Emergency Medicine Influencers' Twitter Use During the COVID-19 Pandemic: A Mixed-methods Analysis.

INTRODUCTION: The objective of this study was to analyze the messages of influential emergency medicine (EM) Twitter users in the United States (US) during the early stages of the coronavirus disease 2019 (COVID-19) global pandemic by characterizing the themes, emotional tones, temporal viewpoints, and depth of engagement with the tweets. METHODS: We performed a retrospective mixed-methods analysis of publicly available Twitter data derived from the publicly available "Coronavirus Tweet IDs" dataset, March 3, 2020-May 1, 2020. Original tweets and modified retweets in the dataset by 50 influential EM Twitter users in the US were analyzed using linguistic software to report the emotional tone and temporal viewpoint. We qualitatively analyzed a 25% random subsample and report themes. RESULTS: There were 1315 tweets available in the dataset from 36/50 influential EM Twitter users in the US. The majority of tweets were either positive (455/1315, 34.6%) or neutral (407/1315, 31%) in tone and focused on the present (1009/1315, 76.7%). Qualitative analysis identified six distinct themes, with users most often sharing news or clinical information. CONCLUSIONS: During the early weeks of the COVID-19 pandemic, influential EM Twitter users in the US delivered mainly positive or neutral messages, most often pertaining to news stories or information directly relating to patient care. The majority of these messages led to engagement by other users. This study underscores how EM influencers can leverage social media in public health outbreaks to bring attention to topics of importance.

Surface Engineering of Spherical Metal Nanoparticles with Polymers toward Selective Asymmetric Synthesis of Nanobowls and Janus-Type Dimers.

New synthetic methods capable of controlling structural and compositional complexities of asymmetric nanoparticles (NPs) are very challenging but highly desired. A simple and general synthetic approach to designing sophisticated asymmetric NPs by anisotropically patterning the surface of isotropic metallic NPs with amphiphilic block copolymers (BCPs) is reported. The selective galvanic replacement and seed-mediated growth of a second metal can be achieved on the exposed surface of metal NPs, resulting in the formation of nanobowls and Janus-type metal-metal dimers, respectively. Using Ag and Au NPs tethered with amphiphilic block copolymers of poly(ethylene oxide)-block-polystyrene (PEO-b-PS), anisotropic surface patterning of metallic NPs (e.g., Ag and Au) is shown to be driven by thermodynamical phase segregation of BCP ligands on isotropic metal NPs. Two proof-of-concept experiments are given on, i) synthesis of Au nanobowls by a selective galvanic replacement reaction on Janus-type patched Ag/polymer NPs; and ii) preparation of Au-Pd heterodimers and Au-Au homodimers by a seed-mediated growth on Janus-type patched Au/polymer NPs. The method shows remarkable versatility; and it can be easily handled in aqueous solution. This synthetic strategy stands out as the new methodology to design and synthesis asymmetric metal NPs with sophisticated topologies.

Mathematical simulation of membrane protein clustering for efficient signal transduction.

Initiation and propagation of cell signaling depend on productive interactions among signaling proteins at the plasma membrane. These diffusion-limited interactions can be influenced by features of the membrane that introduce barriers, such as cytoskeletal corrals, or microdomains that transiently confine both transmembrane receptors and membrane-tethered peripheral proteins. Membrane topographical features can lead to clustering of receptors and other membrane components, even under very dynamic conditions. This review considers the experimental and mathematical evidence that protein clustering impacts cell signaling in complex ways. Simulation approaches used to consider these stochastic processes are discussed.