The problems in hospitals which led to the development of the clinical engineering profession are described along with recent changes in the hospital environment. The authors discuss how the profession is adapting to these changes. Also discussed is the tendency for BMETs to move into clinical engineering roles.
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Deep learning-based design and experimental validation of a medicine-like human antibody library.
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Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
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Centre for Heart Lung Innovation, University of British Columbia, Vancouver. (K.H., M.A., L.R., Y.L., A.S., H.H., L.R.B., Z.W.L.).
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Flexible deformation and special interface structure in nanoparticle-stabilized Pickering bubbles strengthen the immunological response as adjuvant.
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State Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing 100190, P. R. China.
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Department of Internal Medicine V, University Hospital Schleswig-Holstein and University of Kiel, Kiel, Germany.
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