ABSTRACT
Background The lack of interaction in pharmacology courses, especially in terms of the Coronavirus Disease 2019 (COVID-19) pandemic, has required a fast shift to remote learning at medical schools, causing students to feel anxious and isolated. As a result, new interactive teaching approaches are required to improve pharmacology learning attention and interaction in traditional classrooms or remoting education.Methods: We introduced the bullet screen into pharmacology teaching. Then, a survey was distributed to first-, second- and third-year pre-clinical undergraduate medical and nursing students at the Shanghai Jiaotong University School of Medicine from November 2020 to March 2022. We evaluated the essential features, instructional effectiveness, and recreational value of bullet screens. Responses to structured and open-ended questions about strengths and weaknesses of the bullet screen and overall reflections were coded and compared between medical and nursing students.Results: For the essential features, bullet screen has a high degree of acceptability among students, and this novel instructional style conveniently increased the classroom interaction. For instructional effectiveness, bullet screen might inspire students’ in-depth thinking. Meanwhile, students tended to consider bullet-screen comments as a way to express their support rather than to make additional comments or to express their opposition. For the recreational value, the process of using bullet screen was interesting. The lack of idea might lead to the relative differences between medical and nursing students, indicating that guiding the appropriate use of bullet screen is necessary.Conclusions: The bullet screen may be popularized as an auxiliary teaching approach to promote interaction between teachers and students in the curriculum as well as remote education. It's interesting and beneficial in pharmacology courses, yet there are several aspects that might be improved for popularization.
Subject(s)
COVID-19ABSTRACT
Cryptococcal meningoencephalitis is an emerging infection shifted from primarily ART-naive to being ART-experienced HIV/AIDS patients, COVID-19 patients and also in immune competent individuals, mainly caused by the human opportunistic pathogen Cryptococcus neoformans , yet mechanisms of the brain or CNS dissemination remain to elucidate, which is the deadest process for the disease. Meanwhile, illustrations of clinically relevant responses in cryptococcosis were limited, as the low availabilities of clinical samples. In this study, macaque and mouse infection models were employed and miRNA-mRNA transcriptomes were performed and combined, which revealed cytoskeleton, a major feather in HIV/AIDS patients, was a centric pathway regulated in both two infection models. Notably, assays of clinical immune cells confirmed an enhanced “Trojan Horse” in HIV/AIDS patients, which can be shut down by cytoskeleton inhibitors. Furthermore, we identified a novel enhancer for macrophage “Trojan Horse”, myocilin, and an enhanced fungal burden was achieved in brains of MYOC transgenic mice. Taking together, this study reveals fundamental roles of cytoskeleton and MYOC in blocking fungal CNS dissemination, which not only helps to understand the high prevalence of cryptococcal meningitis in HIV/AIDS, but also facilitates the development of novel drugs for therapies of meningoencephalitis caused by C. neoformans and other pathogenic microorganisms.
Subject(s)
Cryptococcosis , HIV Infections , Meningoencephalitis , Acquired Immunodeficiency Syndrome , COVID-19 , Meningitis, CryptococcalABSTRACT
As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here we leverage our expertise in computational antibody engineering to rationally design/optimize three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R, and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS. Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody induced virus activation or enhancement, we applied NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life.
Subject(s)
COVID-19ABSTRACT
The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods.
Subject(s)
COVID-19 , Learning DisabilitiesABSTRACT
Since December 2019, a number of patients with novel coronavirus pneumonia (NCP) have been identified in Wuhan, Hubei Province, China. NCP has rapidly spread to other provinces and cities in China and other countries in the world. Due to the rapid increase in reported cases in China and around the world, on January 30, 2020, the World Health Organization (WHO) Emergency Committee announced that NCP is a Public Health Emergency of International Concern (PHEIC). However, there are relatively few suggestions and measures for tumor patients, especially patients with head and neck tumors. This article summarizes the prevention and control of disease in our medical institution to provide a reference for front-line head and neck surgeons.