ABSTRACT
Pharmaceutical drugs and vaccines require the use of material containers for protection, storage, and transportation. Glass and plastic materials are widely used for packaging, and a longstanding challenge in the field is the nonspecific adsorption of pharmaceutical drugs to container walls – the so-called "sticky containers, vanishing drugs” problem – that effectively reduces the active drug concentration and can cause drug denaturation. This challenge has been frequently discussed in the case of the anticancer drug, paclitaxel, and the ongoing coronavirus disease 2019 (COVID-19) pandemic has brought renewed attention to this material science challenge in light of the need to scale up COVID-19 vaccine production and to secure sufficient quantities of packaging containers. To reduce nonspecific adsorption on inner container walls, various strategies based on siliconization and thin polymer films have been explored, while it would be advantageous to develop mass-manufacturable, natural material solutions, especially ones involving pharmaceutical grade excipients. Inspired by how lipid nanoparticles have revolutionized the vaccine field, in this perspective, we discuss the prospects for developing lipid bilayer coatings to prevent nonspecific adsorption of pharmaceutical drugs and vaccines and how recent advances in lipid bilayer coating fabrication technologies are poised to accelerate progress in the field. We critically discuss recent examples of how lipid bilayer coatings can prevent nonspecific sticking of proteins and vaccines to relevant material surfaces and examine future translational prospects. © 2021 The Authors. VIEW published by Shanghai Fuji Technology Consulting Co., Ltd, authorized by Professional Community of Experimental Medicine, National Association of Health Industry and Enterprise Management (PCEM) and John Wiley & Sons Australia, Ltd.
ABSTRACT
Under the background that Covid-19 is spreading across the world, the lifestyle of people has to confront a series of changes and challenges. This also presents new problems and requirements to automation facilities. For example, nowadays masks have almost become necessities for people in public places. However, most access control systems (ACS) cannot recognize people wearing masks and authenticate their identities to deal with increasingly serious epidemic pressure. Consequently, many public entries have turned to an attendant mode that brings low efficiency, infection potential, and high possibility of negligence. In this paper, a new security classification framework based on face recognition is proposed. This framework uses mask detection algorithm and face authentication algorithm with anti-spoofing function. In order to evaluate the performance of the framework, this paper employs the Chinese Academy of Science Institute of Automation-Face Anti-spoofing Datasets (CASIA-FASD) and Reply-Attack datasets as benchmarks. Performance evaluation indicates that the Half Total Error Rate (HTER) is 9.7%, the Equal Error Rate (EER) is 5.5%. The average process time of a single frame is 0.12 seconds. The results demonstrate that this framework has a high anti-spoofing capability and can be employed on the embedded system to complete the mask detection and face authentication task in real-time.