Which East Asian herbal medicines can decrease viral infections?

Whilst Western research for the COVID-19 crisis focuses on vaccination, in East Asia traditional herbal prescriptions are studied for SARS-CoV2 therapy. In Japan, Maoto (Ephedrae herba 4 g, Armeniacae semen 4 g, Cinnamomi cortex 3 g, and Glycyrrhizae radix 2 g, JPXVII) is used based on clinical evidence for its effect on early phase influenza (also caused by RNA viruses) comparable to that of oseltamivir. The Health Ministry of Thailand has approved Andrographis paniculata (Jap. Senshinren) extracts for treatment of COVID-19. Its combination (4 g) with Maoto, Maoto-ka-senshinren, seems most promising for the treatment of viral pandemics. In China, the official guideline for COVID-19 treatment contains TCM medications with antiviral, as well as immunmodulatory and anti-inflammatory effects such as: Qing-Fei-Pai-Du-Tang (Jap. Seihai-haidokuto) contains 21 drugs; Shufeng Jiedu Jiaonang (Bupleuri radix 8 g, Forsythiae fructus 8 g, Glycyrrhizae radix 4 g, Isatidis radix 8 g, Patriniae herba 8 g, Phragmitis rhizoma 6 g, Polygoni cuspidati rhizoma 10 g, Verbenae herba 8 g); Fufang Yuxingcao Heiji (Forsythiae fructus 0.6 g, Houttuyniae herba 6 g, Isatidis radix 1.5 g, Lonicerae flos 0.6 g, Scutellariae radix 1.5 g) first gained prominence during the 2002 SARS epidemic. With no Western medicine available, the following overview discusses efficacy and mechanisms in view of viral entry and replication of different East Asian herbal remedies for COVID-19 treatment.

Review of the risk factors for SARS-CoV-2 transmission.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, which has lasted for nearly a year, has made people deeply aware of the strong transmissibility and pathogenicity of SARS-CoV-2 since its outbreak in December 2019. By December 2020, SARS-CoV-2 had infected over 65 million people globally, resulting in more than 1 million deaths. At present, the exact animal origin of SARS-CoV-2 remains unclear and antiviral vaccines are now undergoing clinical trials. Although the social order of human life is gradually returning to normal, new confirmed cases continue to appear worldwide, and the majority of cases are sporadic due to environmental factors and lax self-protective consciousness. This article provides the latest understanding of the epidemiology and risk factors of nosocomial and community transmission of SARS-CoV-2, as well as strategies to diminish the risk of transmission. We believe that our review will help the public correctly understand and cope with SARS-CoV-2.

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.