High-Throughput SARS-CoV-2 Antiviral Testing Method Using the Celigo Image Cytometer.

The COVID-19 pandemic has created a worldwide public health crisis that has since resulted in 6.8 million reported deaths. The pandemic prompted the immediate response of researchers around the world to engage in rapid vaccine development, surveillance programs, and antiviral testing, which resulted in the delivery of multiple vaccines and repurposed antiviral drug candidates. However, the emergence of new highly transmissible SARS-CoV-2 variants has renewed the desire for discovering new antiviral drug candidates with high efficacy against the emerging variants of concern. Traditional antiviral testing methods employ the plaque-reduction neutralization tests (PRNTs), plaque assays, or RT-PCR analysis, but each assay can be tedious and time-consuming, requiring 2-3 days to complete the initial antiviral assay in biologically relevant cells, and then 3-4 days to visualize and count plaques in Vero cells, or to complete cell extractions and PCR analysis. In recent years, plate-based image cytometers have demonstrated high-throughput vaccine screening methods, which can be adopted for screening potential antiviral drug candidates. In this work, we developed a high-throughput antiviral testing method employing the Celigo Image Cytometer to investigate the efficacy of antiviral drug candidates on SARS-CoV-2 infectivity using a fluorescent reporter virus and their safety by measuring the cytotoxicity effects on the healthy host cell line using fluorescent viability stains. Compared to traditional methods, the assays defined here eliminated on average 3-4 days from our standard processing time for antiviral testing. Moreover, we were able to utilize human cell lines directly that are not typically amenable to PRNT or plaque assays. The Celigo Image Cytometer can provide an efficient and robust method to rapidly identify potential antiviral drugs to effectively combat the rapidly spreading SARS-CoV-2 virus and its variants during the pandemic.

Comparison of the prophylactic antithrombotic effect of indobufen and warfarin in patients with nephrotic syndrome: a randomized controlled trial.

PURPOSE: The risk of thromboembolic events is elevated in patients with nephrotic syndrome, and warfarin use has been associated with an increased risk of bleeding. Indobufen, a selective cyclooxygenase-1 inhibitor, is currently being evaluated for the prevention of thromboembolic events in nephrotic syndrome. This study aimed to compare the efficacy and safety of indobufen with that of warfarin in patients with nephrotic syndrome. MATERIALS AND METHODS: This multicenter, randomized, three-arm, open-label, parallel controlled trial involved a total of 180 adult patients with nephrotic syndrome from four centers in China. Patients were randomly assigned to receive 100 mg indobufen (bid), 200 mg indobufen (bid), and 3 mg warfarin (qd) daily for 12 weeks. The primary endpoints included thromboembolic and bleeding events, while laboratory results and adverse events constituted secondary endpoints. RESULTS: No thromboembolic events occurred in the high-/low-dose indobufen and warfarin groups. Moreover, the use of a low dose of indobufen significantly reduced the risk of minor bleeding events compared with warfarin use (2% versus 18%, p < .05). Finally, adverse events were more frequent in warfarin-treated patients. CONCLUSIONS: This study found that indobufen therapy provided equivalent effects in preventing thromboembolic events compared with warfarin therapy, while low dose of indobufen was associated with a reduced risk of bleeding events, thus it should be recommended for the prevention of thromboembolic events in clinical practice in patients with nephrotic syndrome. TRIAL REGISTRATION NUMBER: ChiCTR-IPR-17013428.

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.

Teaching Evolution: The Use of Social Networking Sites

Online schooling has been adopted worldwide due to the COVID-19 pandemic. During quarantine, people go online for all kind of purposes, especially for amusement such as via social networking sites (SNSs). This study examined university physical education (PE) students’SNSs usage intention using the Unified Theory of Acceptance and Use of Technology model II (UTAUT2) in Taiwan. Research respondents were selected from PE departments of 19 universities through purposive sampling method. A total of 707 questionnaires were collected, with a returning rate of 93%. Using Warp PLS 7.0 as the main instrument for data analysis, this research finds that performance expectancy, facilitating conditions, hedonic motivation, price value, and habit within the UTAUT2 model have significant positive effects on students’intention to use social networking sites, and the model explains 63.4% of the variance in their intention to use SNSs. Among those variables, hedonic motivation had the highest impact (β= 0.24). Moreover, intention, facilitating conditions and habit have significant positive effects on students’use of social networking sites, and the model explains 13.4% of the variance in their use of social networking sites. The moderating effects of gender, age and experience are found in some path analyses. These findings provide future university instructors a with better understanding of students using SNSs. We thus recommend for university PE instructors to create interesting and pleasant classroom learning experiences to attract students’attention, and recommend that they may even manage a SNS as an aid for teaching to enhance students’interests in learning.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

A high-throughput screen for TMPRSS2 expression identifies FDA-approved compounds that can limit SARS-CoV-2 entry.

SARS-CoV-2 (2019-nCoV) is the pathogenic coronavirus responsible for the global pandemic of COVID-19 disease. The Spike (S) protein of SARS-CoV-2 attaches to host lung epithelial cells through the cell surface receptor ACE2, a process dependent on host proteases including TMPRSS2. Here, we identify small molecules that reduce surface expression of TMPRSS2 using a library of 2,560 FDA-approved or current clinical trial compounds. We identify homoharringtonine and halofuginone as the most attractive agents, reducing endogenous TMPRSS2 expression at sub-micromolar concentrations. These effects appear to be mediated by a drug-induced alteration in TMPRSS2 protein stability. We further demonstrate that halofuginone modulates TMPRSS2 levels through proteasomal-mediated degradation that involves the E3 ubiquitin ligase component DDB1- and CUL4-associated factor 1 (DCAF1). Finally, cells exposed to homoharringtonine and halofuginone, at concentrations of drug known to be achievable in human plasma, demonstrate marked resistance to SARS-CoV-2 infection in both live and pseudoviral in vitro models. Given the safety and pharmacokinetic data already available for the compounds identified in our screen, these results should help expedite the rational design of human clinical trials designed to combat active COVID-19 infection.

Ephrin-A1 and the sheddase ADAM12 are upregulated in COVID-19.

More than 3.5 million people have died globally from COVID-19, yet an effective therapy is not available. It is, therefore, important to understand the signaling pathways that mediate disease progression in order to identify new molecular targets for therapeutic development. Here, we report that the blood serum levels of ephrin-A1 and the sheddase ADAM12 were significantly elevated in COVID-19 patients treated at SUNY Downstate Hospital of Brooklyn, New York. Both ephrin-A1 and ADAM12 are known to be involved in inflammation and regulate endothelial cell permeability, thus providing a gateway to lung injury. The clinical outcome correlated with the ephrin-A1 and ADAM12 serum levels during the first week of hospitalization. In contrast, the serum levels of TNFα were elevated in only a small subset of the patients, and these same patients also had highly elevated levels of the sheddase ADAM17. These data indicate that ephrin-A1-mediated inflammatory signaling may contribute to COVID-19 disease progression more so than TNFα-mediated inflammatory signaling. They also support the notion that, in COVID-19 inflammation, ADAM12 sheds ephrin-A1, while ADAM17 sheds TNFα. Furthermore, the results suggest that elevated serum levels and activity of cytokines, such as TNFα, and other secreted inflammatory molecules, such as ephrin-A1, are not simply due to overexpression, but also to upregulation of sheddases that release them into the blood circulation. Our results identify ephrin-A1, ADAM12, and other molecules in the ephrin-A1 signaling pathway as potential pharmacological targets for treating COVID-19 inflammation.

A computational evaluation of targeted oxidation strategy (TOS) for potential inhibition of SARS-CoV-2 by disulfiram and analogues.

In the new millennium, the outbreak of new coronavirus has happened three times: SARS-CoV, MERS-CoV, and SARS-CoV-2. Unfortunately, we still have no pharmaceutical weapons against the diseases caused by these viruses. The pandemic of SARS-CoV-2 reminds us the urgency to search new drugs with totally different mechanism that may target the weaknesses specific to coronaviruses. Herein, we disclose a computational evaluation of targeted oxidation strategy (TOS) for potential inhibition of SARS-CoV-2 by disulfiram, a 70-year-old anti-alcoholism drug, and predict a multiple-target mechanism. A preliminary list of promising TOS drug candidates targeting the two thiol proteases of SARS-CoV-2 are proposed upon virtual screening of 32,143 disulfides.

Modulation of lysosomal function as a therapeutic approach for coronaviral infections.

The endo-lysosomal pathway plays an important role in pathogen clearance and both bacteria and viruses have evolved complex mechanisms to evade this host system. Here, we describe a novel aspect of coronaviral infection, whereby the master transcriptional regulator of lysosome biogenesis - TFEB - is targeted for proteasomal-mediated degradation upon viral infection. Through mass spectrometry analysis and an unbiased siRNA screen, we identify that TFEB protein stability is coordinately regulated by the E3 ubiquitin ligase subunit DCAF7 and the PAK2 kinase. In particular, viral infection triggers marked PAK2 activation, which in turn, phosphorylates and primes TFEB for ubiquitin-mediated protein degradation. Deletion of either DCAF7 or PAK2 blocks viral-mediated TFEB degradation and protects against viral-induced cytopathic effects. We further derive a series of small molecules that interfere with the DCAF7-TFEB interaction. These agents inhibit viral-triggered TFEB degradation and demonstrate broad anti-viral activities including attenuating in vivo SARS-CoV-2 infection. Together, these results delineate a viral-triggered pathway that disables the endogenous cellular system that maintains lysosomal function and suggest that small molecule inhibitors of the E3 ubiquitin ligase DCAF7 represent a novel class of endo-lysosomal, host-directed, anti-viral therapies.

Administration of high titer convalescent anti-SARS-CoV-2 plasma: From donor selection to monitoring recipient outcomes.

Early in the SARS-CoV-2 pandemic, convalescent plasma (CP) therapy was proposed as a treatment for severely ill patients. We conducted a CP treatment protocol under the Mayo Clinic Extended Access Program at University Hospital Brooklyn (UHB). Potential donors were screened with a lateral flow assay (LFA) for IgM and IgG antibodies against the SARS-CoV-2 S1 receptor-binding domain (RBD). Volunteers that were LFA positive were tested with an ELISA to measure IgG titers against the RBD. Subjects with titers of at least 1:1024 were selected to donate. Most donors with positive LFA had acceptable titers and were eligible to donate. Out of 171 volunteers, only 65 tested positive in the LFA (38.0%), and 55 (32.2%) had titers of at least 1:1024. Before our donation program started, 31 CP units were procured from the New York Blood Center (NYBC). Among the 31 CP units that were obtained from the NYBC, 25 units (80.6%) were positive in the LFA but only 12 units (38.7%) had titers of at least 1:1024. CP was administered to 28 hospitalized COVID-19 patients. Patients who received low titer CP, high titer CP and patients who did not receive CP were followed for 45 days after presentation. Severe adverse events were not associated with CP transfusion. Death was a less frequent outcome for patients that received high titer CP (>1:1024) 38.6% mortality, than patients that received low titer CP (≤1:1024) 77.8% mortality.

The Effect of Person-Job Fit on Nurses' Engagement in the Context of COVID-19: The Mediating Role of Exemplary Role of Heroes in Harm’s Way

Objective: to understand the status quo of nurse's person-job fit, nurse's engagement, and nurses' attitude towards the exemplary role of heroes in harm’s way. To explore the relationship between person-job fit and nurses' engagement under the background of COVID-19, and the mediating role of the exemplary role of heroes in harm’s way. In the context of COVID-19 and other major public health emergencies, it provides countermeasures for strengthening the matching degree between nurses and their work and enhancing nurses' engagement, so as to provide theoretical support for further demonstrating exemplary role of heroes in harm’s way during the COVID-19 and major public health emergencies. Methods: in August 2020, 639 nurses were investigated with person-job fit scale, model demonstration scale and engagement scale. Results: person-job fit positively affected nurses' engagement and its dimensions, and the exemplary role of heroes in harm’s way mediated the relationship between them. Conclusion: The degree of understanding and influence of the heroes in harm’s way is different, and the influence degree of each variable is also different;the higher the level of person-job fit of nurses, the higher the degree of nurses' engagement. The exemplary role of heroes in harm’s way will mediate the relationship between the two.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

A high throughput screen for TMPRSS2 expression identifies FDA-approved and clinically advanced compounds that can limit SARS-CoV-2 entry.

SARS-CoV-2 (2019-nCoV) is the pathogenic coronavirus responsible for the global pandemic of COVID-19 disease. The Spike (S) protein of SARS-CoV-2 attaches to host lung epithelial cells through the cell surface receptor ACE2, a process dependent on host proteases including TMPRSS2. Here, we identified small molecules that can reduce surface expression of TMPRSS2 using a 2,700 FDA-approved or current clinical trial compounds. Among these, homoharringtonine and halofuginone appear the most potent agents, reducing endogenous TMPRSS2 expression at sub-micromolar concentrations. These effects appear to be mediated by a drug-induced alteration in TMPRSS2 protein stability. We further demonstrate that halofuginone modulates TMPRSS2 levels through proteasomal-mediated degradation that involves the E3 ubiquitin ligase component DDB1- and CUL4-associated factor 1 (DCAF1). Finally, cells exposed to homoharringtonine and halofuginone, at concentrations of drug known to be achievable in human plasma, demonstrated marked resistance to SARS-CoV-2 pseudoviral infection. Given the safety and pharmacokinetic data already available for the compounds identified in our screen, these results should help expedite the rational design of human clinical trials designed to combat COVID-19 infection.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.