The international science and technology collaboration during the prevention and control of the infectious diseases

In a world of increased economic and cultural exchanges, the emerging infectious diseases (EIDs) are characterized by the global spread, with a significant impact on the world economy and the public health. The international science and technology collaboration on preventing the infectious diseases, and dealing with the challenge of the global public health emergency, is an urgent task. The mechanism of the international science and technology collaboration (ISTC) plays an important role in the prevention and the control of the EIDs. Based on the studies of the recent outbreak of the newest EID, the novel coronavirus pneumonia, the roles of the ISTC during the EIDs prevention and control are analyzed in this paper. It is shown that the EID prevention and control involves several aspects of the ISTC, including the timely exchange of the latest epidemic information, the quick-response joint research, the sharing of antiviral drugs and medical technology, the personnel training and the construction and the improvement of the EID prevention and control system. Finally, this paper puts forward a few policy recommendations with respect to the EID prevention and control from the perspective of the global governance and the ISTC.

CANPT Score: A Tool to Predict Severe COVID-19 on Admission.

Background and Aims: Patients with critical coronavirus disease 2019 (COVID-19) have a mortality rate higher than 50%. The purpose of this study was to establish a model for the prediction of the risk of severe disease and/or death in patients with COVID-19 on admission. Materials and Methods: Patients diagnosed with COVID-19 in four hospitals in China from January 22, 2020 to April 15, 2020 were retrospectively enrolled. The demographic, laboratory, and clinical data of the patients with COVID-19 were collected. The independent risk factors related to the severity of and death due to COVID-19 were identified with a multivariate logistic regression; a nomogram and prediction model were established. The area under the receiver operating characteristic curve (AUROC) and predictive accuracy were used to evaluate the model's effectiveness. Results: In total, 582 patients with COVID-19, including 116 patients with severe disease, were enrolled. Their comorbidities, body temperature, neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, and levels of total bilirubin (Tbil), creatinine (Cr), creatine kinase (CK), and albumin (Alb) were independent risk factors for severe disease. A nomogram was generated based on these eight variables with a predictive accuracy of 85.9% and an AUROC of 0.858 (95% CI, 0.823-0.893). Based on the nomogram, the CANPT score was established with cut-off values of 12 and 16. The percentages of patients with severe disease in the groups with CANPT scores <12, ≥12, and <16, and ≥16 were 4.15, 27.43, and 69.64%, respectively. Seventeen patients died. NLR, Cr, CK, and Alb were independent risk factors for mortality, and the CAN score was established to predict mortality. With a cut-off value of 15, the predictive accuracy was 97.4%, and the AUROC was 0.903 (95% CI 0.832, 0.974). Conclusions: The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.

Effect of low-dose aspirin on mortality and viral duration of the hospitalized adults with COVID-19.

ABSTRACT: To clarify the effect of aspirin on mortality and viral duration in adults infected with respiratory syndrome coronavirus 2 (SARS-Cov-2).After propensity score-matched (PSM) case-control analyses 24 pairs of patients were enrolled and followed up for 2 months. Both 30-day and 60-day mortality in the aspirin group were significantly lower than that in the non-aspirin group (P = .021 and P = .030, respectively). The viral duration time between the 2 groups was not significantly different (P = .942).Among adults (with hypertension, cardiovascular diseases) infected with SARS-Cov-2, low-dose aspirin medication (100 mg/day) was associated with lower risk of mortality compared with non-aspirin users.

In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. By using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep-learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.

RASP: an atlas of transcriptome-wide RNA secondary structure probing data.

RNA molecules fold into complex structures that are important across many biological processes. Recent technological developments have enabled transcriptome-wide probing of RNA secondary structure using nucleases and chemical modifiers. These approaches have been widely applied to capture RNA secondary structure in many studies, but gathering and presenting such data from very different technologies in a comprehensive and accessible way has been challenging. Existing RNA structure probing databases usually focus on low-throughput or very specific datasets. Here, we present a comprehensive RNA structure probing database called RASP (RNA Atlas of Structure Probing) by collecting 161 deduplicated transcriptome-wide RNA secondary structure probing datasets from 38 papers. RASP covers 18 species across animals, plants, bacteria, fungi, and also viruses, and categorizes 18 experimental methods including DMS-seq, SHAPE-Seq, SHAPE-MaP, and icSHAPE, etc. Specially, RASP curates the up-to-date datasets of several RNA secondary structure probing studies for the RNA genome of SARS-CoV-2, the RNA virus that caused the on-going COVID-19 pandemic. RASP also provides a user-friendly interface to query, browse, and visualize RNA structure profiles, offering a shortcut to accessing RNA secondary structures grounded in experimental data. The database is freely available at http://rasp.zhanglab.net.

COVID-19 in two infants in China.

INTRODUCTION: Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly around the world and caused more than 487 000 infections and 22 000 deaths worldwide. METHODS: We report two infant cases with coronavirus disease 2019 (COVID-19) in Yichang, Hubei, China. The younger of the two is only 5-months old. We recorded their clinical manifestations, epidemiological history, laboratory examination, and treatment in detail. In addition, we provide computed tomographic images of their chest, which are the most serious imaging manifestation among the infants recorded so far. RESULTS: Although both of them eventually recovered and were discharged from the hospital, they were complicated with varying degrees of liver and myocardial injury. In addition, one of them was complicated with mycoplasma pneumoniae infection. CONCLUSIONS: Pediatricians should consider the potential risks of developing severe illness of infants infected by SARS-CoV-2 and take them seriously.