ABSTRACT
The outbreak of corona virus disease 2019 (COVID-19) has aroused great attention around the world. SARS-CoV-2 possesses characteristics of faster transmission, immune escape, and occult transmission by many mutation, which caused still grim situation of prevention and control. Early detection and isolation of patients are still the most effective measures at present. So, there is an urgent need for new rapid and highly sensitive testing tools to quickly identify infected patients as soon as possible. This review briefly introduces general characteristics of SARS-CoV-2, and provides recentl overview and analysis based on different detection methods for nucleic acids, antibodies, antigens as detection target. Novel nano-biosensors for SARS-CoV-2 detection are analyzed based on optics, electricity, magnetism, and visualization. In view of the advantages of nanotechnology in improving detection sensitivity, specificity and accuracy, the research progress of new nano-biosensors is introduced in detail, including SERS-based biosensors, electrochemical biosensors, magnetic nano-biosensors and colorimetric biosensors. Functions and challenges of nano-materials in construction of new nano-biosensors are discussed, which provides ideas for the development of various coronavirus biosensing technologies for nanomaterial researchers.
ABSTRACT
The outbreak of corona virus disease 2019 (COVID-19) has aroused great attention around the world. SARS-CoV-2 possesses characteristics of faster transmission, immune escape, and occult transmission by many mutation, which caused still grim situation of prevention and control. Early detection and isolation of patients are still the most effective measures at present. So, there is an urgent need for new rapid and highly sensitive testing tools to quickly identify infected patients as soon as possible. This review briefly introduces general characteristics of SARS-CoV-2, and provides recentl overview and analysis based on different detection methods for nucleic acids, antibodies, antigens as detection target. Novel nano-biosensors for SARS-CoV-2 detection are analyzed based on optics, electricity, magnetism, and visualization. In view of the advantages of nanotechnology in improving detection sensitivity, specificity and accuracy, the research progress of new nano-biosensors is introduced in detail, including SERS-based biosensors, electrochemical biosensors, magnetic nano-biosensors and colorimetric biosensors. Functions and challenges of nano-materials in construction of new nano-biosensors are discussed, which provides ideas for the development of various coronavirus biosensing technologies for nanomaterial researchers.
ABSTRACT
In this paper, a reaction-diffusion SIRE epidemic model in contaminated environments is proposed, in which the effect of protection for susceptible individuals is included by the nonlinear incidence functions b(S)E$b(S)E$ and g(S)I$g(S)I$. When the space is heterogeneous, the basic reproduction number R0$\mathcal {R}_{0}$ is derived, by which we find that if R0 <= 1$\mathcal {R}_{0}\le 1$, the disease-free steady state is globally asymptotically stable, while R0>1$\mathcal {R}_{0}>1$, the disease is uniform persistent. Furthermore, when R0>1$\mathcal {R}_{0}>1$ and additional conditions hold, the global asymptotic stability of special endemic steady state is obtained in homogeneous space. Finally, the theoretical results are validated by numerical simulations, some open questions are illustrated.
ABSTRACT
In the version of this article initially published, the first name of Chuansheng Zheng was misspelled as Chuangsheng. The error has been corrected in the HTML and PDF versions of the article. © The Author(s) 2022.
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.