Management strategies for pediatric wards of designated hospitals for omicron variant BA.5.1.3 infection in south area of Hainan

OBJECTIVE: To describe the experience of comprehensive management of pediatric COVID-19 cases in designated hospitals under 'mixed management mode' during the '0801' epidemic in Sanya. METHODS: A pediatric comprehensive ward of children and parents has been specially opened in the designated hospitals to carry out comprehensive treatment and management of pediatric children. RESULTS: From Aug 1, 2022 to Oct 1, 2022, Sanya Central Hospital(Hainan Third People's Hospital), as the designated hospital for the treatment of COVID-19 cases in South area of Hainan, a total of 251 children aged between 0 and 14 year old and 195 aged more than 14 years old were treated for COVID-19 in the pediatric comprehensive wards. Among children under 14 years old, including 10(4.0%) neonates aged between 0 and 28 days, 32(12.7%) cases aged between 29 days and 1 year old, 87(34.7%) cases aged between 1 and 3 years old and 122(48.6%) cases aged between 3 and 14 years old. There were 16(6.4%) cases of common type, 223(88.8%) cases of mild type, 12(4.8%)asymptomatic cases and 7(2.8%) re-positive cases. There were 246(98.0%) children with accompanying care and 5(2.0%) children without accompanying care and 23(9.2%) children complicated with underlying diseases. The youngest age was 11 days, and the oldest age was 77 years old. The large age span and poor compliance with treatment brought unprecedented challenges to the diagnosis and treatment of children in the designated hospitals. The mixed model of comprehensive management and multidisciplinary diagnosis and treatment helped to achieve the ultimate goals of 'zero death of patients and 'zero infection' of health care workers. CONCLUSION: The comprehensive management means such as optimization of flow, stress on prevention and control of nosocomial infection, multidisciplinary collaboration and support of information platform offer strong guarantee for the mixed mode of management as well as scientific experience for centralized diagnosis and treatment of respiratory infectious diseases in children.

The association between team resilience and team performance in nurses during COVID-19 pandemic: a network analysis.

BACKGROUND: Compared to other healthcare workers, nurses are more vulnerable to the potentially devastating effects of pandemic-related stressors. Studies have not yet investigated the deeper characteristics of the relationship between team resilience and team performance among nurses during the COVID-19 pandemic. This study aimed to elucidate the characteristics of team resilience and performance networks among nurses during the pandemic. METHODS: A cross-sectional study involving 118 nursing teams comprising 1627 practice nurses from four tertiary-A and secondary-A hospitals in Shandong Province, China, was conducted. Analyzing and Developing Adaptability and Performance in Teams to Enhance Resilience Scale and the Team Effectiveness Scale were used to measure team resilience and performance, respectively. The estimation of the network model and calculation of related metrics, network stability and accuracy, and network comparison tests were performed using R 4.0.2. RESULTS: Node monitoring had the highest centralities in the team resilience and performance network model, followed by node anticipation, cooperation satisfaction, and cooperation with other departments. Moreover, node cooperation satisfaction and learning had the highest levels of bridge centrality in the entire network. CONCLUSION: Monitoring, anticipation, cooperation satisfaction, cooperation with other departments, and learning constituted core variables maintaining the team resilience-performance network structure of nurses during the pandemic. Clinical interventions targeting core variables may be effective in maintaining or promoting both team resilience and performance in this population.

Optimization of Population-Level Testing, Contact Tracing, and Isolation in Emerging COVID-19 Outbreaks: a Mathematical Modeling Study - Tonghua City and Beijing Municipality, China, 2021-2022.

Introduction: The transmissibility of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant poses challenges for the existing measures containing the virus in China. In response, this study investigates the effectiveness of population-level testing (PLT) and contact tracing (CT) to help curb coronavirus disease 2019 (COVID-19) resurgences in China. Methods: Two transmission dynamic models (i.e. with and without age structure) were developed to evaluate the effectiveness of PLT and CT. Extensive simulations were conducted to optimize PLT and CT strategies for COVID-19 control and surveillance. Results: Urban Omicron resurgences can be controlled by multiple rounds of PLT, supplemented by CT - as long as testing is frequent. This study also evaluated the time needed to detect COVID-19 cases for surveillance under different routine testing rates. The results show that there is a 90% probability of detecting COVID-19 cases within 3 days through daily testing. Otherwise, it takes around 7 days to detect COVID-19 cases at a 90% probability level if biweekly testing is used. Routine testing applied to the age group 21-60 for COVID-19 surveillance would achieve similar performance to that applied to all populations. Discussion: Our analysis evaluates potential PLT and CT strategies for COVID-19 control and surveillance.

Predictive Model for National Minimal CFR during Spontaneous Initial Outbreak of Emerging Infectious Disease: Lessons from COVID-19 Pandemic in 214 Nations and Regions.

The minimal case fatality rate (CFR) is one of the essential fundaments for the establishment of a diverse national response strategy against the COVID-19 epidemic, but cannot be quantitatively predicted. The aim of the present study was to explore the applicable quantitative parameters labeling integrating responding capacity from national daily CFR curves, and whether the minimal CFR during initial emerging epidemic outbreaks can be predicted. We analyzed data from 214 nations and regions during the initial 2020 COVID-19 epidemic and found similar falling zones marked with two turning points within a fitting three-day-moving CFR curve which occurred for many nations and regions. The turning points can be quantified with parameters for the day duration (T1, T2, and ΔT) and for the three-day moving arithmetic average CFRs (CFR1, CFR2, and ΔCFR) under wave theory for 71 nations and regions after screening. Two prediction models of minimal CFR were established with multiple linear regressions (M1) and multi-order curve regressions (M2) after internal and external evaluation. Three kinds of falling zones could be classified in the other 71 nations and regions. Only the minimal CFR showed significant correlations with nine independent national indicators in 65 nations and regions with CFRs less than 7%. Model M1 showed that logarithmic population, births per 1000 people, and household size made significant positive contributions, and logarithmic GDP, percentage of population aged 65+ years, domestic general government health expenditure, physicians per 1000 people, nurses per 1000 people, and body mass index made negative contributions to the minimal CFR against COVID-19 epidemics for most nations and regions. The spontaneous minimal CFR was predicted well with model M1 for 57 nations and regions based on the nine national indicators (R2 = 0.5074), or with model M2 for 59 nations and regions based on the nine national indicators (R2 = 0.8008) at internal evaluation. The study confirmed that national spontaneous minimal CFR could be predicted with models successfully for most nations and regions against COVID-19 epidemics, which provides a critical method to predict the essential early evidence to evaluate the integrating responding capacity and establish national responding strategies reasonably for other emerging infectious diseases in the future.

Knowledge Graph: Applications in Tracing the Source of Large-Scale Outbreak - Beijing Municipality, China, 2020-2021.

Introduction: Tracing transmission paths and identifying infection sources have been effective in curbing the spread of coronavirus disease 2019 (COVID-19). However, when facing a large-scale outbreak, this is extremely time-consuming and labor-intensive, and resources for infection source tracing become limited. In this study, we aimed to use knowledge graph (KG) technology to automatically infer transmission paths and infection sources. Methods: We constructed a KG model to automatically extract epidemiological information and contact relationships from case reports. We then used an inference engine to identify transmission paths and infection sources. To test the model's performance, we used data from two COVID-19 outbreaks in Beijing. Results: The KG model performed well for both outbreaks. In the first outbreak, 20 infection relationships were identified manually, while 42 relationships were determined using the KG model. In the second outbreak, 32 relationships were identified manually and 31 relationships were determined using the KG model. All discrepancies and omissions were reasonable. Discussion: The KG model is a promising tool for predicting and controlling future COVID-19 epidemic waves and other infectious disease pandemics. By automatically inferring the source of infection, limited resources can be used efficiently to detect potential risks, allowing for rapid outbreak control.

Interruption of Influenza Transmission under Public Health Emergency Response for COVID-19: a Study Based on Real-World Data from Beijing, China.

To estimate the effect of the corona virus disease 2019 (COVID-19) control measures taken to mitigate community transmission in many regions, we analyzed data from the influenza surveillance system in Beijing from week 27 of 2014 to week 26 of 2020. We collected weekly numbers of influenza-like illness (ILI) cases, weekly positive proportion of ILI cases, weekly ILI case proportion in outpatients, and the dates of implementation of COVID-19 measures. We compared the influenza activity indicators of the 2019/2020 season with the preceding five seasons and built two ARIMAX models to estimate the effectiveness of COVID-19 measures declared since January 24, 2020 by the emergency response. Based on the observed data, compared to the preceding five influenza seasons, ILIs, positive proportion of ILIs, and duration of the influenza epidemic period in 2019/2020 had increased from 13% to 54%; in particular, the number of weeks from the peak to the end of the influenza epidemic period had decreased from 12 to 1. According to ARIMAX model forecasting, after considering natural decline, weekly ILIs had decreased by 48.6%, weekly positive proportion had dropped by 15% in the second week after the emergency response was declared, and COVID-19 measures had reduced by 83%. We conclude that the public health emergency response can significantly interrupt the transmission of influenza.

Identifying Drug-Induced Liver Injury Associated With Inflammation-Drug and Drug-Drug Interactions in Pharmacologic Treatments for COVID-19 by Bioinformatics and System Biology Analyses: The Role of Pregnane X Receptor

Of the patients infected with coronavirus disease 2019 (COVID-19), approximately 14–53% developed liver injury resulting in poor outcomes. Drug-induced liver injury (DILI) is the primary cause of liver injury in COVID-19 patients. In this study, we elucidated liver injury mechanism induced by drugs of pharmacologic treatments against SARS-CoV-2 (DPTS) using bioinformatics and systems biology. Totally, 1209 genes directly related to 216 DPTS (DPTSGs) were genes encoding pharmacokinetics and therapeutic targets of DPTS and enriched in the pathways related to drug metabolism of CYP450s, pregnane X receptor (PXR), and COVID-19 adverse outcome. A network, constructed by 110 candidate targets which were the shared part of DPTSGs and 445 DILI targets, identified 49 key targets and four Molecular Complex Detection clusters. Enrichment results revealed that the 4 clusters were related to inflammatory responses, CYP450s regulated by PXR, NRF2-regualted oxidative stress, and HLA-related adaptive immunity respectively. In cluster 1, IL6, IL1B, TNF, and CCL2 of the top ten key targets were enriched in COVID-19 adverse outcomes pathway, indicating the exacerbation of COVID-19 inflammation on DILI. PXR-CYP3A4 expression of cluster 2 caused DILI through inflammation-drug interaction and drug-drug interactions among pharmaco-immunomodulatory agents, including tocilizumab, glucocorticoids (dexamethasone, methylprednisolone, and hydrocortisone), and ritonavir. NRF2 of cluster 3 and HLA targets of cluster four promoted DILI, being related to ritonavir/glucocorticoids and clavulanate/vancomycin. This study showed the pivotal role of PXR associated with inflammation-drug and drug-drug interactions on DILI and highlighted the cautious clinical decision-making for pharmacotherapy to avoid DILI in the treatment of COVID-19 patients.

Investment strategy for blockchain technology in a shipping supply chain.

In the post-COVID-19 pandemic era, how to promote blockchain technology to improve the efficiency of port customs clearance and logistics transparency has become a hot research question in the shipping industry. In this paper, we investigate the value of blockchain-based vertical cooperation led by a port or a shipping company in a one-to-two shipping service competition model. A status quo scenario and two different investment scenarios led by different stakeholders are constructed, and equilibrium solutions of the Stackelberg game in three scenarios are proposed. Meanwhile, consumer surplus and social welfare under different cooperation frameworks are discussed. We find that i) investment in blockchain technology can significantly increase the profits of shipping supply chain participants. ii) From the point of view of profit, when the investment efficiency of the port and the shipping company satisfies a certain relationship, there is a balanced strategy for both parties to invest in blockchain technology. iii) The more intense the competition for the services of shipping companies, the lower the level of blockchain technology to improve the logistics capabilities of the shipping supply chain participants. iv) The port's investment in blockchain technology brings more consumer surplus and social welfare. The abovementioned findings can provide managerial insights for ports and shipping companies and present decision support for the government to formulate blockchain technology promotion policies.

Development and Validation of a Nomogram for Predicting the Risk of Coronavirus-Associated Acute Respiratory Distress Syndrome: A Retrospective Cohort Study.

Background: Since the outbreak of coronavirus disease (COVID-19) in December 2019 in Wuhan, it has spread rapidly worldwide. We aimed to establish and validate a nomogram that predicts the probability of coronavirus-associated acute respiratory distress syndrome (CARDS). Methods: In this single-centre, retrospective study, 261 patients with COVID-19 were recruited using positive reverse transcription-polymerase chain reaction tests for severe acute respiratory syndrome coronavirus 2 in Tongji Hospital at Huazhong University of Science and Technology (Wuhan, China). These patients were randomly distributed into the training cohort (75%) and the validation cohort (25%). The factors included in the nomogram were determined using univariate and multivariate logistic regression analyses based on the training cohort. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency of the nomogram in the training and validation cohorts. Results: Independent predictive factors, including fasting plasma glucose, platelet, D-dimer, and cTnI, were determined using the nomogram. In the training cohort, the AUC and concordance index were 0.93. Similarly, in the validation cohort, the nomogram still showed great distinction (AUC: 0.92) and better calibration. The calibration plot also showed a high degree of agreement between the predicted and actual probabilities of CARDS. In addition, the DCA proved that the nomogram was clinically beneficial. Conclusion: Based on the results of laboratory tests, we established a predictive model for acute respiratory distress syndrome in patients with COVID-19. This model shows good performance and can be used clinically to identify CARDS early. Trial Registration: Ethics committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (No.:(2020) Linlun-34th).

The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China.

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above -7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as "warning cities") have more days of "dangerous weather", which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.

Factors Associated with SARS-CoV-2 Repeat Positivity - Beijing, China, June-September 2020.

INTRODUCTION: Repeat positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) following COVID-19 initial viral clearance (re-positivity) poses a public health management challenge. The objective was to determine factors associated with neutralizing antibody (Nab) level and re-positivity among patients infected with a single strain SARS-CoV-2. METHODS: During a single strain SARS-CoV-2 cluster in Beijing, China, longitudinal individual clinical, virological, and immunological data were collected from 368 infections from June 13 to September 22, 2020. Factors associated with Nab level and re-positivity were analyzed using generalized estimating equations. RESULTS: A total of 353 (96%) SARS-CoV-2 infections had demographic, clinical, and laboratory data available. Among the 353 infections, 55 (15.5%) were re-positive, and blood draws were taken from 346 individuals (98.0%) during hospitalization and/or during the follow-up period. Symptoms were milder for the second-time admission for the re-positives, although 36.4% of re-positives presented with radiographic appearance of pneumonia manifestation. Compared to non-re-positive patients, NAb titers were lower among re-positives; NAb was positively associated with clinical severity. Samples from the lower respiratory tract manifested higher viral load than that from the upper respiratory tract. Multivariable analysis showed re-positivity was positively associated with being female [odd ratio (OR)=1.7, 95% confidence interval (CI) 1.1-2.8] and being aged <18 years (OR=5.2, 95% CI 1.5-18.1); having initially asymptomatic infection (OR=13.7, 95% CI 1.6-116.3); and negatively associated with a higher NAb level (OR=0.9, 95% CI 0.5-1.7). CONCLUSIONS: NAb may be important for sustained viral clearance. Lower respiratory tract infection was associated with higher viral load among all infections when compared to upper respiratory tract infection. Continuous lower respiratory and intermittent upper respiratory viral shedding among COVID-19 infections may occur.

Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China.

OBJECTIVES: The aim of the study was to reconstruct the complete transmission chain of the COVID-19 outbreak in Beijing's Xinfadi Market using data from epidemiological investigations, which contributes to reflecting transmission dynamics and transmission risk factors. METHODS: We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates. RESULTS: The rate of transmission of COVID-19 within households is 9.2%. Older people are more susceptible to infection. The accuracy of our reconstructed transmission chain was 67.26%. In the gathering place of this outbreak, the Beef and Mutton Trading Hall of Xinfadi market, most of the transmission occurs within 20 m, only 19.61% of the transmission occurs over a wider area (>20 m), with an overall average transmission distance of 13.00 m. The deepest transmission generation is 9. In this outbreak, there were 2 abnormally high transmission events. CONCLUSIONS: The statistical method of reconstruction of transmission trees from incomplete epidemic data provides a valuable tool to help understand the complex transmission factors and provides a practical guideline for investigating the characteristics of the development of epidemics and the formulation of control measures.

Structure Based Affinity Maturation and Characterizing of SARS-CoV Antibody CR3022 against SARS-CoV-2 by Computational and Experimental Approaches.

The COVID-19 epidemic is raging around the world. Neutralizing antibodies are powerful tools for the prevention and treatment of SARS-CoV-2 infection. Antibody CR3022, a SARS-CoV neutralizing antibody, was found to cross-react with SARS-CoV-2, but its affinity was lower than that of its binding with SARS-CoV, which greatly limited the further development of CR3022 against SARS-CoV-2. Therefore, it is necessary to improve its affinity to SARS-CoV-2 in vitro. In this study, the structure-based molecular simulations were utilized to virtually mutate the possible key residues in the complementarity-determining regions (CDRs) of the CR3022 antibody. According to the criteria of mutation energy, the mutation sites that have the potential to impact the antibody affinity were then selected. Then optimized CR3022 mutants with the enhanced affinity were further identified and verified by enzyme-linked immunosorbent assay (ELISA), surface plasma resonance (SPR) and autoimmune reactivity experiments. Finally, molecular dynamics (MD) simulation and binding free energy calculation (MM/PBSA) were performed on the wild-type CR3022 and its two double-site mutants to understand in more detail the contribution of these sites to the higher affinity. It was found that the binding affinity of the CR3022 antibody could be significantly enhanced more than ten times after the introduction of the S103F/Y mutation in HCDR-3 and the S33R mutation in LCDR-1. The additional hydrogen-bonding, hydrophobic interactions, as well as salt-bridges formed between the modified double-site mutated antibody and SARS-CoV-2 RBD were identified. The computational and experimental results clearly demonstrated that the affinity of the modified antibody has been greatly enhanced. This study indicates that CR3022 as a neutralizing antibody recognizing the conserved region of RBD against SARS-CoV with cross-reactivity with SARS-CoV-2, a different member in a large family of coronaviruses, could be improved by the computational and experimental approaches which provided insights for developing antibody drugs against SARS-CoV-2.

A retrospective on the intellectual adventures of think tanks in biosecurity before and after the COVID-19 pandemic outbreak.

Think tanks play a fundamental role in shaping policy agendas in Western countries, especially in the US. As international biosecurity is turning from a moderate to a serious concern, the convergence of biosecurity subjects and think tanks is evidently increasing. Examining the involvement and implication of think tanks in biosecurity policy formulation domestically and internationally is, therefore, of great value. This article takes a brief look at the intellectual output of over 30 think tanks during the last five years, before and after the outbreak of COVID-19, and tries to build an understanding of the extent to which these think tanks informed strategic, operational, and tactical decisions, with the aim of providing a better basis for dealing with sophisticated biological threats.

Effects of SARS-CoV-2 Infection During Late Pregnancy on Early Childhood Development: A Prospective Cohort Study.

Background: There is little direct or indirect evidence of the effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy on early childhood development. Methods: We conducted a prospective, observational cohort study in China from May 1 to October 31, 2020, that enrolled 135 mother-infant dyads: 57 dyads in the infection cohort and 78 in the non-infection cohort. Among all infants, 14.0% were preterm birth in the infection cohort and 6.4% in the non-infection cohort. Participants were followed by telephone interviews to collect demographic characteristics, medical records of coronavirus disease 2019, breastfeeding data, and early childhood development was assessed by the Age and Stage Questionnaire (ASQ-3) and Age and Stage Questionnaire Social-Emotional (ASQ:SE-2) Chinese versions at 3 months after childbirth. We used multivariable Poisson regression models to estimate the relative risk (RR) of SARS-CoV-2 infection. Multivariable linear regression models and a mediation model were used to test the direct and indirect associations between SARS-CoV-2 infection and the ASQ-3 score. This study was approved by the Peking University Third Hospital Medical Science Research Ethics Committee (No. IRB00006761-M2020127). Results: In the infection cohort, 13.6% of the children showed social-emotional developmental delay, and 13.5% showed overall developmental delay. The corresponding rates in the non-infection cohort were 23.4 and 8.1%. Compared with the non-infection cohort, SARS-CoV-2 infection during pregnancy did not increase the risk of social-emotional (RR = 0.87, 95% CI: 0.51-1.49) or overall (RR = 1.02, 95% CI: 0.60-1.73) developmental delay. The mediation model showed that SARS-CoV-2 infection indirectly affected the ASQ-3 score by increasing the length of mother-infant separation. Conclusions: SARS-CoV-2 during late pregnancy did not increase the risk of developmental delay of the offspring 3 months after delivery. However, SARS-CoV-2 may have indirect effects on early childhood development by increasing mother-infant separation.

Computational Simulation of HIV Protease Inhibitors to the Main Protease (Mpro) of SARS-CoV-2: Implications for COVID-19 Drugs Design.

SARS-CoV-2 is highly homologous to SARS-CoV. To date, the main protease (Mpro) of SARS-CoV-2 is regarded as an important drug target for the treatment of Coronavirus Disease 2019 (COVID-19). Some experiments confirmed that several HIV protease inhibitors present the inhibitory effects on the replication of SARS-CoV-2 by inhibiting Mpro. However, the mechanism of action has still not been studied very clearly. In this work, the interaction mechanism of four HIV protease inhibitors Darunavir (DRV), Lopinavir (LPV), Nelfinavir (NFV), and Ritonavire (RTV) targeting SARS-CoV-2 Mpro was explored by applying docking, molecular dynamics (MD) simulations, and MM-GBSA methods using the broad-spectrum antiviral drug Ribavirin (RBV) as the negative and nonspecific control. Our results revealed that LPV, RTV, and NFV have higher binding affinities with Mpro, and they all interact with catalytic residues His41 and the other two key amino acids Met49 and Met165. Pharmacophore model analysis further revealed that the aromatic ring, hydrogen bond donor, and hydrophobic group are the essential infrastructure of Mpro inhibitors. Overall, this study applied computational simulation methods to study the interaction mechanism of HIV-1 protease inhibitors with SARS-CoV-2 Mpro, and the findings provide useful insights for the development of novel anti-SARS-CoV-2 agents for the treatment of COVID-19.

Application of multivariate statistical analysis and network pharmacology to explore the mechanism of Danggui Liuhuang Tang in treating perimenopausal syndrome.

ETHNOPHARMACOLOGICAL RELEVANCE: Danggui Liuhuang Tang (DGLHT), first recorded in "Lan-Shi-Mi-Cang" (written in 1276 AD), is a famous classical formula. In 2018, it was listed in the Catalogue of Ancient Classic and Famous Prescriptions (First Batch) formulated by the National Administration of Traditional Chinese Medicine and the National Medical Products Administration. Perimenopausal syndrome (PMS) refers to a series of syndromes with autonomic nervous system dysfunction and neuropsychological symptoms. The treatment of PMS demands non-hormonal drugs. Natural products are considered to be effective substitutes for the treatment of PMS. It is reported that DGLHT has not only good therapeutic effects but also higher safety and fewer side effects in the treatment of PMS. However, the mechanism of DGLHT in treating PMS is not clear. AIM OF THE STUDY: To explore the chemical basis and the mechanism of DGLHT in treating PMS. MATERIALS AND METHODS: Multivariate statistical analysis was used to analyze the difference of components in supernatant before and after compatibility of DGLHT based on LC-MS data. The qualitative analysis was performed on the precipitate formed in the decocting process using LC-MS while the quantitative analysis on the potential markers using LC-UV. Then, the potential markers were analyzed by network pharmacology. The regulatory effect of DGLHT on FSH, P and E2 were carried out in PMS rats. RESULTS: Five potential markers, epiberberine, coptisine, palmatine, berberine and baicalin, were screened from the analysis of compounds in the supernatant. Four complexes, composed of potential marker monomers, were identified in the sediment, including two that have not been reported. The key targets of potential markers include TNF, NOS3, EGFR, ESR1, PTGS2, AR, CDC42 and RPS6KB1. The top signaling pathways include the cGMP-PKG signaling pathway, PI3K-Akt signaling pathway and estrogen signaling pathway. DGLHT could call back the hormone levels of P and E2 in PMS rats. CONCLUSION: DGLHT active ingredients, epiberberine, coptisine, palmatine, berberine and baicalin contribute a lot to the therapeutic effect. And DGLHT takes effect by regulating hormones secreted by the ovary.

Structural Basis of a Human Neutralizing Antibody Specific to the SARS-CoV-2 Spike Protein Receptor-Binding Domain.

The emerging new lineages of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have marked a new phase of coronavirus disease 2019 (COVID-19). Understanding the recognition mechanisms of potent neutralizing monoclonal antibodies (NAbs) against the spike protein is pivotal for developing new vaccines and antibody drugs. Here, we isolated several monoclonal antibodies (MAbs) against the SARS-CoV-2 spike protein receptor-binding domain (S-RBD) from the B cell receptor repertoires of a SARS-CoV-2 convalescent. Among these MAbs, the antibody nCoV617 demonstrates the most potent neutralizing activity against authentic SARS-CoV-2 infection, as well as prophylactic and therapeutic efficacies against the human angiotensin-converting enzyme 2 (ACE2) transgenic mouse model in vivo. The crystal structure of S-RBD in complex with nCoV617 reveals that nCoV617 mainly binds to the back of the "ridge" of RBD and shares limited binding residues with ACE2. Under the background of the S-trimer model, it potentially binds to both "up" and "down" conformations of S-RBD. In vitro mutagenesis assays show that mutant residues found in the emerging new lineage B.1.1.7 of SARS-CoV-2 do not affect nCoV617 binding to the S-RBD. These results provide a new human-sourced neutralizing antibody against the S-RBD and assist vaccine development. IMPORTANCE COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has posed a serious threat to global health and the economy, so it is necessary to find safe and effective antibody drugs and treatments. The receptor-binding domain (RBD) in the SARS-CoV-2 spike protein is responsible for binding to the angiotensin-converting enzyme 2 (ACE2) receptor. It contains a variety of dominant neutralizing epitopes and is an important antigen for the development of new coronavirus antibodies. The significance of our research lies in the determination of new epitopes, the discovery of antibodies against RBD, and the evaluation of the antibodies' neutralizing effect. The identified antibodies here may be drug candidates for the development of clinical interventions for SARS-CoV-2.