Time-course RNA-Seq profiling reveals isoform-level gene expression dynamics of the cGAS-STING pathway.

The cGAS-STING pathway, orchestrating complicated transcriptome-wide immune responses, is essential for host antiviral defense but can also drive immunopathology in severe COVID-19. Here, we performed time-course RNA-Seq experiments to dissect the transcriptome expression dynamics at the gene-isoform level after cGAS-STING pathway activation. The in-depth time-course transcriptome after cGAS-STING pathway activation within 12 h enabled quantification of 48,685 gene isoforms. By employing regression models, we obtained 13,232 gene isoforms with expression patterns significantly associated with the process of cGAS-STING pathway activation, which were named activation-associated isoforms. The combination of hierarchical and k-means clustering algorithms revealed four major expression patterns of activation-associated isoforms, including two clusters with increased expression patterns enriched in cell cycle, autophagy, antiviral innate-immune functions, and COVID-19 coronavirus disease pathway, and two clusters showing decreased expression pattern that mainly involved in ncRNA metabolism, translation process, and mRNA processing. Importantly, by merging four clusters of activation-associated isoforms, we identified three types of genes that underwent isoform usage alteration during the cGAS-STING pathway activation. We further found that genes exhibiting protein-coding and non-protein-coding gene isoform usage alteration were strongly enriched for the factors involved in innate immunity and RNA splicing. Notably, overexpression of an enriched splicing factor, EFTUD2, shifted transcriptome towards the cGAS-STING pathway activated status and promoted protein-coding isoform abundance of several key regulators of the cGAS-STING pathway. Taken together, our results revealed the isoform-level gene expression dynamics of the cGAS-STING pathway and uncovered novel roles of splicing factors in regulating cGAS-STING pathway mediated immune responses.

Quarantine and serial testing for variants of SARS-CoV-2 with benefits of vaccination and boosting on consequent control of COVID-19.

Quarantine and serial testing strategies for a disease depend principally on its incubation period and infectiousness profile. In the context of COVID-19, these primary public health tools must be modulated with successive SARS CoV-2 variants of concern that dominate transmission. Our analysis shows that (1) vaccination status of an individual makes little difference to the determination of the appropriate quarantine duration of an infected case, whereas vaccination coverage of the population can have a substantial effect on this duration, (2) successive variants can challenge disease control efforts by their earlier and increased transmission in the disease time course relative to prior variants, and (3) sufficient vaccine boosting of a population substantially aids the suppression of local transmission through frequent serial testing. For instance, with Omicron, increasing immunity through vaccination and boosters-for instance with 100% of the population is fully immunized and at least 24% having received a third dose-can reduce quarantine durations by up to 2 d, as well as substantially aid in the repression of outbreaks through serial testing. Our analysis highlights the paramount importance of maintaining high population immunity, preferably by booster uptake, and the role of quarantine and testing to control the spread of SARS CoV-2.

Cell tropism and viral clearance during SARS-CoV-2 lung infection.

Pulmonary capillary microthrombosis has been proposed as a major pathogenetic factor driving severe COVID-19. Autopsy studies reported endothelialitis but it is under debate if it is caused by SARS-CoV-2 infection of endothelial cells. In this study, RNA in situ hybridization was used to detect viral RNA and to identify the infected cell types in lung tissue of 40 patients with fatal COVID-19. SARS-CoV-2 Spike protein-coding RNA showed a steadily decreasing signal abundance over a period of three weeks. Besides the original virus strain the variants of concern Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529) could also be detected by the assay. Viral RNA was mainly detected in alveolar macrophages and pulmonary epithelial cells, while only single virus-positive endothelial cells were observed even in cases with high viral load suggesting that viral infection of endothelial cells is not a key factor for the development of pulmonary capillary microthrombosis.

An unsupervised framework for tracing textual sources of moral change

Morality plays an important role in social wellbeing, but people's moral perception is not stable and changes over time. Recent advances in natural language processing have shown that text is an effective medium for informing moral change, but no attempt has been made to quantify the origins of these changes. We present a novel unsupervised framework for tracing textual sources of moral change toward entities through time. We characterize moral change with probabilistic topical distributions and infer the source text that exerts prominent influence on the moral time course. We evaluate our framework on a diverse set of data ranging from social media to news articles. We show that our framework not only captures fine-grained human moral judgments, but also identifies coherent source topics of moral change triggered by historical events. We apply our methodology to analyze the news in the COVID-19 pandemic and demonstrate its utility in identifying sources of moral change in high-impact and real-time social events. © 2021 Association for Computational Linguistics.

Deep learning offering resilience from trending cyber-attacks, a review

During the Covid-19 pandemic world has witnessed the rise of cyber-attacks, especially during the Lockdown time course announced by the countries throughout the world, when almost every aspect of life changed the routine from offline to online. Protecting and securing information resources during pandemics has been a top priority for the modern computing world, with databases, banking, E-commerce and mailing services, etc. being the eye-catching credentials to the attackers. Apart from cryptography, machine learning and deep learning can offer an enormous amount of help in testing, training, and extracting negligible information from the data sets. Deep learning and machine learning have many methods and models in the account to detect and classify the different versions of cyber-attacks occasionally, from the datasets. Some of the most common deep learning methods inspired by the neural networks are Recurrent Neural Networks, Convolutional Neural Networks, Deep Belief Networks, Deep Boltzman Networks, Autoencoders, and Stacked Auto-encoders. Also counting machine learning algorithms into the account, there is a vast variety of algorithms that are meant to perform classification and regression. The survey will provide some of the most important deep learning and machine learning architectures used for Cyber-security and can offer protective services against cyber-attacks. The paper is a survey about various categories of cyber-attacks with a timeline of different attacks that took place in India and some of the other countries in the world. The final section of the report is about what deep learning methods can offer for developing and improving the security policies and examining vulnerabilities of an information system. © 2021 IEEE.

Timing and Duration of Axillary Lymph Node Swelling After COVID-19 Vaccination: Japanese Case Report and Literature Review.

BACKGROUND/AIM: COVID-19 vaccination is now performed in most of the world to limit the spread of the disease. The first mRNA vaccine was approved in clinical settings and has specific side effects including axillary lymph node swelling, which can be misdiagnosed as breast cancer metastasis. The timing of axillary lymph node swelling and its duration are unclear. Here, we present a Japanese case and review of the existing literature. CASE REPORT: We report the case of a 67-year-old woman with breast calcification. She had regular follow ups in our hospital for this calcification and received ultrasonography of the breast and axilla at every visit. She visited 6 months before having her COVID-19 vaccination, and 7 days and 6 months after the first COVID-19 vaccination. She had a swollen axillary lymph node 7 days after the first vaccination, which although it was improved, remained for 6 months. CONCLUSION: Axillary lymph node swelling occurred 7 days after vaccination and remained up to 6 months after it.

Spatial and temporal patterns and factors influencing the spread of the COVID-19 pandemic in China

It is essential to unravel the spatial and temporal patterns of the spread of the epidemic in China during the backdrop of the global coronavirus disease 2019 (COVID-19) outbreak in 2020, as the underlying drivers are crucial for scientific formulation of epidemy-preventing strategies. A discriminant model for the spatio-temporal pattern of epidemic spread was developed for 317 prefecture-level cities using accumulated data on confirmed cases. The model was introduced for the real-time evolution of the outbreak starting from the rapid spread of COVID-19 on January 24, 2020, until the control on March 18, 2020. The model was used to analyze the basic characteristics of the spatio-temporal patterns of the epidemic spread by combining parameters such as peak position, full width at half maximum, kurtosis, and skewness. A multivariate logistic regression model was developed to unravel the key drivers of the spatio-temporal patterns based on traffic accessibility, urban connectivity, and population flow. The results of the study are as follows. (1) The straight-line distance of 588 km from Wuhan was used as the effective boundary to identify the four spatial patterns of epidemic spread, and 13 types of spatio-temporal patterns were obtained by combining the time-course categories of the same spatial pattern. (2) The spread of the epidemic was relatively severe in the leapfrogging model. Besides the short-distance leapfrogging model, significant differences emerged in the spatial patterns of the time course of epidemic spread. The peaks of the new confirmed cases in various spatio-temporal patterns were mostly observed on February 3, 2020. The average full widths at the half maximum of all ordinary cities were approximately 14 days, thus, resonating with the incubation period of the COVID-19 virus. (3) The degree of the population correlation with Wuhan city has mainly influenced the spreading and the short-distance leapfrogging spatial patterns. The existence of direct flight from Wuhan city exhibited a positive effect on the long-distance leapfrogging spatial pattern. The number of population outflows has significantly affected the leapfrogging spatial pattern. The integrated spatial pattern was influenced by both primary and secondary epidemic outbreak sites. Thus, cities should pay great attention to traffic control during the epidemic as analysis has shown that the spatio-temporal patterns of epidemic spread in the respective cities can curb the spread of the epidemic from key links. © 2022, Science Press. All right reserved.

Time course of risk factors associated with mortality of 1260 critically ill patients with COVID-19 admitted to 24 Italian intensive care units.

PURPOSE: To evaluate the daily values and trends over time of relevant clinical, ventilatory and laboratory parameters during the intensive care unit (ICU) stay and their association with outcome in critically ill patients with coronavirus disease 19 (COVID-19). METHODS: In this retrospective-prospective multicentric study, we enrolled COVID-19 patients admitted to Italian ICUs from February 22 to May 31, 2020. Clinical data were daily recorded. The time course of 18 clinical parameters was evaluated by a polynomial maximum likelihood multilevel linear regression model, while a full joint modeling was fit to study the association with ICU outcome. RESULTS: 1260 consecutive critically ill patients with COVID-19 admitted in 24 ICUs were enrolled. 78% were male with a median age of 63 [55-69] years. At ICU admission, the median ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2) was 122 [89-175] mmHg. 79% of patients underwent invasive mechanical ventilation. The overall mortality was 34%. Both the daily values and trends of respiratory system compliance, PaO2/FiO2, driving pressure, arterial carbon dioxide partial pressure, creatinine, C-reactive protein, ferritin, neutrophil, neutrophil-lymphocyte ratio, and platelets were associated with survival, while for lactate, pH, bilirubin, lymphocyte, and urea only the daily values were associated with survival. The trends of PaO2/FiO2, respiratory system compliance, driving pressure, creatinine, ferritin, and C-reactive protein showed a higher association with survival compared to the daily values. CONCLUSION: Daily values or trends over time of parameters associated with acute organ dysfunction, acid-base derangement, coagulation impairment, or systemic inflammation were associated with patient survival.

Changes in fears and worries related to COVID-19 during the pandemic among current employees in Japan: a 5-month longitudinal study.

OBJECTIVES: This study investigates and describes the time course of fears and worries about COVID-19 among current employees during this outbreak. STUDY DESIGN: This was a longitudinal study. METHODS: This study was a part of the Employee Cohort Study in Japan. The study comprised 4120 individuals from February 2019. A baseline survey in March 2020, a 2-month follow-up survey in May 2020, and a 5-month follow-up survey in August 2020 were conducted. Questions surveyed respondents' global fear and worry and six items related to COVID-19. A mixed model for repeated measures of an analysis of variance was used. RESULTS: A total of 1421 respondents completed the baseline survey. At 2- and 5-month follow-ups, 1032 and 1181 respondents completed surveys, respectively. Of those, 64 and 33 individuals who were temporarily laid off or on leave were recorded as missing values. Global fear and worry about COVID-19 significantly increased from March to August 2020. Fears of personal or family infection, limiting one's activities and national and local government policies also significantly increased with time. In contrast, fears of lack of knowledge and difficulty of obtaining hygiene products significantly decreased. CONCLUSION: To conduct efficient risk communication during a pandemic, knowing the concerns of the populace, providing correct information and a sufficient supply of products, and setting clear guidelines are essential.

SARS-CoV-2 worldwide replication drives rapid rise and selection of mutations across the viral genome: a time-course study - potential challenge for vaccines and therapies.

Scientists and the public were alarmed at the first large viral variant of SARS-CoV-2 reported in December 2020. We have followed the time course of emerging viral mutants and variants during the SARS-CoV-2 pandemic in ten countries on four continents. We examined > 383,500 complete SARS-CoV-2 nucleotide sequences in GISAID (Global Initiative of Sharing All Influenza Data) with sampling dates extending until April 05, 2021. These sequences originated from ten different countries: United Kingdom, South Africa, Brazil, United States, India, Russia, France, Spain, Germany, and China. Among the 77 to 100 novel mutations, some previously reported mutations waned and some of them increased in prevalence over time. VUI2012/01 (B.1.1.7) and 501Y.V2 (B.1.351), the so-called UK and South Africa variants, respectively, and two variants from Brazil, 484K.V2, now called P.1 and P.2, increased in prevalence. Despite lockdowns, worldwide active replication in genetically and socio-economically diverse populations facilitated selection of new mutations. The data on mutant and variant SARS-CoV-2 strains provided here comprise a global resource for easy access to the myriad mutations and variants detected to date globally. Rapidly evolving new variant and mutant strains might give rise to escape variants, capable of limiting the efficacy of vaccines, therapies, and diagnostic tests.

Socio-economic impact on COVID-19 cases and deaths and its evolution in New Jersey.

Socio-economic factors could impact how epidemics spread. In this study, we investigated the possible effect of several local socio-economic factors on the case count and time course of confirmed Covid-19 cases and Covid-19-related deaths across the twenty one counties of New Jersey. Socio-economic and geographic factors considered included population, percentage of elders in the population, percentage of low-income households, access to food and health facilities and distance to New York. We found that the counties could be clustered into three groups based on (a) the case totals, (b) the total number of deaths, (c) the time course of the cases and (d) the time course of the deaths. The four groupings were very similar to one another and could all be largely explained by the county population, the percentage of low-income population, and the distance of the county from New York. As for food and health factors, the numbers of local restaurants and pharmacies significantly influenced the total number of cases and deaths as well as the epidemic's evolution. In particular, the number of cases and deaths showed a decrease with the number of McDonald's within the county in contrast to other fast-food or non-fast food restaurants. Overall, our study found that the evolution of the epidemic was influenced by certain socio-economic factors, which could be helpful for the formulation of public health policies.

Severe acute respiratory syndrome coronavirus 2 serology levels in pregnant women and their neonates.

BACKGROUND: Pregnant women and their neonates represent 2 vulnerable populations with an interdependent immune system that are highly susceptible to viral infections. The immune response of pregnant women to severe acute respiratory syndrome coronavirus 2 and the interplay of how the maternal immune response affects the neonatal passive immunity have not been studied systematically. OBJECTIVE: We characterized the serologic response in pregnant women and studied how this serologic response correlates with the maternal clinical presentation and with the rate and level of passive immunity that the neonate received from the mother. STUDY DESIGN: Women who gave birth and who tested positive for immunoglobulin M or immunoglobulin G against severe acute respiratory syndrome coronavirus 2 using semiquantitative detection in a New York City hospital between March 22, 2020, and May 31, 2020, were included in this study. A retrospective chart review of the cases that met the inclusion criteria was conducted to determine the presence of coronavirus disease 2019 symptoms and the use of oxygen support. Serology levels were compared between the symptomatic and asymptomatic patients using a Welch 2 sample t test. Further chart review of the same patient cohort was conducted to identify the dates of self-reported onset of coronavirus disease 2019 symptoms and the timing of the peak immunoglobulin M and immunoglobulin G antibody levels after symptom onset was visualized using local polynomial regression smoothing on log2-scaled serologic values. To study the neonatal serology response, umbilical cord blood samples of the neonates born to the subset of serology positive pregnant women were tested for serologic antibody responses. The maternal antibody levels of serology positive vs the maternal antibody levels of serology negative neonates were compared using the Welch 2 sample t test. The relationship between the quantitative maternal and quantitative neonatal serologic data was studied using a Pearson correlation and linear regression. A multiple linear regression analysis was conducted using maternal symptoms, maternal serology levels, and maternal use of oxygen support to determine the predictors of neonatal immunoglobulin G levels. RESULTS: A total of 88 serology positive pregnant women were included in this study. The antibody levels were higher in symptomatic pregnant women than in asymptomatic pregnant women. Serology studies in 34 women with symptom onset data revealed that the maternal immunoglobulin M and immunoglobulin G levels peak around 15 and 30 days after the onset of coronavirus disease 2019 symptoms, respectively. Furthermore, studies of 50 neonates born to this subset of serology positive women showed that passive immunity in the form of immunoglobulin G is conferred in 78% of all neonates. The presence of passive immunity is dependent on the maternal antibody levels, and the levels of neonatal immunoglobulin G correlate with maternal immunoglobulin G levels. The maternal immunoglobulin G levels and maternal use of oxygen support were predictive of the neonatal immunoglobulin G levels. CONCLUSION: We demonstrated that maternal serologies correlate with symptomatic maternal infection, and higher levels of maternal antibodies are associated with passive neonatal immunity. The maternal immunoglobulin G levels and maternal use of oxygen support, a marker of disease severity, predicted the neonatal immunoglobulin G levels. These data will further guide the screening for this uniquely linked population of mothers and their neonates and can aid in developing maternal vaccination strategies.

Two SARS-CoV-2 IgG immunoassays comparison and time-course profile of antibodies response.

INTRODUCTION: The persistence of circulating antibodies to SARS-CoV-2 infection is not yet well known. We compare the results of 2 automated systems for the determination of IgG against SARS CoV-2 and assess the time-course of the IgG response. METHODS: IgG were measured in 103 specimens of 55 patients with COVID-19 (time from the symptoms' onset: 3-187 days) using the automated tests "Abbott SARS-COV-2 IgG" and "MAGLUMI 2019-nCoV IgG". RESULTS: The 2 methods had a concordance of 90.3%, but the quantitative correlation, although significant, showed dispersed results. All the specimens resulted positive after 17 days. However, the median concentrations of IgG rapidly increased up to 20 days and decreased for Maglumi IgG while Abbott IgG showed a constant trend up to 85 days, and then slowly declined. CONCLUSIONS: The titer of IgG against SARS-CoV-2 may significantly and rapidly decrease, but with a very different time-course depending on the method used for the determination.

Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2.

Human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causative pathogen of the COVID-19 pandemic, exerts a massive health and socioeconomic crisis. The virus infects alveolar epithelial type 2 cells (AT2s), leading to lung injury and impaired gas exchange, but the mechanisms driving infection and pathology are unclear. We performed a quantitative phosphoproteomic survey of induced pluripotent stem cell-derived AT2s (iAT2s) infected with SARS-CoV-2 at air-liquid interface (ALI). Time course analysis revealed rapid remodeling of diverse host systems, including signaling, RNA processing, translation, metabolism, nuclear integrity, protein trafficking, and cytoskeletal-microtubule organization, leading to cell cycle arrest, genotoxic stress, and innate immunity. Comparison to analogous data from transformed cell lines revealed respiratory-specific processes hijacked by SARS-CoV-2, highlighting potential novel therapeutic avenues that were validated by a high hit rate in a targeted small molecule screen in our iAT2 ALI system.

The Role of Personality, Political Attitudes and Socio-Demographic Characteristics in Explaining Individual Differences in Fear of Coronavirus: A Comparison Over Time and Across Countries.

Since the emergence of the SARS-CoV-2 pandemic in December 2019 about 500,000 people died within the first 6 months. The virus itself, as well as the related political decisions, intensified an increasing feeling of fear in billions of people worldwide. However, while some people remained unperturbed, others experienced panic over the current situation. In order to investigate individual differences in the perceptions, emotions and behaviors in response to the Coronavirus pandemic, an online survey was conducted between 6th and 27th of March 2020. Participants included 7309 individuals from 96 countries, who provided information on socio-demographics, personality, political orientation and general life satisfaction. To determine the specificity of fear of Coronavirus, we also investigated fear related to two other current political issues: the refugee and the climate crises. Overall, in parallel with the escalation of the pandemic, fear of Coronavirus increased significantly over the 22-day period, with the strongest predictors being the personality variable neuroticism, as well as education, sex and being an at-risk person. A detailed longitudinal analysis of the largest sample, Germany, revealed that political orientation was also an important predictor of fear of Coronavirus. Specifically, conservatives were more afraid of Coronavirus than liberals. However, as the perceived threat of the virus increased, the influence of political orientation disappeared, whereas personality remained a stable predictor. The pattern of results regarding the perceived threat of the refugee and climate crises painted a different picture: political orientation was by far the best predictor, more important even than personality. Conservatives were more worried about the refugees, and liberals about climate change. Cross-cultural analyses showed pronounced differences between countries, dependent on the crisis. Nonetheless, the importance of personality for the prediction of fear of Coronavirus remained stable over time and across the world within the investigated 22-day period.