ACE2 and COVID-19 and the resulting ARDS.

This article reviews the correlation between ACE2 and COVID-19 and the resulting acute respiratory distress syndrome (ARDS). ACE2 is a crucial component of the renin-angiotensin system (RAS). The classical ACE-angiotensin Ⅱ (Ang II)-angiotensin type 1 receptor (AT1R) axis and the ACE2-Ang(1-7)-Mas counter-regulatory axis play an essential role in RAS system. ACE2 antagonises the activation of the classical RAS ACE-Ang II-AT1R axis and protects against lung injury. Similar to severe acute respiratory syndrome-related coronavirus, 2019 novel coronavirus (2019-nCoV) also uses ACE2 for cell entry. ARDS is a clinical high-mortality disease which is probably due to the excessive activation of RAS caused by 2019-nCoV infection, and ACE2 has a protective effect on ARDS caused by COVID-19. Because of these protective effects of ACE2 on ARDS, the development of drugs enhancing ACE2 activity may become one of the most promising approaches for the treatment of COVID-19 in the near future. In the meantime, however, the use of RAS blockers such as ACE inhibitors and angiotensin II receptor blockers that inhibit the damaging (ACE-Ang II) arm of the RAS cascade in the lung may also be promising. Trial registration number: NCT04287686.

A spike-targeting bispecific T cell engager strategy provides dual layer protection against SARS-CoV-2 infection in vivo.

Neutralizing antibodies exert a potent inhibitory effect on viral entry; however, they are less effective in therapeutic models than in prophylactic models, presumably because of their limited efficacy in eliminating virus-producing cells via Fc-mediated cytotoxicity. Herein, we present a SARS-CoV-2 spike-targeting bispecific T-cell engager (S-BiTE) strategy for controlling SARS-CoV-2 infection. This approach blocks the entry of free virus into permissive cells by competing with membrane receptors and eliminates virus-infected cells via powerful T cell-mediated cytotoxicity. S-BiTE is effective against both the original and Delta variant of SARS-CoV2 with similar efficacy, suggesting its potential application against immune-escaping variants. In addition, in humanized mouse model with live SARS-COV-2 infection, S-BiTE treated mice showed significantly less viral load than neutralization only treated group. The S-BiTE strategy may have broad applications in combating other coronavirus infections.

CapsNet-COVID19: Lung CT image classification method based on CapsNet model.

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.

Dynamic Changes of Liver Function Indexes in Patients with Different Clinical Types of COVID-19.

BACKGROUND: To investigate the liver function indexes and dynamic changes in patients with different clinical types of new coronavirus pneumonia (COVID-19). METHODS: A retrospective analysis of 170 COVID-19 patients hospitalized in the Wuxi Fifth People's Hospital was divided into asymptomatic group (13 cases), mild-common group (142 cases) and seriously-critically ill group (15 cases), the clinical data and liver function indexes of the three groups were compared. RESULTS: A total of 170 patients included 94 males and 76 females, with an average age of 44.7 ± 17.8 years. Seriously-critically ill group was older, and the proportion of patients with diabetes and liver injury at admission was also higher. As the hospitalization time increased, the changes of alanine aminotransferase (ALT) levels in asymptomatic group and mild-common group were not significant (all P > 0.05), while the ALT levels of seriously-critically ill group showed a curve that first flattened and then decreased (degree of freedom: 1.809, P = 0.002). Compared with the mild-common group, the daily decrease of ALT was 1.220U/L more in the seriously-critically ill group (P<0.001). The aspartate aminotransferase (AST) in asymptomatic group and seriously-critically ill group did not decrease significantly (all P > 0.05), while the AST in mild-common group decreased significantly (regression coefficient: -10.507, P = 0.008). There was no significant difference in AST changes between the three groups (P = 0.250-0.904). CONCLUSION: Liver injury is common in COVID-19 patients, especially for severe patients; the dynamic change pattern of liver function indicators may be helpful to judge liver injury and evaluate treatment effects in patients with different clinical types.

Identification of potent human neutralizing antibodies against SARS-CoV-2 implications for development of therapeutics and prophylactics.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that is spreading rapidly, which seriously impacts global public health and economy. Thus, developing effective drugs remains urgent. We identify two potent antibodies, nCoVmab1 and nCoVmab2, targeting the SARS-CoV-2 spike protein receptor-binding domain (RBD) with high affinities from a naïve human phage-displayed Fab library. nCoVmab1 and nCoVmab2 neutralize authentic SARS-CoV-2 with picomolar and nanomolar IC50 values, respectively. No detectable defects of nCoVmab1 and nCoVmab2 are found during the preliminary druggability evaluation. nCoVmab1 could reduce viral titer and lung injury when administered prophylactically and therapeutically in human angiotensin-converting enzyme II (hACE2)-transgenic mice. Therefore, phage display platform could be efficiently used for rapid development of neutralizing monoclonal antibodies (nmabs) with clinical potential against emerging infectious diseases. In addition, we determinate epitopes in RBD of these antibodies to elucidate the neutralizing mechanism. We also convert nCoVmab1 and nCoVmab2 to their germline formats for further analysis, which reveals the contribution of somatic hypermutation (SHM) during nCoVmab1 and nCoVmab2 maturation. Our findings not only provide two highly potent nmabs against SARS-CoV-2 as prophylactic and therapeutic candidates, but also give some clues for development of anti-SARS-CoV-2 agents (e.g., drugs and vaccines) targeting the RBD.

COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation.

COVID-19 is a new type of respiratory infectious disease that poses a serious threat to the survival of human beings all over the world. Using artificial intelligence technology to analyze lung images of COVID-19 patients can achieve rapid and effective detection. This study proposes a COVSeg-NET model that can accurately segment ground glass opaque lesions in COVID-19 lung CT images. The COVSeg-NET model is based on the fully convolutional neural network model structure, which mainly includes convolutional layer, nonlinear unit activation function, maximum pooling layer, batch normalization layer, merge layer, flattening layer, sigmoid layer, and so forth. Through experiments and evaluation results, it can be seen that the dice coefficient, sensitivity, and specificity of the COVSeg-NET model are 0.561, 0.447, and 0.996 respectively, which are more advanced than other deep learning methods. The COVSeg-NET model can use a smaller training set and shorter test time to obtain better segmentation results.

Engineering mesenchymal stromal cells with neutralizing and anti-inflammatory capability against SARS-CoV-2 infection.

The emergence of the novel human severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has led to the pandemic of coronavirus disease 2019 (COVID-19), which has markedly affected global health and the economy. Both uncontrolled viral replication and a proinflammatory cytokine storm can cause severe tissue damage in patients with COVID-19. SARS-CoV-2 utilizes angiotensin-converting enzyme 2 (ACE2) as its entry receptor. In this study, we generated ACE2 extracellular domain-Fc and single-chain variable fragment-interleukin 6 (IL-6) single-chain variable fragment against IL-6 receptor (scFv-IL6R)-Fc fusion proteins to differentially neutralize viruses and ameliorate the cytokine storm. The human ACE2 (hACE2)1-740-Fc fusion protein showed a potent inhibitory effect on pseudo-typed SARS-CoV-2 entry and a good safety profile in mice. In addition, scFv-IL6R-Fc strongly blocked IL-6 signal activation. We also established a mesenchymal stromal cell (MSC)-based hACE21-740-Fc and scFv-IL6R-Fc delivery system, which could serve as a potential therapy strategy for urgent clinical needs of patients with COVID-19.

Application of a prediction model with laboratory indexes in the risk stratification of patients with COVID-19

In the present study, a prediction model with combined laboratory indexes in risk stratification of patients with COVID-19 was established and tested. The data of 170 patients with COVID-19 who were divided into an asymptomatic-moderate group (141 cases) and severe or above group (29 cases) were retrospectively analyzed. The clinical characteristics and laboratory indexes of the two groups were compared. Multivariate logistic regression analysis was performed to construct the prediction model based on laboratory indexes. A receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficacy of different indexes. Decision curve analysis (DCA) was performed to quantify and compare the clinical validity of the prediction models. There were significant differences in blood cell count, high-sensitivity C-reactive protein (hsCRP) and procalcitonin (PCT) levels between the severe or above group and the asymptomatic-moderate group (all P<0.05). Among all individual indexes, hsCRP had the highest diagnostic efficacy (area under the curve=0.870), with a sensitivity and specificity of 0.828 and 0.802, respectively. The red blood cell count, hsCRP and PCT were used to construct the prediction model. The AUC of the prediction model was higher than that of hsCRP (0.912 vs. 0.870) but the difference was not significant (P=0.307). DCA suggested that the net benefit of the prediction model was higher than that of hsCRP in most cases and significantly higher than that of PCT, lymphocytes and monocytes. The prediction model with combined laboratory indexes was able to more effectively predict the clinical classification of patients with COVID-19 and may be used as a tool for risk stratification of patients. [ABSTRACT FROM AUTHOR] Copyright of Experimental & Therapeutic Medicine is the property of Spandidos Publications UK Ltd 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.)

Effect of Thymoquinone on Acute Kidney Injury Induced by Sepsis in BALB/c Mice.

Acute kidney injury (AKI) is a common complication of sepsis and has also been observed in some patients suffering from the new coronavirus pneumonia COVID-19, which is currently a major global concern. Thymoquinone (TQ) is one of the most active ingredients in Nigella sativa seeds. It has a variety of beneficial properties including anti-inflammatory and antioxidative activities. Here, we investigated the possible protective effects of TQ against kidney damage in septic BALB/c mice. Eight-week-old male BALB/c mice were divided into four groups: control, TQ, cecal ligation and puncture (CLP), and TQ+CLP. CLP was performed after 2 weeks of TQ gavage. After 48 h, we measured the histopathological alterations in the kidney tissue and the serum levels of creatinine (CRE) and blood urea nitrogen (BUN). We also evaluated pyroptosis (NLRP3, caspase-1), apoptosis (caspase-3, caspase-8), proinflammatory (TNF-α, IL-1ß, and IL-6)-related protein and gene expression levels. Our results demonstrated that TQ inhibited CLP-induced increased serum CRE and BUN levels. It also significantly inhibited the high levels of NLRP3, caspase-1, caspase-3, caspase-8, TNF-α, IL-1ß, and IL-6 induced by CLP. Furthermore, NF-κB protein level was significantly decreased in the TQ+CLP group than in the CLP group. Together, our results indicate that TQ may be a potential therapeutic agent for sepsis-induced AKI.