Porcine epidemic diarrhea virus E protein inhibits type I interferon production through endoplasmic reticulum stress response (ERS)-mediated suppression of antiviral proteins translation.

Porcine epidemic diarrhea virus (PEDV) envelope protein (E) is recognized as a viroporin that plays important functions in virus budding, assembly and virulence. Our previous study found that PEDV E protein induces endoplasmic reticulum stress (ERS), as well as suppresses the type I interferon (IFN) response, but their link and underlying mechanism remain obscure. To better understand this relationship, we investigated the roles of PEDV E protein-induced ERS in regulating cellular type I IFN production. Our results showed that PEDV E protein localized in the ER and triggered ERS through activation of PERK/eIF2α branch, as revealed by the up-regulated phosphorylation of PERK and eIF2α. PEDV E protein also significantly inhibited both poly(I:C)-induced and RIG-I signaling-mediated type I interferon production. The PERK/eIF2α branch of ERS activated by PEDV E protein led to the translation attenuation of RIG-I signaling-associated antiviral proteins, resulting in the suppression of type I IFN production. However, PEDV E protein had no effect on the mRNA transcription of RIG-I-associated molecules. Moreover, suppression of ERS with 4-PBA, a widely used ERS inhibitor, restored the expression of RIG-I-signaling-associated antiviral proteins and mRNA transcription of IFN-ß and ISGs genes to their normal levels, suggesting that PEDV E protein blocks the production of type I IFN through inhibiting expression of antiviral proteins caused by ERS-mediated translation attenuation. This study elucidates the mechanism by which PEDV E protein specifically modulates the ERS to inhibit type I IFN production, which will augment our understanding of PEDV E protein-mediated virus evasion of host innate immunity.

Propagation and Diffusion of Fluorescent Substances with Footprints in Indoor Environments.

Some studies have shown that contaminants can be transferred between floors and the soles, and there are few studies on pollutant propagation caused by human walking in real-life situations. This study explored the propagation and diffusion law of ground pollutants from rubber soles to poly vinyl chloride (PVC) floor during indoor walking through employing a fluorescent solution as a simulant. The footprint decay (D) and transfer efficiency (τ) of the fluorescent solution transferred from the sole to the indoor floor during walking were analyzed based on the fluorescent footprint imaging. The effects of namely body weight (50-75 kg), walking frequency (80-120 steps/min), and solution viscosity (oil and water) were also investigated. It was found that the total fluorescence gray value on the ground decreased exponentially as the number of walking steps (i) increased. The relationship between the normalized gray value of the fluorescent solution (D) on each floor panel i was Di=aebi,2.1≤a≤3.8,-1.4≤b≤-0.7, and τ was distributed in the range of 0.51-0.72. All influencing factors had a significant effect on a, and a greater body weight resulted in a smaller a value, while only the body weight had a significant effect on b and τ, and a greater body weight led to larger b and lower τ values.

Machine Learning for Prediction of Patients on Hemodialysis with an Undetected SARS-CoV-2 Infection.

Background: We developed a machine learning (ML) model that predicts the risk of a patient on hemodialysis (HD) having an undetected SARS-CoV-2 infection that is identified after the following ≥3 days. Methods: As part of a healthcare operations effort, we used patient data from a national network of dialysis clinics (February-September 2020) to develop an ML model (XGBoost) that uses 81 variables to predict the likelihood of an adult patient on HD having an undetected SARS-CoV-2 infection that is identified in the subsequent ≥3 days. We used a 60%:20%:20% randomized split of COVID-19-positive samples for the training, validation, and testing datasets. Results: We used a select cohort of 40,490 patients on HD to build the ML model (11,166 patients who were COVID-19 positive and 29,324 patients who were unaffected controls). The prevalence of COVID-19 in the cohort (28% COVID-19 positive) was by design higher than the HD population. The prevalence of COVID-19 was set to 10% in the testing dataset to estimate the prevalence observed in the national HD population. The threshold for classifying observations as positive or negative was set at 0.80 to minimize false positives. Precision for the model was 0.52, the recall was 0.07, and the lift was 5.3 in the testing dataset. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the model was 0.68 and 0.24 in the testing dataset, respectively. Top predictors of a patient on HD having a SARS-CoV-2 infection were the change in interdialytic weight gain from the previous month, mean pre-HD body temperature in the prior week, and the change in post-HD heart rate from the previous month. Conclusions: The developed ML model appears suitable for predicting patients on HD at risk of having COVID-19 at least 3 days before there would be a clinical suspicion of the disease.

A digital single-molecule nanopillar SERS platform for predicting and monitoring immune toxicities in immunotherapy.

The introduction of immune checkpoint inhibitors has demonstrated significant improvements in survival for subsets of cancer patients. However, they carry significant and sometimes life-threatening toxicities. Prompt prediction and monitoring of immune toxicities have the potential to maximise the benefits of immune checkpoint therapy. Herein, we develop a digital nanopillar SERS platform that achieves real-time single cytokine counting and enables dynamic tracking of immune toxicities in cancer patients receiving immune checkpoint inhibitor treatment - broader applications are anticipated in other disease indications. By analysing four prospective cytokine biomarkers that initiate inflammatory responses, the digital nanopillar SERS assay achieves both highly specific and highly sensitive cytokine detection down to attomolar level. Significantly, we report the capability of the assay to longitudinally monitor 10 melanoma patients during immune inhibitor blockade treatment. Here, we show that elevated cytokine concentrations predict for higher risk of developing severe immune toxicities in our pilot cohort of patients.

Impact of cancer on mortality and severity of corona virus disease 2019: A protocol for systematic review and meta-analysis.

BACKGROUND: Cancer patients are in a state of systemic immunosuppression and are considered a highly vulnerable population in the Corona Virus Disease 2019 (COVID-19) epidemic. However, the relationship between cancer and the severity and mortality of patients with COVID-19 remains unclear. This study aims to evaluate whether cancer patients with COVID-19 may be at an increased risk of severe illness and mortality. METHODS: We will perform comprehensive searches in PubMed, EMBASE.com, Web of Science, and the Cochrane Central Register of Controlled Trials to identify studies providing prevalence of cancer between patients with severe and non-severe illness or between non-survivors and survivors. We will use the Newcastle-Ottawa quality assessment scale to assess the quality of included studies. We will conduct pairwise meta-analyses to compute the odds ratio and 95% confidence interval using the Mantel Haenszel method with the random-effects model. The statistical heterogeneity will be assessed using the I statistic. Subgroup analyses, sensitivity analyses, and meta-regression analyses will be performed to explore the sources of heterogeneity. RESULTS: The results of this study will be published in a peer-reviewed journal. CONCLUSION: Our meta-analysis will systematically evaluate the association between cancer and the severity and mortality of patients with COVID-19. This study will provide evidence to help determine whether cancer patients should be provided with special precautions and advised to use stronger personal protection. INPLASY REGISTRATION NUMBER: INPLASY202090093.