RNA G-quadruplex in TMPRSS2 reduces SARS-CoV-2 infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to have devastating consequences worldwide. Recently, great efforts have been made to identify SARS-CoV-2 host factors, but the regulatory mechanisms of these host molecules, as well as the virus per se, remain elusive. Here we report a role of RNA G-quadruplex (RG4) in SARS-CoV-2 infection. Combining bioinformatics, biochemical and biophysical assays, we demonstrate the presence of RG4s in both SARS-CoV-2 genome and host factors. The biological and pathological importance of these RG4s is then exemplified by a canonical 3-quartet RG4 within Tmprss2, which can inhibit Tmprss2 translation and prevent SARS-CoV-2 entry. Intriguingly, G-quadruplex (G4)-specific stabilizers attenuate SARS-CoV-2 infection in pseudovirus cell systems and mouse models. Consistently, the protein level of TMPRSS2 is increased in lungs of COVID-19 patients. Our findings reveal a previously unknown mechanism underlying SARS-CoV-2 infection and suggest RG4 as a potential target for COVID-19 prevention and treatment.

Nucleic acid detection results analysis in different COVID-19 samples

Objective: To understand the results and influencing factors of SARS-CoV-2 nucleic acid detection among specimens in different types and stages of coronavirus disease 2019 (COVID-19) progression.

Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information.

The outbreak of COVID-19 around the world has caused great pressure to the health care system, and many efforts have been devoted to artificial intelligence (AI)-based analysis of CT and chest X-ray images to help alleviate the shortage of radiologists and improve the diagnosis efficiency. However, only a few works focus on AI-based lung ultrasound (LUS) analysis in spite of its significant role in COVID-19. In this work, we aim to propose a novel method for severity assessment of COVID-19 patients from LUS and clinical information. Great challenges exist regarding the heterogeneous data, multi-modality information, and highly nonlinear mapping. To overcome these challenges, we first propose a dual-level supervised multiple instance learning module (DSA-MIL) to effectively combine the zone-level representations into patient-level representations. Then a novel modality alignment contrastive learning module (MA-CLR) is presented to combine representations of the two modalities, LUS and clinical information, by matching the two spaces while keeping the discriminative features. To train the nonlinear mapping, a staged representation transfer (SRT) strategy is introduced to maximumly leverage the semantic and discriminative information from the training data. We trained the model with LUS data of 233 patients, and validated it with 80 patients. Our method can effectively combine the two modalities and achieve accuracy of 75.0% for 4-level patient severity assessment, and 87.5% for the binary severe/non-severe identification. Besides, our method also provides interpretation of the severity assessment by grading each of the lung zone (with accuracy of 85.28%) and identifying the pathological patterns of each lung zone. Our method has a great potential in real clinical practice for COVID-19 patients, especially for pregnant women and children, in aspects of progress monitoring, prognosis stratification, and patient management.

Prognostic value of bedside lung ultrasound score in patients with COVID-19.

BACKGROUND: Bedside lung ultrasound (LUS) has emerged as a useful and non-invasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019 (COVID-19). However, the clinical significance of the LUS score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of the LUS score in patients with COVID-19. METHOD: The LUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. The LUS score based on B-lines, lung consolidation and pleural line abnormalities was evaluated. RESULTS: The median time from admission to LUS examinations was 7 days (interquartile range [IQR] 3-10). Patients in the highest LUS score group were more likely to have a lower lymphocyte percentage (LYM%); higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain; more invasive mechanical ventilation therapy; higher incidence of ARDS; and higher mortality than patients in the lowest LUS score group. After a median follow-up of 14 days [IQR, 10-20 days], 37 patients developed ARDS, and 13 died. Patients with adverse outcomes presented a higher rate of bilateral involvement; more involved zones and B-lines, pleural line abnormalities and consolidation; and a higher LUS score than event-free survivors. The Cox models adding the LUS score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI] 1.02 ~ 1.08; P < 0.001; Akaike information criterion [AIC] = 272; C-index = 0.903) or as a categorical variable (HR 10.76, 95% CI 2.75 ~ 42.05; P = 0.001; AIC = 272; C-index = 0.902) were found to predict poor outcomes more accurately than the basic model (AIC = 286; C-index = 0.866). An LUS score cut-off > 12 predicted adverse outcomes with a specificity and sensitivity of 90.5% and 91.9%, respectively. CONCLUSIONS: The LUS score devised by our group performs well at predicting adverse outcomes in patients with COVID-19 and is important for risk stratification in COVID-19 patients.

COVID-19: Challenges to GIS with Big Data

The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

Nurses' experiences regarding shift patterns in isolation wards during the COVID-19 pandemic in China: A qualitative study.

AIM AND OBJECTIVE: To explore nurses' experiences regarding shift patterns while providing front-line care for COVID-19 patients in isolation wards of hospitals in Shanghai and Wuhan during the novel coronavirus pandemic. Our findings will help to optimise shift work scheduling, use the existing nursing workforce more efficiently and improve nursing quality. BACKGROUND: Nurses are one of the main professionals fighting against COVID-19. Providing care for COVID-19 patients is challenging. In isolation wards, the workload has increased, and the workflow and shift patterns are completely different from the usual. More importantly, there is a shortage of nurses. Therefore, it is essential and urgent to arrange nurses' shifts correctly and use the existing workforce resources efficiently. DESIGN: A qualitative descriptive study of 14 nurses in Chinese hospitals was conducted. METHODS: Semi-structured interviews were used based on the phenomenological research method; data were analysed using Colaizzi's method of data analysis. This study aligns with the COREQ checklist. RESULTS: Four themes were extracted: assess the competency of nurses to assign nursing work scientifically and reasonably, reorganise nursing workflow to optimise shift patterns, communicate between managers and front-line nurses to humanise shift patterns, and nurses' various feelings and views on shift patterns. CONCLUSION: It is necessary to arrange shift patterns scientifically and allocate workforce rationally to optimise nursing workforce allocation, reduce nurses' workload, improve nursing quality and promote physical and mental health among nurses during the COVID-19 pandemic. RELEVANCE TO CLINICAL PRACTICE: This study emphasised nurses' experiences on shift patterns in isolation wards, providing useful information to manage shift patterns. Nursing managers should arrange shifts scientifically, allocate nursing workforce rationally, formulate emergency plans and establish emergency response rosters during the COVID-19 pandemic.