Non-contact screening system based for COVID-19 on XGBoost and logistic regression.

BACKGROUND: The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. OBJECTIVE: We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. METHODS: We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. RESULTS: The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. CONCLUSION: The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff.

The high diversity of SARS-CoV-2-related coronaviruses in pangolins alerts potential ecological risks.

Understanding the zoonotic origin and evolution history of SARS-CoV-2 will provide critical insights for alerting and preventing future outbreaks. A significant gap remains for the possible role of pangolins as a reservoir of SARS-CoV-2 related coronaviruses (SC2r-CoVs). Here, we screened SC2r-CoVs in 172 samples from 163 pangolin individuals of four species, and detected positive signals in muscles of four Manis javanica and, for the first time, one M. pentadactyla. Phylogeographic analysis of pangolin mitochondrial DNA traced their origins from Southeast Asia. Using in-solution hybridization capture sequencing, we assembled a partial pangolin SC2r-CoV (pangolin-CoV) genome sequence of 22 895 bp (MP20) from the M. pentadactyla sample. Phylogenetic analyses revealed MP20 was very closely related to pangolin-CoVs that were identified in M. javanica seized by Guangxi Customs. A genetic contribution of bat coronavirus to pangolin-CoVs via recombination was indicated. Our analysis revealed that the genetic diversity of pangolin-CoVs is substantially higher than previously anticipated. Given the potential infectivity of pangolin-CoVs, the high genetic diversity of pangolin-CoVs alerts the ecological risk of zoonotic evolution and transmission of pathogenic SC2r-CoVs.

Chronic Active Hepatitis B with COVID-19 in Pregnancy: A Case Report.

Currently, infection with coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), during pregnancy is a problem worthy of attention, especially in patients with underlying diseases. In this case report, we present a case of chronic active hepatitis B with COVID-19 in pregnancy. A 31-year-old woman at 29 weeks of gestation who had a history of chronic hepatitis B virus infection discontinued antiviral treatment, was admitted to the hospital with chronic active hepatitis B, and tested positive for SARS-CoV-2 infection. In this case, we applied liver protective and antiviral agents, and low-dose dexamethasone therapy to successfully treat the critically ill pregnant woman suffering from chronic active hepatitis B combined with COVID-19.

Inactivating SARS-CoV-2 by electrochemical oxidation.

Fully inactivating SARS-CoV-2, the virus causing coronavirus disease 2019, is of key importance for interrupting virus transmission but is currently performed by using biologically or environmentally hazardous disinfectants. Herein, we report an eco-friendly and efficient electrochemical strategy for inactivating the SARS-CoV-2 using in-situ formed nickel oxide hydroxide as anode catalyst and sodium carbonate as electrolyte. At a voltage of 5 V, the SARS-CoV-2 viruses can be rapidly inactivated with disinfection efficiency reaching 95% in only 30 s and 99.99% in 5 min. Mass spectrometry analysis and theoretical calculations indicate that the reactive oxygen species generated on the anode can oxidize the peptide chains and induce cleavage of the peptide backbone of the receptor binding domain of the SARS-CoV-2 spike glycoprotein, and thereby disables the virus. This strategy provides a sustainable and highly efficient approach for the disinfection of the SARS-CoV-2 viruliferous aerosols and wastewater.