (FDA) approval for treating castration-resistant prostate cancer with symptomatic bone metastases, excluding visceral metastases.
The Israel military occupation, ongoing for over 75 years, has profoundly impacted the health and well-being of Palestinians. Despite longstanding calls for Universal Health Coverage (UHC) and sustainable development, the response of global health systems and organizations to crises such as the recent large-scale military assault on Gaza in October 2023 has been inadequate. There is a critical need to examine why these global health approaches have failed and how they can be restructured to address the unique challenges in Gaza effectively.
Immune checkpoint inhibitor (ICI) therapies have made considerable advances in cancer immunotherapy, but the complex and diverse spectrum of ICI-induced toxicities poses substantial challenges to treatment outcomes and computational analysis. Here we introduce DySPred, a dynamic graph convolutional network-based deep learning framework, to map and predict the toxicity profiles of ICIs at the population level by leveraging large-scale real-world pharmacovigilance data. DySPred accurately predicts toxicity risks across diverse demographic cohorts and cancer types, demonstrating resilience in small-sample scenarios and revealing toxicity trends over time.
State Key Laboratory of Macromolecular Drugs and Large-Scale Preparation, School of Pharmaceutical Sciences and Food Engineering, Liaocheng University, Liaocheng 252059, China.
pH-sensitive lipids are important components of lipid nanoparticles, which enable the targeted delivery and controlled release of drugs. Understanding the mechanism of pH-triggered drug release at the molecular level is important for the rational design of ionizable lipids. Based on a recently reported pH-switchable lipid, named SL2, molecular dynamics (MD) simulations were employed to explore the microscopic mechanism behind the membrane destabilization induced by the conformational change of pH-switchable lipids.
Artificial intelligence (AI) is revolutionizing scientific discovery because of its super capability, following the neural scaling laws, to integrate and analyze large-scale datasets to mine knowledge. Foundation models, large language models (LLMs) and large vision models (LVMs), are among the most important foundations paving the way for general AI by pre-training on massive domain-specific datasets. Different from the well annotated, formatted and integrated large textual and image datasets for LLMs and LVMs, biomedical knowledge and datasets are fragmented with data scattered across publications and inconsistent databases that often use diverse nomenclature systems in the field of AI for Precision Health and Medicine (AI4PHM).
