An AIEgen/graphene oxide nanocomposite (AIEgen@GO)‐based two‐stage “turn‐on” nucleic acid biosensor for rapid detection of SARS‐CoV‐2 viral sequence

The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) pandemic has posed significant challenges in early viral diagnosis. Hence, it is urgently desirable to develop a rapid, inexpensive, and sensitive method to aid point‐of‐care SARS‐CoV‐2 detection. In this work, we report a highly sequence‐specific biosensor based on nanocomposites with aggregation‐induced emission luminogens (AIEgen)‐labeled oligonucleotide probes on graphene oxide nanosheets (AIEgen@GO) for one step‐detection of SARS‐CoV‐2‐specific nucleic acid sequences (Orf1ab or N genes). A dual “turn‐on” mechanism based on AIEgen@GO was established for viral nucleic acids detection. Here, the first‐stage fluorescence recovery was due to dissociation of the AIEgen from GO surface in the presence of target viral nucleic acid, and the second‐stage enhancement of AIE‐based fluorescent signal was due to the formation of a nucleic acid duplex to restrict the intramolecular rotation of the AIEgen. Furthermore, the feasibility of our platform for diagnostic application was demonstrated by detecting SARS‐CoV‐2 virus plasmids containing both Orf1ab and N genes with rapid detection around 1 h and good sensitivity at pM level without amplification. Our platform shows great promise in assisting the initial rapid detection of the SARS‐CoV‐2 nucleic acid sequence before utilizing quantitative reverse transcription‐polymerase chain reaction for second confirmation. An AIEgen‐graphene oxide (GO) nanocomposite‐based assay is designed for rapid detection of SARS‐CoV‐2 nucleic acids. The sensing mechanism is based on two‐stage fluorescence signal recovery due to fluorescence resonance energy transfer (FRET) effect by detaching AIEgen from GO surface and restricted intramolecular rotation (RIR) effect by formation of nucleic acid duplexes.

Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study.

BACKGROUND: In a fast-evolving public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi-steady-state phase of the old information. OBJECTIVE: This study aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users. METHODS: We developed and analyzed two classes of susceptible-forwarding-immune information propagation models with delay in transmission to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information were posted frequently by each opinion leader related to COVID-19 with high influence (data acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of a publishing strategy were analyzed. RESULTS: The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation. CONCLUSIONS: Our model can carry out an accurate numerical simulation of information at different release intervals and achieve a dynamic evaluation of information transmission by constructing an indicator system so as to provide theoretical support and strategic suggestions for government decision making. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management.

The role of proactive behavior on COVID-19 infordemic in the Chinese Sina-Microblog: a modeling study.

In order to avoid forming an information cocoon, the information propagation of COVID-19 is usually created through the action of "proactive search", an important behavior other than "reactive follow". This behavior has been largely ignored in modeling information dynamics. Here, we propose to fill in this gap by proposing a proactive-reactive susceptible-discussing-immune (PR-SFI) model to describe the patterns of co-propagation on social networks. This model is based on the forwarding quantity and takes into account both proactive search and reactive follow behaviors. The PR-SFI model is parameterized by data fitting using real data of COVID-19 related topics in the Chinese Sina-Microblog, and the model is calibrated and validated using the prediction accuracy of the accumulated forwarding users. Our sensitivity analysis and numerical experiments provide insights about optimal strategies for public health emergency information dissemination.

COVID-19 information contact and participation analysis and dynamic prediction in the Chinese Sina-microblog.

The outbreak of a novel coronavirus (COVID-19) aroused great public opinion in the Chinese Sina-microblog. To help in designing effective communication strategies during a major public health emergency, we analyze the real data of COVID-19 information and propose a comprehensive susceptible-reading-forwarding-immune (SRFI) model to understand the patterns of key information propagation considering both public contact and participation. We develop the SRFI model, based on the public reading quantity and forwarding quantity that denote contact and participation respectively, and take into account the behavior that users may re-enter another related topic during the attention phase or the participation phase freely. Data fitting using the real data of both reading quantity and forwarding quantity obtained from Chinese Sina-microblog can parameterize the model to make an accurate prediction of the COVID-19 public opinion trend until the next major news item occurs, and the sensitivity analysis provides the basic strategies for communication.