Quantifying Time-Frequency Co-movement Impact of COVID-19 on U.S. and China Stock Market Toward Investor Sentiment Index.

The worldwide spread of COVID-19 dramatically influences the world economic landscape. In this paper, we have quantitatively investigated the time-frequency co-movement impact of COVID-19 on U.S. and China stock market since early 2020 in terms of daily observation from National Association of Securities Dealers Automated Quotations Index (NDX), Dow Jones Industrial Average (DJIA), Standard & Poor's 500 Index (SPX), Shanghai Securities Composite Index (SSEC), Shenzhen Securities Component Index (SZI), in favor of spatiotemporal interactions over investor sentiment index, and propose to explore the divisibility and the predictability to the volatility of stock market during the development of COVID-19. We integrate evidence yielded from wavelet coherence and phase difference to suggest the responses of stock market indexes to the COVID-19 epidemic in a long-term band, which could be roughly divided into three distinguished phases, namely, 30-75, 110-150, and 220-280 business days for China, and 80-125 and 160-175 after 290 business days for the U.S. At the first phase, the reason for the extreme volatility of stock market mainly attributed to the sudden emergence of the COVID-19 epidemic due to the pessimistic expectations from investors; China and U.S. stock market shared strongly negative correlation with the growing number of COVID-19 cases. At the second phase, the revitalization of stock market shared strong simultaneous moves but exhibited opposite responses to the COVID-19 impact on China and U.S. stock market; the former retained a significant negative correlation, while the latter turned to positively correlated throughout the period. At the third phase, the progress in vaccine development and economic stimulus began to impose forces to stock market; the vulnerability to COVID-19 diminished to some extent as the investor sentiment indexes rebounded. Finally, we attempted to initially establish a coarse-grained representation to stock market indexes and investor sentiment indexes, which demonstrated the homogenous spacial distribution in the vectorgraph after normalization and quantization, implying the strong consistency when filtering the frequent small fluctuations during the evolution of the COVID-19 pandemic, which might help insights into the prediction of possible status transition in stock market performance under the public health issues, potentially performing as the quantitative references in reasonably deducing the economic influences.

Nineteen New Flavanol-Fatty Alcohol Hybrids with α-Glucosidase and PTP1B Dual Inhibition: One Unusual Type of Antidiabetic Constituent from Amomum tsao-ko.

The dried fruits of Amomum tsao-ko were first revealed to have hypoglycemic effects on db/db mice at a concentration of 200 mg/kg. In order to clarify the antidiabetic constituents, 19 new flavanol-fatty alcohol hybrids, tsaokoflavanols A-S (1-19), were isolated and determined by extensive spectroscopic data and ECD calculations. Most of the compounds showed α-glucosidase and PTP1B dual inhibition, among which 1, 2, 6, 11, and 18 exhibited obvious activity against α-glucosidase with IC50 values of 5.2-9.0 µM, 20-35 times stronger than that of acarbose (IC50, 180.0 µM); meanwhile, 6, 10-12, and 19 were PTP1B/TCPTP-selective inhibitors with IC50 values of 56.4-80.4 µM, 2-4 times stronger than that of suramin sodium (IC50, 200.5 µM). Enzyme kinetics study indicated that compounds 1, 2, 6, and 11 were α-glucosidase and PTP1B mixed-type inhibitors with Ki values of 13.0, 11.7, 2.9, and 5.3 µM and 142.3, 88.9, 39.2, and 40.8 µM, respectively. Docking simulations proved the importance of hemiacetal hydroxy, the orientation of 3,4-dihydroxyphenyl, and the length of alkyl in binding with α-glucosidase and PTP1B.

Discovery of potential SARS-CoV-2 M protease inhibitors by virtual screening, molecular dynamics, and binding free energy analyses①

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) gained tremendous attention due to its high infectivity and pathogenicity. The 3-chymotrypsin-like hydrolase protease (Mpro) of SARS-CoV-2 has been proven to be an important target for anti-SARS-CoV-2 activity. To better identify the drugs with potential in treating coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and according to the crystal structure of Mpro, we conducted a virtual screening of FDA-approved drugs and chemical agents that have entered clinical trials. As a result, 9 drug candidates with therapeutic potential for the treatment of COVID-19 and with good docking scores were identified to target SARS-CoV-2. Consequently, molecular dynamics (MD) simulation was performed to explore the dynamic interactions between the predicted drugs and Mpro. The binding mode during MD simulation showed that hydrogen bonding and hydrophobic interactions played an important role in the binding processes. Based on the binding free energy calculated by using MM/PBSA, Lopiravir, an inhibitor of human immunodeficiency virus (HIV) protease, is under investigation for the treatment of COVID-19 in combination with ritionavir, and it might inhibit Mpro effectively. Moreover, Ombitasvir, an inhibitor for non-structural protein 5A of hepatitis C virus (HCV), has good inhibitory potency for Mpro. It is notable that the GS-6620 has a binding free energy, with respect to binding Mpro, comparable to that of ombitasvir. Our study suggests that ombitasvir and lopinavir are good drug candidates for the treatment of COVID-19, and that GS-6620 has good anti-SARS-CoV-2 activity. © 2021 Fujian Institute of Research of the Structure of Matter. All rights reserved.

External treatment of traditional Chinese medicine for COVID-19: A protocol for systematic review and meta-analysis.

BACKGROUND: There is a worldwide outbreak of COVID-19, as the number of patients increases. External treatment of traditional Chinese medicine includes acupuncture, massage, fire needle, cupping, and other alternative therapies. Currently, there are no relevant articles for systematic review. METHODS: We will search the randomized controlled trials related to the external treatment of traditional Chinese medicine (such as, acupuncture, massage, etc) and COVID-19 from inception to June 2020. The following database is our focus area: the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and Wan-Fang Database. All published randomized controlled trials in English or Chinese related to massage for COVID-19 will be included. Primary outcomes include the influence of external treatment of traditional Chinese medicine on the patients with COVID-19. Secondary outcomes include accompanying symptoms (such as myalgia, expectoration, stuffiness, runny nose, pharyngalgia, anhelation, chest distress, dyspnea, crackles, headache, nausea, vomiting, anorexia, diarrhea) disappear rate, negative COVID-19 results rate on 2 consecutive occasions (not on the same day), average hospitalization time, Clinical curative effect, and improved quality of life. RESULTS: The results will provide a high-quality synthesis of current evidence for researchers in this subject area. CONCLUSION: The conclusion of our study will provide evidence to judge whether external treatment of traditional Chinese medicine is an effective intervention on the patients with COVID-19. PROSPERO REGISTRATION NUMBER: CRD42020181336.

Immune dysfunction following COVID-19, especially in severe patients.

The coronavirus disease 2019 (COVID-19) has been spreading worldwide. Severe cases quickly progressed with unfavorable outcomes. We aim to investigate the clinical features of COVID-19 and identify the risk factors associated with its progression. Data of confirmed SARS-CoV-2-infected patients and healthy participants were collected. Thirty-seven healthy people and 79 confirmed patients, which include 48 severe patients and 31 mild patients, were recruited. COVID-19 patients presented with dysregulated immune response (decreased T, B, and NK cells and increased inflammatory cytokines). Also, they were found to have increased levels of white blood cell, neutrophil count, and D-dimer in severe cases. Moreover, lymphocyte, CD4+ T cell, CD8+ T cell, NK cell, and B cell counts were lower in the severe group. Multivariate logistic regression analysis showed that CD4+ cell count, neutrophil-to-lymphocyte ratio (NLR) and D-dimer were risk factors for severe cases. Both CT score and clinical pulmonary infection score (CPIS) were associated with disease severity. The receiver operating characteristic (ROC) curve analysis has shown that all these parameters and scores had quite a high predictive value. Immune dysfunction plays critical roles in disease progression. Early and constant surveillance of complete blood cell count, T lymphocyte subsets, coagulation function, CT scan and CPIS was recommended for early screening of severe cases.

COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients (preprint)

BACKGROUND Corona Virus Disease 2019 (COVID-19) is spreading worldwide. Effective screening for patients is important to limit the epidemic. However, some defects make the currently applied diagnosis methods are still not very ideal for early warning of patients. We aimed to develop a diagnostic model that allows for the quick screening of highly suspected patients using easy-to-get variables. METHODS A total of 1,311 patients receiving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleicacid detection were included, whom with a positive result were classified into COVID-19 group. Multivariate logistic regression analyses were performed to construct the diagnostic model. Receiver operating characteristic (ROC) curve analysis were used for model validation. RESULTS After analysis, signs of pneumonia on CT, history of close contact, fever, neutrophil-to-lymphocyte ratio (NLR), Tmax and sex were included in the diagnostic model. Age and meaningful respiratory symptoms were enrolled into COVID-19 early warning score (COVID-19 EWS). The areas under the ROC curve (AUROC) indicated that both of the diagnostic model (training dataset 0.956 [95%CI 0.935-0.977, P < 0.001]; validation dataset 0.960 [95%CI 0.919-1.0, P < 0.001] ) and COVID-19 EWS (training dataset 0.956 [95%CI 0.934-0.978, P < 0.001] ; validate dataset 0.966 [95%CI 0.929-1, P < 0.001]) had good discrimination capacity. In addition, we also obtained the cut-off values of disease severity predictors, such as CT score, CD8+ T cell count, CD4+ T cell count, and so on. CONCLUSIONS The new developed COVID-19 EWS was a considerable tool for early and relatively accurately warning of SARS-CoV-2 infected patients.