E3 Ubiquitin Ligases: The Operators of the Ubiquitin Code That Regulates the RLR and cGAS-STING Pathways.

The outbreaks caused by RNA and DNA viruses, such as SARS-CoV-2 and monkeypox, pose serious threats to human health. The RLR and cGAS-STING pathways contain major cytoplasmic sensors and signaling transduction axes for host innate antiviral immunity. In physiological and virus-induced pathological states, the activation and inactivation of these signal axes are tightly controlled, especially post-translational modifications (PTMs). E3 ubiquitin ligases (E3s) are the direct manipulator of ubiquitin codons and determine the type and modification type of substrate proteins. Therefore, members of the E3s family are involved in balancing the host's innate antiviral immune responses, and their functions have been extensively studied over recent decades. In this study, we overviewed the mechanisms of different members of three E3s families that mediate the RLR and cGAS-STING axes and analyzed them as potential molecular targets for the prevention and treatment of virus-related diseases.

Presepsin Predicts Severity and Secondary Bacterial Infection in COVID-19 by Bioinformatics Analysis.

Introduction: Novel coronavirus pneumonia (COVID-19) is an acute respiratory disease caused by the novel coronavirus SARS-CoV-2. Severe and critical illness, especially secondary bacterial infection (SBI) cases, accounts for the vast majority of COVID-19-related deaths. However, the relevant biological indicators of COVID-19 and SBI are still unclear, which significantly limits the timely diagnosis and treatment. Methods: The differentially expressed genes (DEGs) between severe COVID-19 patients with SBI and without SBI were screened through the analysis of GSE168017 and GSE168018 datasets. By performing Gene Ontology (GO) enrichment analysis for significant DEGs, significant biological processes, cellular components, and molecular functions were selected. To understand the high-level functions and utilities of the biological system, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed. By analyzing protein-protein interaction (PPI) and key subnetworks, the core DEGs were found. Results: 85 DEGs were upregulated, and 436 DEGs were downregulated. The CD14 expression was significantly increased in the SBI group of severe COVID-19 patients (P < 0.01). The area under the curve (AUC) of CD14 in the SBI group in severe COVID-19 patients was 0.9429. The presepsin expression was significantly higher in moderate to severe COVID-19 patients (P < 0.05). Presepsin has a diagnostic value for moderate to severe COVID-19 with the AUC of 0.9732. The presepsin expression of COVID-19 patients in the nonsurvivors was significantly higher than that in the survivors (P < 0.05). Conclusion: Presepsin predicts severity and SBI in COVID-19 and may be associated with prognosis in COVID-19.

Large-scale decrease in the social salience of climate change during the COVID-19 pandemic.

There are concerns that climate change attention is waning as competing global threats intensify. To investigate this possibility, we analyzed all link shares and reshares on Meta's Facebook platform (e.g., shares and reshares of news articles) in the United States from August 2019 to December 2020 (containing billions of aggregated and de-identified shares and reshares). We then identified all link shares and reshares on "climate change" and "global warming" from this repository to develop a social media salience index-the Climate SMSI score-and found an 80% decrease in climate change content sharing and resharing as COVID-19 spread during the spring of 2020. Climate change salience then briefly rebounded in the autumn of 2020 during a period of record-setting wildfires and droughts in the United States before returning to low content sharing and resharing levels. This fluctuating pattern suggests new climate communication strategies-focused on "systemic sustainability"-are necessary in an age of competing global crises.

Optically computed phase microscopy for quantitative dynamic imaging of label-free cells and nanoparticles.

Numerous drug delivery systems based on nanoparticles have been developed, such as those used in BioNTech/Pfizer's and Moderna's Covid vaccines. Knowledge on mechanical interactions between cells and nanoparticles is critical to advance the efficiency and safety of these drug delivery systems. To quantitatively track the motion of cell (transparent) and nanoparticles (nontransparent) with nanometer displacement sensitivity, we investigate a novel imaging technology, optically computed phase microscopy (OCPM) that processes 3D spatial-spectral data through optical computation. We demonstrate that OCPM has the capability to image the motion of cells and magnetic nanoparticles that are mechanically excited by an external magnetic field, quantitatively and in the en face plane.

Development and validation of prognosis model of mortality risk in patients with COVID-19.

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.

A U-shaped association between baseline neutrophil count and COVID-19-related mortality: A retrospective cohort study.

Several descriptive studies have reported that higher neutrophil count (NC) may be correlated with poor prognosis in patients with confirmed COVID-19 infection. However, the findings from these studies are limited by methodology and data analysis. This study is a cohort study. We nonselectively and consecutively collected a total of 663 participants in a Chinese hospital from January 7 to February 28. Standardized and two-piecewise Cox regression model were employed to evaluate the association between baseline neutrophil count (bNC), neutrophil count change rate (NCR), and death. bNC had a U-shaped association with death. In the range of 0.1 to ≤1.49 × 109 /L (hazard ratio [HR] = 0.19, 95% confidence interval [CI] = 0.05-0.66) and >3.55 × 109 /L of bNC (HR = 2.82, 95% CI = 1.19-6.67), the trends on bNC with mortality were opposite. By recursive algorithm, the bNC at which the risk of the death was lower in the range of >1.49 to ≤3.55 × 109 /L (HR = 13.64, 95% CI = 0.25-74.71). In addition, we find that NCRs (NCR1 and NCR2) are not associated with COVID-19-related deaths. Compared with NCR, bNC has the potential to be used for early risk stratification in patients with COVID-19 infection. The relationship between bNC and mortality was U-shaped. The safe range of bNC was 1.64-4.0 × 109 /L. Identifying the correlation may be helpful for early risk stratification and medical decision-making.

Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China.

BACKGROUND: This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). METHODS: The training cohort included consecutive COVID-19 patients at the First People's Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. RESULTS: A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80-.95); threshold, -2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92-.99); threshold, -2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68-.93) and 0.88 (.75-.96) for the clinical model and laboratory model, respectively. CONCLUSIONS: We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

Early decrease in blood platelet count is associated with poor prognosis in COVID-19 patients-indications for predictive, preventive, and personalized medical approach. (Predictive, preventive and personalised strategies in combatting COVID-19 pandemic and non-communicable disorders.)

Background Changes in platelet count are common in COVID-19 patients. The platelet count reflects the thrombocyte turnover, acting as a sensitive indicator of illness severity that is of great clinical utility to monitor a quickly changing health condition of patients affected by aggressive viral infections. This study aims to investigate the significance of platelet count during the progression of the disease in COVID-19 patients. Methods A total of 532 COVID-19 patients were involved in the cohort study from the First People's Hospital of Jiangxia District in Wuhan from January 7, 2020, to February 28, 2020. We collected the clinical characteristics and laboratory data of patients. Patients still hospitalized before February 29, 2020, died on admission, with malignant tumors, previous gastrointestinal surgery, missing baseline platelet count, or platelet count detected only once, were excluded. We used a generalized additive model and generalized additive mixed model to compare trends in platelet count over time among survivors and non-survivors, with an adjustment for potential confounders. Results During the follow-up, twenty-nine subjects died (mortality rate, 5.45%). The platelets among non-survivors decreased and among survivors increased gradually within 1 week after admission. In addition, the difference between the two groups showed an increasing trend during 1 week after admission. This difference increased by an average of 5.3 x 10

An ultrapotent synthetic nanobody neutralizes SARS-CoV-2 by stabilizing inactive Spike.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells via an interaction between its Spike protein and the host cell receptor angiotensin-converting enzyme 2 (ACE2). By screening a yeast surface-displayed library of synthetic nanobody sequences, we developed nanobodies that disrupt the interaction between Spike and ACE2. Cryo-electron microscopy (cryo-EM) revealed that one nanobody, Nb6, binds Spike in a fully inactive conformation with its receptor binding domains locked into their inaccessible down state, incapable of binding ACE2. Affinity maturation and structure-guided design of multivalency yielded a trivalent nanobody, mNb6-tri, with femtomolar affinity for Spike and picomolar neutralization of SARS-CoV-2 infection. mNb6-tri retains function after aerosolization, lyophilization, and heat treatment, which enables aerosol-mediated delivery of this potent neutralizer directly to the airway epithelia.

Clinical Manifestation and Neonatal Outcomes of Pregnant Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China.

BACKGROUND: Clinical manifestation and neonatal outcomes of pregnant women with coronavirus disease 2019 (COVID-19) were unclear in Wuhan, China. METHODS: We retrospectively analyzed clinical characteristics of pregnant and nonpregnant women with COVID-19 aged from 20 to 40, admitted between January 15 and March 15, 2020 at Union Hospital, Wuhan, and symptoms of pregnant women with COVID-19 and compared the clinical characteristics and symptoms to historic data previously reported for H1N1. RESULTS: Among 64 patients, 34 (53.13%) were pregnant, with higher proportion of exposure history (29.41% vs 6.67%) and more pulmonary infiltration on computed tomography test (50% vs 10%) compared to nonpregnant women. Of pregnant patients, 27 (79.41%) completed pregnancy, 5 (14.71%) had natural delivery, 18 (52.94%) had cesarean section, and 4 (11.76%) had abortion; 5 (14.71%) patients were asymptomatic. All 23 newborns had negative reverse-transcription polymerase chain results, and an average 1-minute Apgar score was 8-9 points. Pregnant and nonpregnant patients show differences in symptoms such as fever, expectoration, and fatigue and on laboratory tests such as neurophils, fibrinogen, D-dimer, and erythrocyte sedimentation rate. Pregnant patients with COVID-19 tend to have more milder symptoms than those with H1N1. CONCLUSIONS: Clinical characteristics of pregnant patients with COVID-19 are less serious than nonpregnant. No evidence indicated that pregnant women may have fetal infection through vertical transmission of COVID-19. Pregnant patients with H1N1 had more serious condition than those with COVID-19.

Early decrease in blood platelet count is associated with poor prognosis in COVID-19 patients—indications for predictive, preventive, and personalized medical approach

Changes in platelet count are common in COVID-19 patients. The platelet count reflects the thrombocyte turnover, acting as a sensitive indicator of illness severity that is of great clinical utility to monitor a quickly changing health condition of patients affected by aggressive viral infections. This study aims to investigate the significance of platelet count during the progression of the disease in COVID-19 patients. A total of 532 COVID-19 patients were involved in the cohort study from the First People’s Hospital of Jiangxia District in Wuhan from January 7, 2020, to February 28, 2020. We collected the clinical characteristics and laboratory data of patients. Patients still hospitalized before February 29, 2020, died on admission, with malignant tumors, previous gastrointestinal surgery, missing baseline platelet count, or platelet count detected only once, were excluded. We used a generalized additive model and generalized additive mixed model to compare trends in platelet count over time among survivors and non-survivors, with an adjustment for potential confounders. During the follow-up, twenty-nine subjects died (mortality rate, 5.45%). The platelets among non-survivors decreased and among survivors increased gradually within 1 week after admission. In addition, the difference between the two groups showed an increasing trend during 1 week after admission. This difference increased by an average of 5.3 × 10

Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19.

BACKGROUND: Several studies have described the clinical characteristics of patients with novel coronavirus (SARS-CoV-2) infected pneumonia (COVID-19), indicating severe patients tended to have higher neutrophil to lymphocyte ratio (NLR). Whether baseline NLR could be an independent predictor of in-hospital death in Chinese COVID-19 patients remains to be investigated. METHODS: A cohort of patients with COVID-19 admitted to the Zhongnan Hospital of Wuhan University from January 1 to February 29 was retrospectively analyzed. The baseline data of laboratory examinations, including NLR, were collected. Univariate and multivariate logistic regression models were developed to assess the independent relationship between the baseline NLR and in-hospital all-cause death. A sensitivity analysis was performed by converting NLR from a continuous variable to a categorical variable according to tertile. Interaction and stratified analyses were conducted as well. RESULTS: 245 COVID-19 patients were included in the final analyses, and the in-hospital mortality was 13.47%. Multivariate analysis demonstrated that there was 8% higher risk of in-hospital mortality for each unit increase in NLR (Odds ratio [OR] = 1.08; 95% confidence interval [95% CI], 1.01 to 1.14; P = 0.0147). Compared with patients in the lowest tertile, the NLR of patients in the highest tertile had a 15.04-fold higher risk of death (OR = 16.04; 95% CI, 1.14 to 224.95; P = 0.0395) after adjustment for potential confounders. Notably, the fully adjusted OR for mortality was 1.10 in males for each unit increase of NLR (OR = 1.10; 95% CI, 1.02 to 1.19; P = 0.016). CONCLUSIONS: NLR is an independent risk factor of the in-hospital mortality for COVID-19 patients especially for male. Assessment of NLR may help identify high risk individuals with COVID-19.