COVID-19's effect on the spatial integration of fish markets: Evidence from carp price in China.

While curbing the spread of Coronavirus Disease 2019 (COVID-19), lockdown policies and "stay-at-home" restrictions caused massive supply chain disruptions worldwide. This led to breaks in spatial market integration, which could further lead to market inefficiency and resource misallocation. Taking daily price data from 2016 to 2021, this study investigates COVID-19's effect on the spatial market integration of fish in China using cointegration tests. We find a high degree of spatial market integration for fish in China before the COVID-19 pandemic. Further, our results show that COVID-19's effect on the spatial market integration of fish varies spatially in China. Specifically, COVID-19 reduces the degree of spatial market integration in most provinces, especially those with high infection rates. Meanwhile, the degree of spatial market integration in provinces with low infection rates remains high. Therefore, the government should be regionally specific when formulating market recovery policies.

Non-linear feature analysis of public emotion evolution for online teaching during the COVID-19 pandemic

Purpose Nowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era. Design/methodology/approach The article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis. Findings It was found that the sequence has typical non-linear chaotic characteristics;its correlation dimension indicates that it contains obvious fractal characteristics;the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic. Originality/value Based on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

UBR5 Acts as an Antiviral Host Factor against MERS-CoV via Promoting Ubiquitination and Degradation of ORF4b.

Within the past 2 decades, three highly pathogenic human coronaviruses have emerged, namely, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The health threats and economic burden posed by these tremendously severe coronaviruses have paved the way for research on their etiology, pathogenesis, and treatment. Compared to SARS-CoV and SARS-CoV-2, MERS-CoV genome encoded fewer accessory proteins, among which the ORF4b protein had anti-immunity ability in both the cytoplasm and nucleus. Our work for the first time revealed that ORF4b protein was unstable in the host cells and could be degraded by the ubiquitin proteasome system. After extensive screenings, it was found that UBR5 (ubiquitin protein ligase E3 component N-recognin 5), a member of the HECT E3 ubiquitin ligases, specifically regulated the ubiquitination and degradation of ORF4b. Similar to ORF4b, UBR5 can also translocate into the nucleus through its nuclear localization signal, enabling it to regulate ORF4b stability in both the cytoplasm and nucleus. Through further experiments, lysine 36 was identified as the ubiquitination site on the ORF4b protein, and this residue was highly conserved in various MERS-CoV strains isolated from different regions. When UBR5 was knocked down, the ability of ORF4b to suppress innate immunity was enhanced and MERS-CoV replication was stronger. As an anti-MERS-CoV host protein, UBR5 targets and degrades ORF4b protein through the ubiquitin proteasome system, thereby attenuating the anti-immunity ability of ORF4b and ultimately inhibiting MERS-CoV immune escape, which is a novel antagonistic mechanism of the host against MERS-CoV infection. IMPORTANCE ORF4b was an accessory protein unique to MERS-CoV and was not present in SARS-CoV and SARS-CoV-2 which can also cause severe respiratory disease. Moreover, ORF4b inhibited the production of antiviral cytokines in both the cytoplasm and the nucleus, which was likely to be associated with the high lethality of MERS-CoV. However, whether the host proteins regulate the function of ORF4b is unknown. Our study first determined that UBR5, a host E3 ligase, was a potential host anti-MERS-CoV protein that could reduce the protein level of ORF4b and diminish its anti-immunity ability by inducing ubiquitination and degradation. Based on the discovery of ORF4b-UBR5, a critical molecular target, further increasing the degradation of ORF4b caused by UBR5 could provide a new strategy for the clinical development of drugs for MERS-CoV.

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform.

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

Shanghai expert consensus on clinical protocol for traditional Chinese medicine treatment of COVID-19 among the elderly population (second edition).

This document is the revised edition of the previously issued Shanghai Expert Consensus on Clinical Protocol for Traditional Chinese Medicine Treatment of COVID-19 among the Elderly Population. Based on the clinical experience and the Protocol for Diagnosis and Treatment of COVID-19 (Trial 9th Edition), this revised edition provides treatment approaches and recommendations to proactively cope with Omicron variant and increase the therapeutic efficacy for coronavirus disease 2019 among the elderly population in Shanghai, China.

A systematic review and meta-analysis on prevalence of and risk factors associated with depression, anxiety and insomnia in infectious diseases, including COVID-19: a call to action.

Infectious disease epidemics have become more frequent and more complex during the 21st century, posing a health threat to the general public and leading to psychological symptoms. The current study was designed to investigate the prevalence of and risk factors associated with depression, anxiety and insomnia symptoms during epidemic outbreaks, including COVID-19. We systematically searched the PubMed, Embase, Web of Science, OVID, Medline, Cochrane databases, bioRxiv and medRxiv to identify studies that reported the prevalence of depression, anxiety or insomnia during infectious disease epidemics, up to August 14th, 2020. Prevalence of mental symptoms among different populations including the general public, health workers, university students, older adults, infected patients, survivors of infection, and pregnant women across all types of epidemics was pooled. In addition, prevalence of mental symptoms during COVID-19 was estimated by time using meta-regression analysis. A total of 17,506 papers were initially retrieved, and a final of 283 studies met the inclusion criteria, representing a total of 948,882 individuals. The pooled prevalence of depression ranged from 23.1%, 95% confidential intervals (95% CI: [13.9-32.2]) in survivors to 43.3% (95% CI: [27.1-59.6]) in university students, the pooled prevalence of anxiety ranged from 25.0% (95% CI: [12.0-38.0]) in older adults to 43.3% (95% CI: [23.3-63.3]) in pregnant women, and insomnia symptoms ranged from 29.7% (95% CI: [24.4-34.9]) in the general public to 58.4% (95% CI: [28.1-88.6]) in university students. Prevalence of moderate-to-severe mental symptoms was lower but had substantial variation across different populations. The prevalence of mental problems increased over time during the COVID-19 pandemic among the general public, health workers and university students, and decreased among infected patients. Factors associated with increased prevalence for all three mental health symptoms included female sex, and having physical disorders, psychiatric disorders, COVID infection, colleagues or family members infected, experience of frontline work, close contact with infected patients, high exposure risk, quarantine experience and high concern about epidemics. Frequent exercise and good social support were associated with lower risk for these three mental symptoms. In conclusion, mental symptoms are common during epidemics with substantial variation across populations. The population-specific psychological crisis management are needed to decrease the burden of psychological problem and improve the mental wellbeing during epidemic.

Economic Evaluation of First-Line Camrelizumab for Advanced Non-small-cell Lung Cancer in China.

Background: As the first domestic PD-1 antibody approved for lung cancer in China, camrelizumab has exhibited proven effectiveness for non-small-cell lung cancer (NSCLC) patients. However, the cost-effectiveness of this new regimen remains to be investigated. Objective: To evaluate the cost-effectiveness of camrelizumab combination therapy vs. chemotherapy for previously untreated patients with advanced, non-squamous NSCLC without Alk or Egfr genomic aberrations from the perspective of China's healthcare system. Methods: Based on the CameL trial, the study developed a three-health state Markov model to evaluate the cost-effectiveness of adding camrelizumab to chemotherapy compared to chemotherapy alone in NSCLC patients. The analysis models were conducted for patients unselected by PD-L1 tumor expression (the base case) and the patient subgroup with PD-L1-expressing tumors (≥1%). Primary model outcomes included the costs in US dollars and health outcomes in quality-adjusted life-years (QALYs) as well as the incremental cost-effectiveness ratio (ICER) under a willingness-to-pay threshold of $31,500 per QALY. Additionally, a scenario analysis that adjusted within-trial crossover was employed to evaluate camrelizumab combination therapy compared to chemotherapy without subsequent use of PD1/PD-L1 antibodies. Results: Camrelizumab combination therapy was more costly and provided additional 0.11 QALYs over chemotherapy in the base case analysis (0.86 vs. 0.75 QALYs), 0.12 QALYs over chemotherapy in the subgroup analysis (0.99 vs. 0.88 QALYs), and 0.34 QALYs over chemotherapy in the scenario analysis (0.86 vs. 0.52 QALYs). Correspondingly, the ICER was $63,080 per QALY, $46,311 per QALY, and $30,591 per QALY, in the base case, the subgroup, and the scenario analysis, respectively. One-way sensitivity analyses revealed that ICERs of the base case and the subgroup analysis were most sensitive to the cost of camrelizumab, the cost of pemetrexed. Besides, the base case and subgroup analysis were more sensitive to the risk of neutrophil count decreased in the camrelizumab and the utility of stable disease, respectively. Conclusion: Although camrelizumab combination therapy is not cost-effective as first-line therapy for NSCLC patients in China in the base case, adjusting within-trial crossover would move the treatment regimen toward cost-effectiveness in the scenario analysis.

Immunocapture Magnetic Beads Enhanced the LAMP-CRISPR/Cas12a Method for the Sensitive, Specific, and Visual Detection of Campylobacter jejuni.

Campylobacter jejuni is one of the most important causes of food-borne infectious disease, and poses challenges to food safety and public health. Establishing a rapid, accurate, sensitive, and simple detection method for C. jejuni enables early diagnosis, early intervention, and prevention of pathogen transmission. In this study, an immunocapture magnetic bead (ICB)-enhanced loop-mediated isothermal amplification (LAMP) CRISPR/Cas12a method (ICB-LAMP-CRISPR/Cas12a) was developed for the rapid and visual detection of C. jejuni. Using the ICB-LAMP-CRISPR/Cas12a method, C. jejuni was first captured by ICB, and the bacterial genomic DNA was then released by heating and used in the LAMP reaction. After the LAMP reaction, LAMP products were mixed and detected by the CRISPR/Cas12a cleavage mixture. This ICB-LAMP-CRISPR/Cas12a method could detect a minimum of 8 CFU/mL of C. jejuni within 70 min. Additionally, the method was performed in a closed tube in addition to ICB capture, which eliminates the need to separate preamplification and transfer of amplified products to avoid aerosol pollution. The ICB-LAMP-CRISPR/Cas12a method was further validated by testing 31 C. jejuni-positive fecal samples from different layer farms. This method is an all-in-one, simple, rapid, ultrasensitive, ultraspecific, visual detection method for instrument-free diagnosis of C. jejuni, and has wide application potential in future work.

Host E3 ligase HUWE1 attenuates the proapoptotic activity of the MERS-CoV accessory protein ORF3 by promoting its ubiquitin-dependent degradation.

With the outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), coronaviruses have begun to attract great attention across the world. Of the known human coronaviruses, however, Middle East respiratory syndrome coronavirus (MERS-CoV) is the most lethal. Coronavirus proteins can be divided into three groups: nonstructural proteins, structural proteins, and accessory proteins. While the number of each of these proteins varies greatly among different coronaviruses, accessory proteins are most closely related to the pathogenicity of the virus. We found for the first time that the ORF3 accessory protein of MERS-CoV, which closely resembles the ORF3a proteins of severe acute respiratory syndrome coronavirus and SARS-CoV-2, has the ability to induce apoptosis in cells in a dose-dependent manner. Through bioinformatics analysis and validation, we revealed that ORF3 is an unstable protein and has a shorter half-life in cells compared to that of severe acute respiratory syndrome coronavirus and SARS-CoV-2 ORF3a proteins. After screening, we identified a host E3 ligase, HUWE1, that specifically induces MERS-CoV ORF3 protein ubiquitination and degradation through the ubiquitin-proteasome system. This results in the diminished ability of ORF3 to induce apoptosis, which might partially explain the lower spread of MERS-CoV compared to other coronaviruses. In summary, this study reveals a pathological function of MERS-CoV ORF3 protein and identifies a potential host antiviral protein, HUWE1, with an ability to antagonize MERS-CoV pathogenesis by inducing ORF3 degradation, thus enriching our knowledge of the pathogenesis of MERS-CoV and suggesting new targets and strategies for clinical development of drugs for MERS-CoV treatment.

The Impact of Quarantine on Pain Sensation among the General Population in China during the COVID-19 Pandemic.

During the pandemic era, quarantines might potentially have negative effects and disproportionately exacerbate health condition problems. We conducted this cross-sectional, national study to ascertain the prevalence of constant pain symptoms and how quarantines impacted the pain symptoms and identify the factors associated with constant pain to further guide reducing the prevalence of chronic pain for vulnerable people under the pandemic. The sociodemographic data, quarantine conditions, mental health situations and pain symptoms of the general population were collected. After adjusting for potential confounders, long-term quarantine (≥15 days) exposures were associated with an increased risk of constant pain complaints compared to those not under a quarantine (Odds Ratio (OR): 1.26; 95% Confidence Interval (CI): 1.03, 1.54; p = 0.026). Risk factors including unemployment (OR: 1.55), chronic disease history (OR: 2.38) and infection with COVID-19 (OR: 2.15), and any of mental health symptoms including depression, anxiety, insomnia and PTSD (OR: 5.44) were identified by a multivariable logistic regression. Additionally, mediation analysis revealed that the effects of the quarantine duration on pain symptoms were mediated by mental health symptoms (indirect effects: 0.075, p < 0.001). These results advocated that long-term quarantine measures were associated with an increased risk of experiencing pain, especially for vulnerable groups with COVID-19 infection and with mental health symptoms. The findings also suggest that reducing mental distress during the pandemic might contribute to reducing the burden of pain symptoms and prioritizing interventions for those experiencing a long-term quarantine.

Resource Allocation for Multiuser Molecular Communication Systems Oriented to the Internet of Medical Things.

Communication between nanomachines is still an important topic in the construction of the Internet of Bio-Nano Things (IoBNT). Currently, molecular communication (MC) is expected to be a promising technology to realize IoBNT. To effectively serve the IoBNT composed of multiple nanomachine clusters, it is imperative to study multiple-access MC. In this article, based on the molecular division multiple access technology, we propose a novel multiuser MC system, where information molecules with different diffusion coefficients are first employed. Aiming at the user fairness in the considered system, we investigate the optimization of molecular resource allocation, including the assignment of the types of molecules and the number of molecules of a type. Specifically, three performance metrics are considered, namely, min-max fairness for error probability, max-min fairness for achievable rate, and weighted sum-rate maximization. Moreover, we propose two assignment strategies for types of molecules, i.e., best-to-best (BTB) and best-to-worst (BTW). Subsequently, for a two-user scenario, we analytically derive the optimal allocation for the number of molecules when types of molecules are fixed for all users. In contrast, for a three-user scenario, we prove that the BTB and BTW schemes with the optimal allocation for the number of molecules can provide the lower and upper bounds on system performance, respectively. Finally, numerical results show that the combination of BTW and the optimal allocation for the number of molecules yields better performance than the benchmarks.

Microbiome Profiling Using Shotgun Metagenomic Sequencing Identified Unique Microorganisms in COVID-19 Patients With Altered Gut Microbiota.

COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut-lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman's correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.

Patterns of mental health problems before and after easing COVID-19 restrictions: Evidence from a 105248-subject survey in general population in China.

BACKGROUND: The COVID-19 pandemic has alarming implications for individual and population level mental health. Although the future of COVID-19 is unknown at present, more countries or regions start to ease restrictions. The findings from this study have provided the empirical evidence of prevalence and patterns of mental disorders in Chinese general population before and after easing most COVID-19 restrictions, and information of the factors associated with these patterns. METHODS: A cross-sectional population-based online survey was carried out from February to March 2020 in the general population across all provinces in China. The 12-item General Health Questionnaire (GHQ-12) was incorporated in the survey. Latent class analyses were performed to investigate the patterns of mental disorders and multinomial logistic regressions were used to examine how individual and regional risk factors can predict mental disorder patterns. RESULTS: Four distinctive patterns of mental health were revealed in the general population. After the ease of most COVID-19 restrictions, the prevalence of high risk of mental disorders decreased from 25.8% to 20.9% and prevalence of being high risk of unhappiness and loss of confidence decreased from 10.1% to 8.1%. However, the prevalence of stressed, social dysfunction and unhappy were consistently high before and after easing restrictions. Several regional factors, such as case mortality rate and healthcare resources, were associated with mental health status. Of note, healthcare workers were less likely to have mental disorders, compared to other professionals and students. CONCLUSIONS: The dynamic management of mental health and psychosocial well-being is as important as that of physical health both before and after the ease of COVID-19 restrictions. Our findings may help in mental health interventions in other countries and regions while easing COVID-19 restrictions.

Intermittent Hypoxic Preconditioning: A Potential New Powerful Strategy for COVID-19 Rehabilitation.

COVID-19 is a highly infectious respiratory virus, which can proliferate by invading the ACE2 receptor of host cells. Clinical studies have found that the virus can cause dyspnea, pneumonia and other cardiopulmonary system damage. In severe cases, it can lead to respiratory failure and even death. Although there are currently no effective drugs or vaccines for the prevention and treatment of COVID-19, the patient's prognosis recovery can be effectively improved by ameliorating the dysfunction of the respiratory system, cardiovascular systems, and immune function. Intermittent hypoxic preconditioning (IHP) as a new non-drug treatment has been applied in the clinical and rehabilitative practice for treating chronic obstructive pulmonary disease (COPD), diabetes, coronary heart disease, heart failure, hypertension, and other diseases. Many clinical studies have confirmed that IHP can improve the cardiopulmonary function of patients and increase the cardiorespiratory fitness and the tolerance of tissues and organs to ischemia. This article introduces the physiological and biochemical functions of IHP and proposes the potential application plan of IHP for the rehabilitation of patients with COVID-19, so as to provide a better prognosis for patients and speed up the recovery of the disease. The aim of this narrative review is to propose possible causes and pathophysiology of COVID-19 based on the mechanisms of the oxidative stress, inflammation, and immune response, and to provide a new, safe and efficacious strategy for the better rehabilitation from COVID-19.

Secondary attack rate of COVID-19 in household contacts in the Winnipeg Health Region, Canada.

OBJECTIVES: Understanding important transmission parameters, such as secondary attack rates, can help in strategizing, prioritizing, and planning interventions to curb morbidity and mortality due to COVID-19. Early reports suggest the risk of transmission of SARS-CoV-2 (the pathogen that causes COVID-19 disease) is high among household contacts of cases. The purpose of this analysis was to determine the household transmissibility of SARS-CoV-2 in residents of the Winnipeg Health Region. METHODS: We extracted information on COVID-19 cases and their household contacts from the regional public health surveillance database and used this dataset to calculate secondary attack rates. RESULTS: We included 381 individuals from 102 unique households (102 primary cases and 279 household contacts). A total of 41 contacts from 25 households became secondary cases in the 14 days since last unprotected exposure to the primary case, resulting in a secondary attack rate of 14.7% (95% CI: 10.5-18.8). CONCLUSION: Household transmission of SARS-CoV-2 has been an important contributor to the local COVID-19 epidemic.

RéSUMé: OBJECTIFS: Comprendre les importants paramètres de transmission, comme les taux d'infection secondaire, peut aider à établir des stratégies et des priorités et à planifier des interventions afin de réduire la morbidité et la mortalité liées à la COVID-19. Les premiers rapports laissent entendre que le risque de transmission du SRAS-CoV-2 (l'agent pathogène qui cause la COVID-19) est élevé parmi les contacts familiaux de cas de COVID-19. Le but de cette analyse visait à déterminer la transmissibilité familiale du SRAS-CoV-2 chez les résidents de la région sanitaire de Winnipeg. MéTHODES: Nous avons tiré l'information sur les cas de COVID-19 et de leurs contacts familiaux de la base de données de surveillance de la santé publique à l'échelle régionale et nous avons utilisé ces données pour calculer les taux d'infection secondaire. RéSULTATS: Nous avons inclus 381 personnes de 102 ménages uniques (102 cas primaires et 279 contacts familiaux). Au total, 41 contacts de 25 ménages sont devenus des cas secondaires dans les 14 jours depuis la dernière exposition non protégée au cas primaire, ce qui correspond à un taux d'infection secondaire de 14,7 % (IC de 95 % : 10,5­18,8). CONCLUSION: La transmission familiale du SRAS-CoV-2 a contribué grandement à l'épidémie locale de la COVID-19.

Epigallocatechin-3-gallate, an active ingredient of Traditional Chinese Medicines, inhibits the 3CLpro activity of SARS-CoV-2.

SARS-CoV-2 is the etiological agent responsible for the ongoing pandemic of coronavirus disease 2019 (COVID-19). The main protease of SARS-CoV-2, 3CLpro, is an attractive target for antiviral inhibitors due to its indispensable role in viral replication and gene expression of viral proteins. The search of compounds that can effectively inhibit the crucial activity of 3CLpro, which results to interference of the virus life cycle, is now widely pursued. Here, we report that epigallocatechin-3-gallate (EGCG), an active ingredient of Chinese herbal medicine (CHM), is a potent inhibitor of 3CLpro with half-maximum inhibitory concentration (IC50) of 0.874 ± 0.005 µM. In the study, we retrospectively analyzed the clinical data of 123 cases of COVID-19 patients, and found three effective Traditional Chinese Medicines (TCM) prescriptions. Multiple strategies were performed to screen potent inhibitors of SARS-CoV-2 3CLpro from the active ingredients of TCMs, including network pharmacology, molecular docking, surface plasmon resonance (SPR) binding assay and fluorescence resonance energy transfer (FRET)-based inhibition assay. The SPR assay showed good interaction between EGCG and 3CLpro with KD ~6.17 µM, suggesting a relatively high affinity of EGCG with SARS-CoV-2 3CLpro. Our results provide critical insights into the mechanism of action of EGCG as a potential therapeutic agent against COVID-19.

Early therapeutic interventions of traditional Chinese medicine in COVID-19 patients: A retrospective cohort study.

OBJECTIVE: To observe the early interventions of traditional Chinese Medicine (TCM) on the conversion time of nucleic acid in patients with coronavirus disease 2019 (COVID-19), and find possible underlying mechanisms of action. METHODS: A retrospective cohort study was conducted on 300 confirmed COVID-19 patients who were treated with TCM, at a designated hospital in China. The patients were categorized into three groups: TCM1, TCM2 and TCM3, who respectively received TCM interventions within 7, 8-14, and greater than 15 days of hospitalization. Different indicators such as the conversion time of pharyngeal swab nucleic acid, the conversion time of fecal nucleic acid, length of hospital stay, and inflammatory markers (leukocyte count, and lymphocyte count and percentage) were analyzed to observe the impact of early TCM interventions on these groups. RESULTS: The median conversion times of pharyngeal swab nucleic acid in the three groups were 5.5, 7 and 16 d (P < 0.001), with TCM1 and TCM2 being statistically different from TCM3 (P < 0.01). TCM1 (P < 0.05) and TCM3 (P < 0.01) were statistically different from TCM2. The median conversion times of fecal nucleic acid in the three groups were 7, 9 and 17 d (P < 0.001). Conversion times of fecal nucleic acid in TCM1 were statistically different from TCM3 and TCM2 (P < 0.01). The median lengths of hospital stay in the three groups were 13, 16 and 21 d (P < 0.001). TCM1 and TCM2 were statistically different from TCM3 (P < 0.01); TCM1 and TCM3 were statistically different from TCM2 (P < 0.01). Both leucocyte and lymphocyte counts increased gradually with an increase in the length of hospital stay in TCM1 group patients, with a statistically significant difference observed at each time point in the group (P < 0.001). Statistically significant differences in lymphocyte count and percentage in TCM2 (P < 0.001), and in leucocyte count (P = 0.043) and lymphocyte count (P = 0.038) in TCM3 were observed. The comparison among the three groups showed a statistically significant difference in lymphocyte percentage on the third day of admission (P = 0.044). CONCLUSION: In this study, it was observed that in COVID-19 patients treated with a combination of Chinese and Western medicines, TCM intervention earlier in the hospital stay correlated with faster conversion time of pharyngeal swab and fecal nucleic acid, as well as shorter length of hospital stay, thus helping promote faster recovery of the patient. The underlying mechanism of action may be related to improving inflammation in patients with COVID-19.

A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients.

BACKGROUND: COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. METHODS: COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson's χ2-test or Fisher's exact test, and continuous variables were analyzed using Student's t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. RESULTS: A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). CONCLUSIONS: A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.