A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

The role transition of radiotherapy for the treatment of liver cancer in the COVID-19 era

The uncontrollable COVID-19 crises in the SARS-CoV-2 high-prevalence areas have greatly disrupted the routine treatment of liver cancer and triggered a role transformation of radiotherapy for liver cancer. The weight of radiotherapy in the treatment algorithm for liver cancer has been enlarged by the COVID-19 pandemic, which is helpful for the optimal risk-benefit profile.

Scrutiny of human lung infection by SARS-CoV-2 and associated human immune responses in humanized mice (preprint)

There is an urgent need for animal models of COVID-19 to study immunopathogenesis and test therapeutic intervenes. In this study we showed that NSG mice engrafted with human lung (HL) tissue (NSG-L) could be infected efficiently by SARS-CoV-2, and that live virus capable of infecting Vero cells was found in the HL grafts and multiple organs from infected NSG-L mice. RNA-seq examination identified a series of differentially expressed genes, which are enriched in viral defense responses, chemotaxis, interferon stimulation, and pulmonary fibrosis between HL grafts from infected and control NSG-L mice. Furthermore, when infecting humanized mice with human immune system (HIS) and autologous HL grafts (HISL mice), the mice had bodyweight loss and hemorrhage and immune cell infiltration in HL grafts, which were not observed in immunodeficient NSG-L mice, indicating the development of anti-viral immune responses in these mice. In support of this possibility, the infected HISL mice showed bodyweight recovery and lack of detectable live virus at the later time. These results demonstrate that NSG-L and HISL mice are susceptible to SARS-CoV-2 infection, offering a useful in vivo model for studying SARS-CoV-2 infection and the associated immune response and immunopathology, and testing anti-SARS-CoV-2 therapies.

Effects of climate variables on the transmission of COVID-19: a systematic review of 62 ecological studies.

The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the most serious global public health crises. A collection and analysis of studies related to the association between COVID-19 (coronavirus disease 2019) transmission and meteorological factors, such as humidity, is vital and indispensable for disease prevention and control. A comprehensive literature search using various databases, including Web of Science, PubMed, and Chinese National Knowledge Infrastructure, was systematically performed to identify eligible studies from Dec 2019 to Feb 1, 2021. We also established six criteria to screen the literature to obtain high-quality literature with consistent research purposes. This systematic review included a total of 62 publications. The study period ranged from 1 to 8 months, with 6 papers considering incubation, and the lag effect of climate factors on COVID-19 activity being taken into account in 22 studies. After quality assessment, no study was found to have a high risk of bias, 30 studies were scored as having moderate risks of bias, and 32 studies were classified as having low risks of bias. The certainty of evidence was also graded as being low. When considering the existing scientific evidence, higher temperatures may slow the progression of the COVID-19 epidemic. However, during the course of the epidemic, these climate variables alone could not account for most of the variability. Therefore, countries should focus more on health policies while also taking into account the influence of weather.

Detection of Respiratory Infections Using RGB-Infrared Sensors on Portable Device

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global pandemic in the past few months and caused huge loss to human society worldwide. For such a large-scale pandemic, early detection and isolation of potential virus carriers is essential to curb the spread of the pandemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the pandemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health conditions of people wearing masks through analysis of the respiratory characteristics from RGB-infrared sensors. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with an attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status of respiratory with 83.69% accuracy, 90.23% sensitivity and 76.31% specificity on the real-world dataset. This work demonstrates that the proposed RGB-infrared sensors on portable device can be used as a pre-scan method for respiratory infections, which provides a theoretical basis to encourage controlled clinical trials and thus helps fight the current COVID-19 pandemic. The demo videos of the proposed system are available at: https://doi.org/10.6084/m9.figshare.12028032.

Immunity-and-Matrix-Regulatory Cells Derived from Human Embryonic Stem Cells Safely and Effectively Treat Mouse Lung Injury and Fibrosis (preprint)

Lung injury and fibrosis represent the most significant outcomes of severe and acute lung disorders, including COVID-19. However, there are still no effective drugs to treat lung injury and fibrosis. In this study, we report the generation of clinical-grade human embryonic stem cells (hESCs)-derived immunity- and matrix-regulatory cells (IMRCs) produced under good manufacturing practice (GMP) requirements, that can treat lung injury and fibrosis in vivo. We generate IMRCs by sequentially differentiating hESCs with serum-free reagents. IMRCs possess a unique gene expression profile distinct from umbilical cord mesenchymal stem cells (UCMSCs), such as higher levels of proliferative, immunomodulatory and anti-fibrotic genes. Moreover, intravenous delivery of IMRCs inhibits both pulmonary inflammation and fibrosis in mouse models of lung injury, and significantly improves the survival rate of the recipient mice in a dose-dependent manner, likely through paracrine regulatory mechanisms. IMRCs are superior to both primary UCMSCs and FDA-approved pirfenidone, with an excellent efficacy and safety profile in mice and monkeys. In light of public health crises involving pneumonia, acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), our findings suggest that IMRCs are ready for clinical trials on lung disorders.