Pathway of Cell Death and Its Role in Virus Infection.

The global pandemic of SARS-CoV-2 in the past 2 years has aroused great attention to infectious diseases, and emerging virus outbreaks have brought huge challenges to the global health system. Viruses are specific pathogens that completely rely on host cells for their own survival and disease transmission. At present, a growing number of studies have proved that inducing the death of virus-infected cells can prevent the spread of virus and promote disease recovery. Therefore, many ways to induce the death of infected cells are considered to be beneficial to host immunity. Cell death is a basic biological phenomenon. Programmed cell death (PCD), as an important part of the host's innate immune response, provides effective protection against virus transmission. Pyroptosis, apoptosis, and necroptosis are the most commonly studied pathways of PCD. Recent studies have found that three pathways of cell death can be activated during virus infection. More and more studies have shown the existence of extensive connections between PCDs, and this complex relationship is defined as PANoptosis, an inflammatory PCD pathway regulated by the PANoptosome complex, whose characteristics cannot be explained by any of the three PCD pathways. During viral infection, PANoptosis can promote inflammatory response by inducing the production of inflammatory cytokines and cell death to exert an antiviral mechanism. This article reviews the various effects of cell death pathways during viral infection and provides new ideas for clinical antiviral therapy and related immunotherapy.

Maternal and neonatal safety of COVID-19 vaccination during the peri-pregnancy period: A prospective study.

BACKGROUND: To investigate the safety of inactivated COVID-19 vaccine in Chinese pregnant women and their fetuses when inoculated during the peri-pregnancy period. METHODS: Eligible pregnant women were prospectively collected and divided into a vaccine group (n = 93) and control group (n = 160) according to whether they had been vaccinated against COVID-19 within 3 months before their last menstruation period (LMP) and after pregnancy. Demographic data of couples, complications during pregnancy and delivery of pregnant women, and data of newborns at birth were collected. RESULTS: Sixty-six women were vaccinated with a median time of 35.5 (range = 0-91) days before LMP, and 27 women were vaccinated with a median time of 17 (range = 1-72) days after LMP. The incidence of premature rupture of membrane (PROM) in the vaccine group was significantly higher than that in the control group (16.13% vs. 6.88%, p = 0.019). Multivariate logistic regression analysis revealed that maternal peri-pregnancy COVID-19 vaccination was not an independent risk factor for PROM (odds ratio: 2.407, 95% confidence interval: 0.932-6.216, p = 0.069). There was no difference in the incidence of other complications during pregnancy and delivery between the two groups. A total of 253 neonates were delivered, including two cases with congenital abnormalities in each group. The incidence of congenital abnormalities between the two groups was similar (2.15% vs. 1.25%, p = 0.626). There was no difference in neonatal length, weight, head circumference, and Apgar score between the two groups (p > 0.05), but the incidence of neonatal jaundice in the vaccine group was significantly higher than that in the control group (20.43% vs. 7.5%, p = 0.002). Multivariate logistic regression analysis revealed that maternal peri-pregnancy vaccination, postpartum blood loss, cesarean section, 1-min Apgar score, and paternal smoking were independent risk factors for neonatal jaundice. CONCLUSIONS: It is safe for pregnant women and their fetuses to be inoculated the inactivated COVID-19 vaccine during the peri-pregnancy period, but attention should be paid to neonatal jaundice.

Pilot Study of Pollution Characteristics and Ecological Risk of Disinfection Byproducts in Natural Waters in Hong Kong.

Increased disinfection efforts in various parts of China, including Hong Kong, to prevent the spread of the novel coronavirus may lead to elevated concentrations of disinfectants in domestic sewage and surface runoff in Hong Kong, generating large quantities of toxic disinfection byproducts. Our study investigated the presence and distribution of four trihalomethanes (THMs), six haloacetic acids (HAAs), and eight nitrosamines (NAMs) in rivers and seawater in Hong Kong. The concentrations of THMs (mean concentration: 1.6 µg/L [seawater], 3.0 µg/L [river water]), HAAs (mean concentration: 1.4 µg/L [seawater], 1.9 µg/L [river water]), and NAMs (mean concentration: 4.4 ng/L [seawater], 5.6 ng/L [river water]) did not significantly differ between river water and seawater. The total disinfection byproduct content in river water in Hong Kong was similar to that in Wuhan and Beijing (People's Republic of China), and the total THM concentration in seawater was significantly higher than that before the COVID-19 pandemic. Among the regulated disinfection byproducts, none of the surface water samples exceeded the maximum index values for THM4 (80 µg/L), HAA5 (60 µg/L), and nitrosodimethylamine (100 ng/L) in drinking water. Among the disinfection byproducts detected, bromoform in rivers and seawater poses the highest risk to aquatic organisms, which warrants attention and mitigation efforts. Environ Toxicol Chem 2022;41:2613-2621. © 2022 SETAC.

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo.

The COVID-19 pandemic has created a global health crisis that has affected economies and societies worldwide. During these times of uncertainty and crisis, people have turned to social media platforms as communication tools and primary information sources. Online discourse is conducted under the influence of many different factors, such as background, culture, politics, etc. However, parallel comparative research studies conducted in different countries to identify similarities and differences in online discourse are still scarce. In this study, we combine the crisis lifecycle and opinion leader concepts and use data mining and a set of predefined search terms (coronavirus and COVID-19) to investigate discourse on Twitter (101,271 tweets) and Sina Weibo (92,037 posts). Then, we use a topic modeling technique, Latent Dirichlet Allocation (LDA), to identify the most common issues posted by users and temporal analysis to research the issue's trend. Social Network Analysis (SNA) allows us to discover the opinion leader on the two different platforms. Finally, we find that online discourse reflects the crisis lifecycle according to the stage of COVID-19 in China and the US. Regarding the status of the COVID-19 pandemic, users of Twitter tend to pay more attention to the economic situation while users of Weibo pay more attention to public health. The issues focused on in online discourse have a strong relationship with the development of the crisis in different countries. Additionally, on the Twitter platform many political actors act as opinion leaders, while on the Weibo platform official media and government accounts control the release of information.

Environmental perspective of COVID-19: Atmospheric and wastewater environment in relation to pandemic.

The pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major challenge to health systems worldwide. Recently, numbers of epidemiological studies have illustrated that climate conditions and air pollutants are associated with the COVID-19 confirmed cases worldwide. Researches also suggested that the SARS-CoV-2 could be detected in fecal and wastewater samples. These findings provided the possibility of preventing and controlling the COVID-19 pandemic from an environmental perspective. With this review, the main purpose is to summarize the relationship between the atmospheric and wastewater environment and COVID-19. In terms of the atmospheric environment, the evidence of the relationship between atmospheric environment (climate factors and air pollution) and COVID-19 is growing, but currently available data and results are various. It is necessary to comprehensively analyze their associations to provide constructive suggestions in responding to the pandemic. Recently, large numbers of studies have shown the widespread presence of this virus in wastewater and the feasibility of wastewater surveillance when the pandemic is ongoing. Therefore, there is an urgent need to clarify the occurrence and implication of viruses in wastewater and to understand the potential of wastewater-based epidemiology of pandemic. Overall, environmental perspective-based COVID-19 studies can provide new insight into pandemic prevention and control, and minimizes the economic cost for COVID-19 in areas with a large outbreak or a low economic level.

The SARS-unique domain (SUD) of SARS-CoV and SARS-CoV-2 interacts with human Paip1 to enhance viral RNA translation.

The ongoing outbreak of severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) demonstrates the continuous threat of emerging coronaviruses (CoVs) to public health. SARS-CoV-2 and SARS-CoV share an otherwise non-conserved part of non-structural protein 3 (Nsp3), therefore named as "SARS-unique domain" (SUD). We previously found a yeast-2-hybrid screen interaction of the SARS-CoV SUD with human poly(A)-binding protein (PABP)-interacting protein 1 (Paip1), a stimulator of protein translation. Here, we validate SARS-CoV SUD:Paip1 interaction by size-exclusion chromatography, split-yellow fluorescent protein, and co-immunoprecipitation assays, and confirm such interaction also between the corresponding domain of SARS-CoV-2 and Paip1. The three-dimensional structure of the N-terminal domain of SARS-CoV SUD ("macrodomain II", Mac2) in complex with the middle domain of Paip1, determined by X-ray crystallography and small-angle X-ray scattering, provides insights into the structural determinants of the complex formation. In cellulo, SUD enhances synthesis of viral but not host proteins via binding to Paip1 in pBAC-SARS-CoV replicon-transfected cells. We propose a possible mechanism for stimulation of viral translation by the SUD of SARS-CoV and SARS-CoV-2.

Promoting Public Engagement during the COVID-19 Crisis: How Effective Is the Wuhan Local Government's Information Release?

During times of public crises (such as COVID-19), governments must act swiftly to release crisis information effectively and efficiently to the public. This paper provides a general overview of the way that the Wuhan local government use Weibo as a channel to engage with their citizens during the COVID-19 pandemic. Based on the media richness, dialogic loop, and a series of theoretically relevant factors, such as content type, text length, and information source, we try to examine how citizen engage with their local government. By analyzing the data mining samples from Wuhan Release, the official Sina Weibo account of Wuhan's local government, results show that, despite the unstable situation COVID-19 over the crisis, there exist three stages of a crisis on the whole. Combining the behavior of the government and the public, duration from 31 December 2019 to 19 January 2020 could be seen as the development period, then the outbreak period (30 January 2020 to 28 February 2020), and a grace period (29 February 2020 to19 April 2020). Public attention to different types of information changes over time, but curbing rumors has always been a priority. Media richness features partially influent citizen engagement. Text length is significantly positively associated with citizen engagement through government social media. However, posts containing information sources have a negative impact on citizen engagement.

Promoting Public Engagement during the COVID-19 Crisis: How Effective Is the Wuhan Local Government’s Information Release?

During times of public crises (such as COVID-19), governments must act swiftly to release crisis information effectively and efficiently to the public. This paper provides a general overview of the way that the Wuhan local government use Weibo as a channel to engage with their citizens during the COVID-19 pandemic. Based on the media richness, dialogic loop, and a series of theoretically relevant factors, such as content type, text length, and information source, we try to examine how citizen engage with their local government. By analyzing the data mining samples from Wuhan Release, the official Sina Weibo account of Wuhan’s local government, results show that, despite the unstable situation COVID-19 over the crisis, there exist three stages of a crisis on the whole. Combining the behavior of the government and the public, duration from 31 December 2019 to 19 January 2020 could be seen as the development period, then the outbreak period (30 January 2020 to 28 February 2020), and a grace period (29 February 2020 to19 April 2020). Public attention to different types of information changes over time, but curbing rumors has always been a priority. Media richness features partially influent citizen engagement. Text length is significantly positively associated with citizen engagement through government social media. However, posts containing information sources have a negative impact on citizen engagement.

A Quantitative and Radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study.

Rationale: Acute respiratory distress syndrome (ARDS) is one of the major reasons for ventilation and intubation management of COVID-19 patients but there is no noninvasive imaging monitoring protocol for ARDS. In this study, we aimed to develop a noninvasive ARDS monitoring protocol based on traditional quantitative and radiomics approaches from chest CT. Methods: Patients diagnosed with COVID-19 from Jan 20, 2020 to Mar 31, 2020 were enrolled in this study. Quantitative and radiomics data were extracted from automatically segmented regions of interest (ROIs) of infection regions in the lungs. ARDS existence was measured by Pa02/Fi02 <300 in artery blood samples. Three different models were constructed by using the traditional quantitative imaging metrics, radiomics features and their combinations, respectively. Receiver operating characteristic (ROC) curve analysis was used to assess the effectiveness of the models. Decision curve analysis (DCA) was used to test the clinical value of the proposed model. Results: The proposed models were constructed using 352 CT images from 86 patients. The median age was 49, and the male proportion was 61.9%. The training dataset and the validation dataset were generated by randomly sampling the patients with a 2:1 ratio. Chi-squared test showed that there was no significant difference in baseline of the enrolled patients between the training and validation datasets. The areas under the ROC curve (AUCs) of the traditional quantitative model, radiomics model and combined model in the validation dataset was 0.91, 0.91 and 0.94, respectively. Accordingly, the sensitivities were 0.55, 0.82 and 0.58, while the specificities were 0.97, 0.86 and 0.98. The DCA curve showed that when threshold probability for a doctor or patients is within a range of 0 to 0.83, the combined model adds more net benefit than "treat all" or "treat none" strategies, while the traditional quantitative model and radiomics model could add benefit in all threshold probability. Conclusions: It is feasible to monitor ARDS from CT images using radiomics or traditional quantitative analysis in COVID-19. The radiomics model seems to be the most practical one for possible clinical use. Multi-center validation with a larger number of samples is recommended in the future.

Quantitative analysis of chest CT imaging findings with the risk of ARDS in COVID-19 patients: a preliminary study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has rapidly become a pandemic worldwide. The value of chest computed tomography (CT) is debatable during the treatment of COVID-19 patients. Compared with traditional chest X-ray radiography, quantitative CT may supply more information, but its value on COVID-19 patients was still not proven. METHODS: An automatic quantitative analysis model based on a deep network called VB-Net for infection region segmentation was developed. A quantitative analysis was performed for patients diagnosed as severe COVID 19. The quantitative assessment included volume and density among the infectious area. The primary clinical outcome was the existence of acute respiratory distress syndrome (ARDS). A univariable and multivariable logistic analysis was done to explore the relationship between the quantitative results and ARDS existence. RESULTS: The VB-Ne model was sensitive and stable for pulmonary lesion segmentation, and quantitative analysis indicated that the total volume and average density of the lung lesions were not related to ARDS. However, lesions with specific density changes showed some influence on the risk of ARDS. The proportion of lesion density from -549 to -450 Hounsfield unit (HU) was associated with increased risk of ARDS, while the density was ranging from -149 to -50 HU was related to a lowered risk of ARDS. CONCLUSIONS: The automatic quantitative model based on VB-Ne can supply useful information for ARDS risk stratification in COVID-19 patients during treatment.