ABSTRACT
The COVID-19 outbreak has seen the widespread use of personal protective equipment, especially antibacterial fibers. In this work, ionic liquid (IL) is used as a solvent to fabricate antibacterial fibers combining plant essential oils (PEOs) with cellulose. PEOs are buried in microcapsules first or mixed directly with IL-cellulose spinning dopes to prepare a series of composite fibers. The internal structures, surface and cross morphologies, thermal stability, mechanical properties, antibacterial activity, washing stability, and biocompatibility of these fibers are investigated and analyzed in-depth further. Artemisia microcapsule fiber (AMCRCF) with a break strength of 30.07 MPa is obtained. Besides, the antibacterial activity rates of AMC-RCF against Escherichia coli and Staphylococcus aureus are 89.8 and 97.8%, and the fibers still have a long-lasting antibacterial effect after 30 standard washes. Furthermore, the antibacterial fibers exhibit excellent biocompatibility. This research provides a green approach for the fabrication of the antibacterial fibers with long-lasting antibacterial activity and good biocompatibility.
ABSTRACT
Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses;however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health. The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.
ABSTRACT
COVID-19 spreads with strong infectivity and triggered a public health crisis, home and abroad. SARS-CoV-2 has high pathogenic homology with SARS-CoV and MERS-CoV, and the three coronaviruses all belong to the Betacoronavirus family. Due to pregnant women's physical and psychological vulnerability, they are the susceptible and high-risk groups during the epidemic. This article will review the reports on adverse effects of maternal and fetal health during the SARS and MERS and COVID-19 epidemics to provide evidence for the clinical management and prevention and control of pregnant cases in SARS-CoV-2 infection.