Continuous evolution and emerging lineage of seasonal human coronaviruses: A multicenter surveillance study.

The seasonal human coronaviruses (HCoVs) have zoonotic origins, repeated infections, and global transmission. The objectives of this study are to elaborate the epidemiological and evolutionary characteristics of HCoVs from patients with acute respiratory illness. We conducted a multicenter surveillance at 36 sentinel hospitals of Beijing Metropolis, China, during 2016-2019. Patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) were included, and submitted respiratory samples for screening HCoVs by multiplex real-time reverse transcription-polymerase chain reaction assays. All the positive samples were used for metatranscriptomic sequencing to get whole genomes of HCoVs for genetical and evolutionary analyses. Totally, 321 of 15 677 patients with ILI or SARI were found to be positive for HCoVs, with an infection rate of 2.0% (95% confidence interval, 1.8%-2.3%). HCoV-229E, HCoV-NL63, HCoV-OC43, and HCoV-HKU1 infections accounted for 18.7%, 38.3%, 40.5%, and 2.5%, respectively. In comparison to ILI cases, SARI cases were significantly older, more likely caused by HCoV-229E and HCoV-OC43, and more often co-infected with other respiratory pathogens. A total of 179 full genome sequences of HCoVs were obtained from 321 positive patients. The phylogenetical analyses revealed that HCoV-229E, HCoV-NL63 and HCoV-OC43 continuously yielded novel lineages, respectively. The nonsynonymous to synonymous ratio of all key genes in each HCoV was less than one, indicating that all four HCoVs were under negative selection pressure. Multiple substitution modes were observed in spike glycoprotein among the four HCoVs. Our findings highlight the importance of enhancing surveillance on HCoVs, and imply that more variants might occur in the future.

A variant-proof SARS-CoV-2 vaccine targeting HR1 domain in S2 subunit of spike protein.

The emerging SARS-CoV-2 variants, commonly with many mutations in S1 subunit of spike (S) protein are weakening the efficacy of the current vaccines and antibody therapeutics. This calls for the variant-proof SARS-CoV-2 vaccines targeting the more conserved regions in S protein. Here, we designed a recombinant subunit vaccine, HR121, targeting the conserved HR1 domain in S2 subunit of S protein. HR121 consisting of HR1-linker1-HR2-linker2-HR1, is conformationally and functionally analogous to the HR1 domain present in the fusion intermediate conformation of S2 subunit. Immunization with HR121 in rabbits and rhesus macaques elicited highly potent cross-neutralizing antibodies against SARS-CoV-2 and its variants, particularly Omicron sublineages. Vaccination with HR121 achieved near-full protections against prototype SARS-CoV-2 infection in hACE2 transgenic mice, Syrian golden hamsters and rhesus macaques, and effective protection against Omicron BA.2 infection in Syrian golden hamsters. This study demonstrates that HR121 is a promising candidate of variant-proof SARS-CoV-2 vaccine with a novel conserved target in the S2 subunit for application against current and future SARS-CoV-2 variants.

[Medication law and mechanism of traditional Chinese medicine in prevention and treatment of epidemic diseases: based on traditional Chinese medicine theory of cold pestilence].

Epidemic diseases have caused huge harm to the society. Traditional Chinese medicine(TCM) has made great contributions to the prevention and treatment of them. It is of great reference value for fighting diseases and developing drugs to explore the medication law and mechanism of TCM under TCM theory. In this study, the relationship between the TCM theory of cold pestilence and modern epidemic diseases was investigated. Particularly, the the relationship of coronavirus disease 2019(COVID-19), severe acute respiratory syndrome(SARS), and influenza A(H1 N1) with the cold pestilence was identified and analyzed. The roles of TCM theory of cold pestilence in preventing and treating modern epidemic diseases were discussed. Then, through data mining and textual research, prescriptions for the treatment of cold pestilence were collected from major databases and relevant ancient books, and their medication laws were examined through analysis of high-frequency medicinals and medicinal pairs, association rules analysis, and cluster analysis. For example, the prescriptions with high confidence levels were identified: "Glycyrrhizae Radix et Rhizoma-Bupleuri Radix-Paeoniae Radix Alba" "Glycyrrhizae Radix et Rhizoma-Pinelliae Rhizoma-Bupleuri Radix", and TCM treatment methods with them were analyzed by clustering analysis to yield the medicinal combinations: "Zingiberis Rhizoma-Aconiti Lateralis Radix Praeparata-Ginseng Radix et Rhizoma" "Poria-Atractylodis Macrocephalae Rhizoma" "Cinnamomi Ramulus-Asari Radix et Rhizoma" "Citri Reticulatae Pericarpium-Perillae Folium" "Pinelliae Rhizoma-Magnoliae Officinalis Cortex-Atractylodis Rhizoma" "Paeoniae Radix Alba-Angelicae Sinensis Radix-Glycyrrhizae Radix et Rhizoma-Bupleuri Radix-Scutellariae Radix-Rhizoma Zingiberis Recens" "Ephedrae Herba-Armeniacae Semen Amarum-Gypsum Fibrosum" "Chuanxiong Rhizoma-Notopterygii Rhizoma et Radix-Angelicae Dahuricae Radix-Platycodonis Radix-Saposhnikoviae Radix". Then, according to the medication law for cold pestilence, the antiviral active components of medium-frequency and high-frequency medicinals were retrieved. It was found that these components exerted the antiviral effect by inhibiting virus replication, regulating virus proteins and antiviral signals, and suppressing protease activity. Based on network pharmacology, the mechanisms of the medicinals against severe acute respiratory syndrome coronavirus(SARS-CoV), 2019 novel coronavirus(2019-nCoV), and H1 N1 virus were explored. It was determined that the key targets were tumor necrosis factor(TNF), endothelial growth factor A(VEGFA), serum creatinine(SRC), epidermal growth factor receptor(EGFR), matrix metalloproteinase 9(MMP9), mitogen-activated protein kinase 14(MAPK14), and prostaglandin-endoperoxide synthase 2(PTGS2), which were involved the mitogen-activated protein kinase(MAPK) pathway, advanced glycation end-products(AGE)-receptor for AGE(RAGE) pathway, COVID-19 pathway, and mTOR pathway. This paper elucidated the medication law and mechanism of TCM for the prevention and treatment of epidemic diseases under the guidance of TCM theory of cold pestilence, in order to build a bridge between the theory and modern epidemic diseases and provide reference TCM methods for the prevention and treatment of modern epidemic diseases and ideas for the application of data mining to TCM treatment of modern diseases.

Consumption-Related Health Education Inequality in COVID-19: A Cross-Sectional Study in China.

Background: The COVID-19 pandemic influences various aspects of society, especially for people with low socioeconomic status. Health education has been proven to be a critical strategy in preventing a pandemic. However, socioeconomic characteristics may limit health education among low socioeconomic status groups. This study explores consumption-related health education inequality and the factors that contribute to this, which are variable across China during COVID-19. Methods: The 2020 China COVID-19 Survey is a cross-sectional study in China, based on an anonymous online survey from 7,715 samples in 85 cities. It employed machine-learning methods to assess household consumption and other contributing variates associated with health education during the pandemic. Concentration Index (CI) and Horizontal Index (HI) were used to measure consumption-related inequalities in health education, respectively. Moreover, Wagstaff decomposition analysis was employed to identify other contributing variables to health education inequality. Results: The result indicates that participants with more education, better income, and positive consumption preferences undertake higher health education during COVID-19. The CI and HI of consumption-health education inequality are 0.0321 (P < 0.001) and 0.0416 (p < 0.001), respectively, which indicates that health education is concentrated in wealthy groups. We adapted Lasso regression to solve issues and omit variables. In terms of other socioeconomic characteristics, Annual Income was also a major contributor to health education inequalities, accounting for 27.1% (P < 0.001). The empirical results also suggests that education, health status, identification residence, and medical health insurance contribute to health education inequality. Conclusions: The difference in Household consumption, annual income, rural and urban disparity, and private healthcare insurance are critical drivers of health education inequality. The government should pay more attention to promoting health education, and healthcare subside policy among vulnerable people. Significantly to improve awareness of undertaking health education with lower education, rural residential, to enhance confidence in economic recovery and life after COVID-19.

MERS-related CoVs in hedgehogs from Hubei Province, China.

The emerging coronavirus diseases such as COVID-19, MERS, and SARS indicated that animal coronaviruses (CoVs) spillover to humans are a huge threat to public health. Therefore, we needed to understand the CoVs carried by various animals. Wild hedgehogs were collected from rural areas in Wuhan and Xianning cities in Hubei Province for analysis of CoVs. PCR results showed that 5 out of 51 (9.8%) hedgehogs (Erinaceus amurensis) were positive to CoVs in Hubei Province with 3 samples from Wuhan City and 2 samples from Xianning City. Phylogenetic analysis based on the partial sequence of RNA-dependent RNA polymerase showed that the CoVs from hedgehogs are classified into Merbecovirus of the genus Betacoronavirus; the hedgehog CoVs formed a phylogenetic sister cluster with human MERS-CoVs and bat MERS-related CoVs. Among the 12 most critical residues of receptor binding domain in MERS-CoV for binding human Dipeptidyl peptidase 4, 3 residuals were conserved between the hedgehog MERS-related CoV obtained in this study and the human MERS-CoV. We concluded that hedgehogs from Hubei Province carried MERS-related CoVs, indicating that hedgehogs might be important in the evolution and transmission of MERS-CoVs, and continuous surveillance of CoVs in hedgehogs was important.

Role of perceived family support in psychological distress for pregnant women during the COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused major public panic in China. Pregnant women may be more vulnerable to stress, which may cause them to have psychological problems. AIM: To explore the effects of perceived family support on psychological distress in pregnant women during the COVID-19 pandemic. METHODS: A total of 2232 subjects were recruited from three cities in China. Through the online surveys, information on demographic data and health status during pregnancy were collected. Insomnia severity index, generalized anxiety disorder 7-item scale, patient health questionnaire-9, somatization subscale of the symptom check list 90 scale, and posttraumatic stress disorder checklist were used to assess the psychological distress. RESULTS: A total of 1015 (45.4%) women reported having at least one psychological distress. The women who reported having inadequate family support were more likely to suffer from multiple psychological distress (≥ 2 psychological distress) than women who received adequate family support. Among the women who reported less family support, 41.8% reported depression, 31.1% reported anxiety, 8.2% reported insomnia, 13.3% reported somatization and 8.9% reported posttraumatic stress disorder (PTSD), which were significantly higher than those who received strong family support. Perceived family support level was negatively correlated with depressive symptoms (r = -0.118, P < 0.001), anxiety symptoms (r = -0.111, P < 0.001), and PTSD symptoms (r = -0.155, P < 0.001). CONCLUSION: Family support plays an important part on pregnant women's mental health during the COVID-19 pandemic. Better family support can help improve the mental health of pregnant women.

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients.

BACKGROUND: The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance. OBJECTIVE: The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis. METHODS: A single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients. RESULTS: One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097-0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916-0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death. CONCLUSIONS: This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.

[Thoughts and suggestions on analysis of death cases report during COVID-19 epidemic].

It is an essential task to discuss the death cases for clinicians. During the emergent public events, the report and analysis of death cases is of far-reaching significance. The epidemic of coronavirus disease 2019(COVID-19) has brought huge losses to China, and the medical system has been sustaining tremendous pressure. The best weapon to defeat the epidemic is medical data and related scientific research, of which the systematic analysis and efficient use of death cases is a key step. Based on the incomplete record of death case report, the lack of humanistic perspective and patient report, every department and institution is facing great challenge in terms of data management. Given that the relevant systems need to be improved, and that the integration of standardized reports and clinical research is not mature,as well as other problems, we put forward several methodological suggestions: ① Establish national medical and health data center and improve relevant laws and regulations. ② Increase investment in medical data management and start data collection and analysis as early as possible during the epidemic. ③ Refine the content of death case report and promote the standardization of report. ④ Pay close attention to the report of death cases, review, summary and analysis. More importantly, we should continue to build and improve platforms and programs related to disease control, carry out epidemic-associated scientific research, enhance the managing efficiency of public health data, elevate the anti-risk capability of our medical system, and promote the steady progress of the health China strategy.

[Thoughts and suggestions on arrangement, analysis and summary of medical data during COVID-19 epidemic].

The analysis and utilization of clinical scientific research data is an effective means to promote the progress of diagnosis and treatment, and a key step in the development of medical sciences. During the epidemic of coronavirus disease 2019(COVID-19), how to transform the limited diagnostic data into clinical research resources has attracted much attention. Based on the low efficiency of data collection and extraction, the inconsistency of data analysis, the irregularity of data report and the high timeliness of data update during the epidemic, this paper briefly analyzed the background and reasons of data application under the current situation, and then discusses the problems and feasible solutions of clinical data applications under the epidemic situation and, more importantly, for future medical clinical research methods. We put forward several methodological suggestions: ① gradually improve the medical big data model and establish the national medical health data center; ② improve the scientific research literacy of medical staff and popularize the basic skills and knowledge of GCP; ③ promote a scientific, networked and shared data collection and management mode; ④ use the mixed research method and collective analysis to improve the efficiency of clinical data analysis; ⑤ pay attention to narration of the medical feelings and emphasize the humanistic data of clinical medicine. It is expected to promote the standardized and reasonable use of clinical scientific research data, the rigorous integration of expert opinions, and ultimately the development of big data for national health care.