Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States

The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest–infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100m to 4km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer distances associated with lowest infection rate are within 1,200m for forest outside park and within 600m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.

Dual functionalisation of polyurethane foam for unprecedented flame retardancy and antibacterial properties using layer-by-layer assembly of MXene chitosan with antibacterial metal particles

Antibacterial surfaces in healthcare settings are an important tool for combating the increasing threat of antibacterial drug resistance, which the global Covid-19 pandemic has further exacerbated. Herein, we report a new method to achieve dual antibacterial and flame retardant functionalities in flexible polyurethane foam (PUF) by synthesising a multifunctional coating using a layer-by-layer assembly technique. The coating consists of Ti3C2 nanosheets and chitosan as the flame retardant and metal particles (copper or silver) for the antibacterial property. Results show that the multilayer Ti3C2/CH/Ag coating possesses excellent antibacterial performance with reductions of 99.97% in gram-negative bacteria (P. aeruginosa) and 88.9% in gram-positive bacteria (S. aureus) compared with the unmodified counterpart. Compared with the pristine PUF, the multifunctional coating yielded 66.3% reductions in the PHRR, and demonstrated outstanding smoke suppression performance with a PSPR reduction of 51.6% and a TSR decline of 65.5%. Moreover, Raman spectroscopy revealed an increased graphitisation level in the residual char of the coated foam, indicating the coating's remarkable charring performance. This exceptional multifunctional performance endows the coating technology with a great potential for eradicating the fire risks of antibacterial surfaces in healthcare settings and providing furniture, interior walls and building panels with antibacterial properties.

Drug Components-Disease Network Related to Acute Lung Injury Inference Based on Forest Graph-embedded Deep Feedforward Network (preprint)

Background: Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. Results: In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train and identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS).Conclusions: The experiment results show that forgeNet performs better than support vector machines (SVM), random forest (RF) and gcForest.

Research on learning initiative Based on behavior quantization and potential value clustering

As an important learning mode, e-learning has become an important way of online learning, especially during the coronavirus pandemic. However, the separation of teaching and learning in time or space undoubtedly makes it difficult for teachers to master and supervise students' learning initiative. From the perspective of inquiry learning initiative, a learning problem model is constructed. To solve the problem of model timeliness, the learning behavior characteristics have been quantified with the guidance of time parameter. Furthermore, a cluster analysis method is proposed to group the students into different clusters, and a case study is given to verify the proposed scheme. The experimentalresults show that the proposed method can effectively identify the type of students and the degree of learning engagement.

A proof of concept for neutralizing antibody-guided vaccine design against SARS-CoV-2.

Mutations and transient conformational movements of the receptor binding domain (RBD) that make neutralizing epitopes momentarily unavailable present immune escape routes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To mitigate viral escape, we developed a cocktail of neutralizing antibodies (NAbs) targeting epitopes located on different domains of spike (S) protein. Screening of a library of monoclonal antibodies generated from peripheral blood mononuclear cells of COVID-19 convalescent patients yielded potent NAbs, targeting the N-terminal domain (NTD) and RBD domain of S, effective at nM concentrations. Remarkably, a combination of RBD-targeting NAbs and NTD-binding NAbs, FC05, enhanced the neutralization potency in cell-based assays and an animal model. Results of competitive surface plasmon resonance assays and cryo-electron microscopy (cryo-EM) structures of antigen-binding fragments bound to S unveil determinants of immunogenicity. Combinations of immunogens, identified in the NTD and RBD of S, when immunized in rabbits and macaques, elicited potent protective immune responses against SARS-CoV-2. More importantly, two immunizations of this combination of NTD and RBD immunogens provided complete protection in macaques against a SARS-CoV-2 challenge, without observable antibody-dependent enhancement of infection. These results provide a proof of concept for neutralization-based immunogen design targeting SARS-CoV-2 NTD and RBD.

Biomimetic Design and Mass Production of Sustainable Multiscale Cellulose Fibers‐Based Hierarchical Filter Materials for Protective Clothing

As a core part of personal protective equipment (PPE), filter materials play a key role in individual protection, especially in the fight against the COVID‐19. Here, a high‐performance multiscale cellulose fibers‐based filter material is introduced for protective clothing, which overcomes the limitation of mutual exclusion of filtration and permeability in cellulose‐based filter materials. With the hierarchical biomimetic structure design and the active surface of multiscale cellulose fibers, high PM2.5 removal efficiency of ≈92% is achieved with the high moisture transmission rate of 8 kg m−2 d−1. Through a simple and effective dip‐coating and roll‐to‐roll process, the hierarchical filter materials can be made on a large scale and further fabricated into high‐quality protective clothing by industrial production equipment. [ABSTRACT FROM AUTHOR] Copyright of Advanced Materials Technologies is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The divergence between SARS-CoV-2 and RaTG13 might be overestimated due to the extensive RNA modification

Aim: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread throughout the world. There is urgent need to understand the phylogeny, divergence and origin of SARS-CoV-2. Materials & methods: A recent study claimed that there was 17% divergence between SARS-CoV-2 and RaTG13 (a SARS-related coronaviruses) on synonymous sites by using sequence alignment. We re-analyzed the sequences of the two coronaviruses with the same methodology. Results: We found that 87% of the synonymous substitutions between the two coronaviruses could be potentially explained by the RNA modification system in hosts, with 65% contributed by deamination on cytidines (C-T mismatches) and 22% contributed by deamination on adenosines (A-G mismatches). Conclusion: Our results demonstrate that the divergence between SARS-CoV-2 and RaTG13 has been overestimated.

Clinical characteristics of 134 convalescent patients with COVID-19 in Guizhou, China (preprint)

Background: Previous studies have focused on the clinical characteristics of hospitalized patients with the novel 2019 coronavirus disease (COVID-19). Limited data are available for convalescent patients. This study aimed to evaluate the clinical characteristics of discharged COVID-19 patients. Methods: : In this retrospective study, we extracted data for 134 convalescent patients with COVID-19 in Guizhou Provincial Staff Hospital from February 15 to March 31, 2020. Cases were analyzed on the basis of demographic, clinical, and laboratory data as well as radiological features. Results: : Of 134 convalescent patients with COVID-19, 19 (14.2%) were severe cases, while 115 (85.8%) were non-severe cases. The median patient age was 33 years (IQR, 21.8 to 46.3), and the cohort included 69 men and 65 women. Compared with non-severe cases, severe patients were older and had more chronic comorbidities, especially hypertension, diabetes, and thyroid disease (P<0.05). Leukopenia was present in 32.1% of the convalescent patients and lymphocytopenia was present in 6.7%, both of which were more common in severe patients. 48 (35.8%) of discharged patients had elevated levels of alanine aminotransferase, which was more common in adults than in children (40.2% vs 13.6%, P=0.018). A normal chest CT was found in 61 (45.5%) patients during rehabilitation. Severe patients had more ground-glass opacity, bilateral patchy shadowing, and fibrosis. No significant differences were observed in the positive rate of IgG and/or IgM antibodies between severe and non-severe patients. Conclusion: Leukopenia, lymphopenia, ground-glass opacity, and fibrosis are common in discharged severe COVID-19 patients, and liver injury is common in discharged adult patients. We suggest physicians develop follow-up treatment plans based on the different clinical characteristics of convalescent patients.

Development and preliminary validation of questionnaire for infection process and prevention of 2019 novel coronavirus infection in medical staffs

Objective To develop the questionnaire and test its reliability for investigating route, prevention, and control of SARS-CoV-2 infection in medical staffs. Methods This questionnaire was development based on the COVID-19 relevant guidelines, official documents issued by the National Health Committee of the People's Republic of China, and published studies. The development group performed repeated discussions and drafted the first questionnaire, then performed expert consultation and revised the draft according to their suggestions. Eventually, some frontline medical staffs were invited to carry out pre-test investigation of the questionnaire and test its reliability. Results The first draft included 48 items;18 experts were invited in the first round questionnaire and 10 experts in the second round questionnaire. The positive coefficient of experts in these two rounds was both greater than 75%, and the authority coefficient of experts' opinions was greater than 0.70. The variation coefficient of these items was between 0.00 and 0.35, the coordination coefficient of experts was 0.193 (P<0.05). The experts of above two rounds put forward 14 suggestions for text modification or adjustment options of some items;after the development group held repeatedly discussions, a total of 8 items were performed secondary consultation and finally reached consensus. The final questionnaire included two domains of questionnaire before and after confirmed diagnosis. The domain "before confirmed diagnosis" covered 4 sections and 29 items involving infectious cause, plan and knowledge of prevention and control, and psychological symptoms. The domain "after confirmed diagnosis" covered 5 sections and 21 items, included symptoms, treatment, and psychological status after diagnosis;impact on the surrounding environment and people, and awareness of protection after infection. The pre-test results showed that the total items were considerably numerous, some items were difficult to understand, some laboratory results and treatment conditions were ambiguous, etc. After modification and re-testing, the test-re-test reliability of each domain was between 0.74 and 0.93, and the overall re-test reliability of the questionnaire content was 0.82. Conclusions This research has developed a questionnaire for investigating infection process, prevention and control of SARS-CoV-2 infection in medical staff, and the items considered two domains prior to and after confirmed diagnosis. The reliability and practicability of the questionnaire are acceptable.

The Trans-omics Landscape of COVID-19 (preprint)

The outbreak of coronavirus disease 2019 (COVID-19) has been causing a global health emergency. Although previous studies investigated COVID-19 at different omics levels, the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here, we presented a trans-omics landscape for COVID-19 based on integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles from blood samples of 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Notably, we found neutrophils heterogeneity existed between asymptomatic and critically ill patients. Expression discordance of inflammatory cytokines at mRNA and protein levels in asymptomatic patients could possibly be explained by post-transcriptional regulation by RNA binding proteins (RBPs) and microRNAs. Neutrophils over-activation, induced arginine depletion, and tryptophan metabolites accumulation contributed to T/NK cell dysfunction in critical patients. Anti-virus interferons were gradually suppressed along with disease severity. Overall, our study systematically revealed multi-omics characteristics of COVID-19, and the data we generated could hopefully help illuminate COVID-19 pathogenesis and provide valuable clues about potential therapeutic strategies for COVID-19.

The Trans-omics Landscape of COVID-19 (preprint)

System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment.

Epidemiologic and Clinical Characteristics of 91 Hospitalized Patients with COVID-19 in Zhejiang, China: A retrospective, multi-centre case series (preprint)

Background Recent studies have focused initial clinical and Epidemiologic characteristics on the COVID-19, mainly revealing situation in Wuhan, Hubei. Aim To reveal more data on the epidemiologic and clinical characteristics of COVID-19 patients outside of Wuhan, in Zhejiang, China. Design Retrospective case series. Methods 88 cases of laboratory-confirmed and 3 cases of clinical-confirmed COVID-19 were admitted to five hospitals in Zhejiang province, China. Data were collected from 20 January 2020 to 11 February 2020. Results Of all 91 patients, 88 (96.70%) were laboratory-confirmed COVID-19 with throat swab samples that tested positive for SARS-Cov-2 while 3 (3.30%) were clinical-diagnosed COVID-19 cases. The median age of the patients was 50 (36.5-57) years, and female accounted for 59.34%. In this sample 40 (43.96%) patients had contracted the diseases from local cases, 31 (34.07%) patients had been to Wuhan/Hubei, 8 (8.79%) cases had contacted with people from Wuhan, 11 (12.09%) cases were confirmed aircraft transmission. In particular within the city of Ningbo, 60.52% cases can be traced back to an event held in a temple. The most common symptoms were fever (71.43%), cough (60.44%) and fatigue (43.96%). The median of incubation period was 6 (IQR, 3-8) days and the median time from first visit to a doctor to confirmed diagnosis was 1 (1-2) days. According to the Chest computed tomography scans, 67.03% cases had bilateral pneumonia. Conclusions Social activity cluster, family cluster and travel by airplane were how COVID-19 patients get transmitted and could be rapidly diagnosed COVID-19 in Zhejiang.