Deciphering suppressive effects of Lianhua Qingwen Capsule on COVID-19 and synergistic effects of its major botanical drug pairs.

The COVID-19 pandemic has resulted in excess deaths worldwide. Conventional antiviral medicines have been used to relieve the symptoms, with limited therapeutic effect. In contrast, Lianhua Qingwen Capsule is reported to exert remarkable anti-COVID-19 effect. The current review aims to: 1) uncover the main pharmacological actions of Lianhua Qingwen Capsule for managing COVID-19; 2) verify the bioactive ingredients and pharmacological actions of Lianhua Qingwen Capsule by network analysis; 3) investigate the compatibility effect of major botanical drug pairs in Lianhua Qingwen Capsule; and 4) clarify the clinical evidence and safety of the combined therapy of Lianhua Qingwen Capsule and conventional drugs. Numerous bioactive ingredients in Lianhu Qingwen, such as quercetin, naringenin, ß-sitosterol, luteolin, and stigmasterol, were identified to target host cytokines, and to regulate the immune defence in response to COVID-19. Genes including androgen receptor (AR), myeloperoxidase (MPO), epidermal growth factor receptor (EGFR), insulin (INS), and aryl hydrocarbon receptor (AHR) were found to be significantly involved in the pharmacological actions of Lianhua Qingwen Capsule against COVID-19. Four botanical drug pairs in Lianhua Qingwen Capsule were shown to have synergistic effect for the treatment of COVID-19. Clinical studies demonstrated the medicinal effect of the combined use of Lianhua Qingwen Capsule and conventional drugs against COVID-19. In conclusion, the four main pharmacological mechanisms of Lianhua Qingwen Capsule for managing COVID-19 are revealed. Therapeutic effect has been noted against COVID-19 in Lianhua Qingwen Capsule.

Characteristics of Surface Ozone and Nitrogen Oxides over a Typical City in the Yangtze River Delta, China

The Yangtze River Delta (YRD) is the most developed region in China. Influenced by intensive and complex anthropogenic activities, atmospheric pollution in this region is highly variable, and reports are sparse. In this study, a seven-year history of the atmospheric O3 and NOx mixing ratios over a typical city, Hangzhou, was presented to enrich the studies on air pollution in the YRD region. Our results revealed that the diurnal variation in NOx corresponded to traffic rush hours, while O3 was mainly impacted by photochemical reactions in the daytime. The weekend effect was significant for NOx, but inapparent for O3. Two O3 peaks in May and September were caused by seasonal atmospheric stability and climatic conditions. The lower NOx and higher O3 levels observed suggested direct effects from traffic restrictions and large-scale industrial shutdowns during the COVID-19 lockdown in 2020 compared with those in the periods before and after lockdown. The model simulation results showed that O3 mixing ratios were not only related to regional anthropogenic emissions but were impacted by air mass transportation from surrounding provinces and the China shelf seas. The NOx mixing ratios showed a decreasing trend, while the O3 mixing ratios showed the opposite trend from 2015 to 2021, which is indicative of the implementation of the Air Pollution Prevention and Control Acton Plan issued by the Chinese government in 2013. [ABSTRACT FROM AUTHOR] Copyright of Atmosphere is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Characteristics of the methane (CH4) mole fraction in a typical city and suburban site in the Yangtze River Delta, China

China is the largest emitter of greenhouse gases in the world. However, the atmospheric observation of greenhouse gases is relatively sparse. In this study, surface measurements of CH4 over 5 years at a typical city site (Hangzhou) in an economically developed region in China were conducted to study the temporal variations and the influence of meteorological factors and airmass transport. The CH4 observations from a suburban site (Lin'an station [LAN]) which is a World Meteorological Organization/Global Atmosphere Monitoring Program (WMO/GAW) regional site, were also compared. Our results showed that the atmospheric CH4 mole fraction in Hangzhou was not only affected by meteorological factors and topography, but also by strong local emissions. Although the distance between the two stations was only 50 km, there was a significant difference in the temporal CH4 variations. The strong anthropogenic emissions in the city were responsible for the urban-suburban site difference. The CH4 peaks in the diurnal cycles in Hangzhou corresponded to rush hours, and there were unique variations during special periods (i.e., the National Day holiday, coronavirus disease 2019 [COVID - 19] lock-down). It also led to an annual average CH4 mole fraction at the Hangzhou station (HZ) that was on average 111.1 ± 1.6 ppb higher than that at the LAN from 2016 to 2020. The lock-down measures caused by the outbreak of COVID - 19 decreased the atmospheric CH4 mole fractions by 6.8% in Hangzhou but only 1.9% in Lin'an in 2020 compared to those in 2019. Excluding the data in 2020, the annual growth rate of the CH4 mole fraction was 19.0 ppb yr−1 in Hangzhou. Our results indicated that the CH4 mole fraction in Hangzhou was mainly driven by local anthropogenic emissions, although they were influenced by emissions from surrounding cities such as Nanjing and Ningbo.

Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.

Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. However, the existing experimental techniques for detecting Kcr sites are cost-ineffective, to a great need for new computational methods to address this problem. We here describe Adapt-Kcr, an advanced deep learning model that utilizes adaptive embedding and is based on a convolutional neural network together with a bidirectional long short-term memory network and attention architecture. On the independent testing set, Adapt-Kcr outperformed the current state-of-the-art Kcr prediction model, with an improvement of 3.2% in accuracy and 1.9% in the area under the receiver operating characteristic curve. Compared to other Kcr models, Adapt-Kcr additionally had a more robust ability to distinguish between crotonylation and other lysine modifications. Another model (Adapt-ST) was trained to predict phosphorylation sites in SARS-CoV-2, and outperformed the equivalent state-of-the-art phosphorylation site prediction model. These results indicate that self-adaptive embedding features perform better than handcrafted features in capturing discriminative information; when used in attention architecture, this could be an effective way of identifying protein Kcr sites. Together, our Adapt framework (including learning embedding features and attention architecture) has a strong potential for prediction of other protein posttranslational modification sites.

Preparation and characterization of immobilized 5-HT1A receptor stationary phase for high throughput screening of the receptor-binding ligands from complex systems like Curcuma wenyujin Y. H. Chen et C. Ling extract.

The incidence of depression has increased significantly during the COVID-19 pandemic. This disease is closely associated with serotonin 1A (5-HT1A) receptor and often treated by complex prescription containing Curcuma wenyujin Y. H. Chen et C. Ling. Therefore, we hypothesized that this herb contains bioactive compounds specially binding to the receptor. However, the rapid discovery of new ligands of 5-HT1A receptor is still challenging due to the lack of efficient screening methods. To address this problem, we developed and characterized a novel approach for the rapid screening of ligands by using immobilized 5-HT1A receptor as the chromatographic stationary phase. Briefly, haloalkane dehalogenase was fused at the C-terminal of 5-HT1A receptor, and the modified 5-HT1A receptor was immobilized on amino-microspheres by the reaction between haloalkane dehalogenase and 6-chlorohexanoic acid linker. Scanning electron microscope and X-ray photo-electron were used to characterize the morphology and element of the immobilized receptor. The binding of three specific ligands to 5-HT1A receptor was investigated by two different methods. Moreover, we examined the feasibility of 5-HT1A receptor colume in high throughput screening of new ligands from complex systems as exemplified by Curcuma wenyujin Y. H. Chen et C. Ling. Gweicurculactone, 2-hydroxy-1-(3,4-dihydroxybenzene)-7-(4'-hydroxybezene)-heptane and curcuminol F were identified as the ligands of 5-HT1A receptor with the binding energies of -7.06 kcal/mol, -7.77 kcal/mol and -5.26 kcal/mol, respectively. Collectively, these results indicated that the immobilized 5-HT1A receptor was capable of screening bioactive compound from complex system, providing an effective methodology for high throughput screening.

Ethical dilemmas faced by frontline support nurses fighting COVID-19.

BACKGROUND: In 2019, an outbreak of COVID-19 broke out in Hubei, China. Medical workers from all over the country rushed to Hubei and participated in the treatment and care of COVID-19 patients. These nurses, dedicated to their professional practice, volunteered to provide compassion and expert clinical care during the pandemic. As with other acts of heroism, the ethical dilemmas associated with working on the front line must be considered for future practice. PURPOSE: To explore the ethical dilemmas of frontline nurses of Jiangsu Province in China during deployment to Wuhan to fight the novel coronavirus pneumonia, and to provide a basis for developing strategies to help nursing staff address personal and practice concerns in order to work more effectively during this pandemic and other disasters in the future. RESEARCH DESIGN AND METHOD: Using the phenomenological research method and the purpose sampling method, semi-structured interviews were conducted with 10 nurses, post-deployment to Wuhan, who had worked on the front line to fight the novel coronavirus. ETHICAL CONSIDERATIONS: The research proposal was approved by the Research Ethic Committee of Yangzhou University, China. FINDINGS: From the analysis of the interviews of the 10 participants, three main themes were identified: ethical dilemmas in clinical nursing, ethical dilemmas in interpersonal relationships, and ethical dilemmas in nursing management. CONCLUSION: During a quick response to public health emergencies, where nurses are deployed immediately as a call to action, the issues surrounding ethical dilemmas from several perspectives must be considered. This research suggests that a team approach to proactive planning and open communication during the emergency is an efficient and productive strategy to improve the nurses' experience and sense of well-being.

CANPT Score: A Tool to Predict Severe COVID-19 on Admission.

Background and Aims: Patients with critical coronavirus disease 2019 (COVID-19) have a mortality rate higher than 50%. The purpose of this study was to establish a model for the prediction of the risk of severe disease and/or death in patients with COVID-19 on admission. Materials and Methods: Patients diagnosed with COVID-19 in four hospitals in China from January 22, 2020 to April 15, 2020 were retrospectively enrolled. The demographic, laboratory, and clinical data of the patients with COVID-19 were collected. The independent risk factors related to the severity of and death due to COVID-19 were identified with a multivariate logistic regression; a nomogram and prediction model were established. The area under the receiver operating characteristic curve (AUROC) and predictive accuracy were used to evaluate the model's effectiveness. Results: In total, 582 patients with COVID-19, including 116 patients with severe disease, were enrolled. Their comorbidities, body temperature, neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, and levels of total bilirubin (Tbil), creatinine (Cr), creatine kinase (CK), and albumin (Alb) were independent risk factors for severe disease. A nomogram was generated based on these eight variables with a predictive accuracy of 85.9% and an AUROC of 0.858 (95% CI, 0.823-0.893). Based on the nomogram, the CANPT score was established with cut-off values of 12 and 16. The percentages of patients with severe disease in the groups with CANPT scores <12, ≥12, and <16, and ≥16 were 4.15, 27.43, and 69.64%, respectively. Seventeen patients died. NLR, Cr, CK, and Alb were independent risk factors for mortality, and the CAN score was established to predict mortality. With a cut-off value of 15, the predictive accuracy was 97.4%, and the AUROC was 0.903 (95% CI 0.832, 0.974). Conclusions: The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.

Clinical Characteristics of 33 Asymptomatic COVID-19 Infections in Wuhan, China.

Clinical characteristics of 33 asymptomatic COVID-19 infections were analyzed in this study. The data showed most of asymptomatic patients had small body mass index, good prognosis and low infectivity. This study suggests that screening from high-risk populations to find and isolate asymptomatic patients is an important disease prevention and control strategy for COVID-19.

Immunomodulation for Severe COVID-19 Pneumonia: The State of the Art.

COVID-19 has become a worldwide pandemic caused by the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Severe cases of COVID-19 have accounted for 10-20% of all infections, leading to more than 500,000 deaths. Increasing evidence has suggested that the inflammatory cytokine storm originating from the anti-SARS-CoV-2 immune response plays an important role in the pathogenesis of critically ill patients with COVID-19, which leads to mixed antagonistic response syndrome (MARS). In the early stage of severe COVID-19, systemic inflammatory response syndrome causes acute respiratory distress syndrome, multiple organ dysfunction syndrome, and even multiple organ failure. In the late stage of severe disease, increased production of anti-inflammatory cytokines drives the immune response to become dominated by compensatory anti-inflammatory response syndrome, which leads to immune exhaustion and susceptibility to secondary infections. Therefore, precise immunomodulation will be beneficial for patients with severe COVID-19, and immunosuppressive or immune enhancement therapy will depend on the disease course and immune status. This review summarizes the current understanding of the immunopathogenesis of severe COVID-19, especially the role of the inflammatory cytokine storm in disease progression. Immune indicators and immunotherapy strategies for severe COVID-19 are reviewed and the potential implications discussed.

A Risk Prediction Model for Evaluating the Disease Progression of COVID-19 Pneumonia.

Background and Objective: The epidemic of coronavirus disease 2019 (COVID-19) pneumonia caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has expanded from China throughout the world. This study aims to estimate the risk of disease progression of patients who have been confirmed with COVID-19. Methods: Meta-analysis was performed in existing literatures to identify risk factors associated with COVID-19 pneumonia progression. Patients with COVID-19 pneumonia were admitted to hospitals in Wuhan or Hangzhou were retrospectively enrolled. The risk prediction model and nomogram were developed from Wuhan cohort through logistic regression algorithm, and then validated in Hangzhou and Yinchuan cohorts. Results: A total of 270 patients admitted to hospital between Dec 30, 2019, and Mar 30, 2020, were retrospectively enrolled (Table 1). The development cohort (Wuhan cohort) included 87 (43%) men and 115 (57%) women, and the median age was 53 years old. Hangzhou validation cohort included 20 (48%) men and 22 (52%) women, and the median age was 59 years old. Yinchuan validation cohort included 12 (46%) men and 14 (54%) women, and the median age was 44 years old. The meta-analysis along with univariate logistic analysis in development cohort have shown that age, fever, diabetes, hypertension, CREA, BUN, CK, LDH, and neutrophil count were significantly associated with disease progression of COVID-19 pneumonia. The model and nomogram derived from development cohort show good performance in both development and validation cohorts. Conclusion: The severe COVID-19 pneumonia is associated with various types of risk factors including age, fever, comorbidities, and some laboratory examination indexes. The model integrated with these factors can help to evaluate the disease progression of COVID-19 pneumonia.

Outcomes of novel coronavirus disease 2019 (COVID-19) infection in 107 patients with cancer from Wuhan, China.

BACKGROUND: Patients with cancer have a higher risk of coronavirus disease 2019 (COVID-19) than noncancer patients. The authors conducted a multicenter retrospective study to investigate the clinical manifestations and outcomes of patients with cancer who are diagnosed with COVID-19. METHODS: The authors reviewed the medical records of hospitalized patients who were treated at 5 hospitals in Wuhan City, China, between January 5 and March 18, 2020. Clinical parameters relating to cancer history (type and treatment) and COVID-19 were collected. The primary outcome was overall survival (OS). Secondary analyses were the association between clinical factors and severe COVID-19 and OS. RESULTS: A total of 107 patients with cancer were diagnosed with COVID-19, with a median age of 66 years (range, 37-98 years). Lung (21 patients; 19.6%), gastrointestinal (20 patients; 18.7%), and genitourinary (20 patients; 18.7%) cancers were the most common cancer diagnoses. A total of 37 patients (34.6%) were receiving active anticancer treatment when diagnosed with COVID-19, whereas 70 patients (65.4%) were on follow-up. Overall, 52.3% of patients (56 patients) developed severe COVID-19; this rate was found to be higher among patients receiving anticancer treatment than those on follow-up (64.9% vs 45.7%), which corresponded to an inferior OS in the former subgroup of patients (hazard ratio, 3.365; 95% CI, 1.455-7.782 [P = .005]). The detrimental effect of anticancer treatment on OS was found to be independent of exposure to systemic therapy (case fatality rate of 33.3% [systemic therapy] vs 43.8% [nonsystemic therapy]). CONCLUSIONS: The results of the current study demonstrated that >50.0% of infected patients with cancer are susceptible to severe COVID-19. This risk is aggravated by simultaneous anticancer treatment and portends for a worse survival, despite treatment for COVID-19.