The Threat of Potentially Pathogenic Bacteria in the Feces of Bats.

Bats have attracted global attention because of their zoonotic association with severe acute respiratory syndrome coronavirus (SARS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous and ongoing studies have predominantly focused on bat-borne viruses; however, the prevalence or abundance of bat-borne pathogenic bacteria and their potential public health significance have largely been neglected. For the first time, this study used both metataxonomics (16S rRNA marker gene sequencing) and culturomics (traditional culture methods) to systematically evaluate the potential public health significance of bat fecal pathogenic bacteria. To this end, fecal samples were obtained from five bat species across different locations in China, and their microbiota composition was analyzed. The results revealed that the bat microbiome was most commonly dominated by Proteobacteria, while the strictly anaerobic phylum Bacteroidetes occupied 35.3% of the relative abundance in Rousettus spp. and 36.3% in Hipposideros spp., but less than 2.7% in the other three bat species (Taphozous spp., Rhinolophus spp., and Myotis spp.). We detected 480 species-level phylotypes (SLPs) with PacBio sequencing, including 89 known species, 330 potentially new species, and 61 potentially higher taxa. In addition, a total of 325 species were identified by culturomics, and these were classified into 242 named species and 83 potentially novel species. Of note, 32 of the 89 (36.0%) known species revealed by PacBio sequencing were found to be pathogenic bacteria, and 69 of the 242 (28.5%) known species isolated by culturomics were harmful to people, animals, or plants. Additionally, nearly 40 potential novel species which may be potential bacterial pathogens were identified. IMPORTANCE Bats are one of the most diverse and widely distributed groups of mammals living in close proximity to humans. In recent years, bat-borne viruses and the viral zoonotic diseases associated with bats have been studied in great detail. However, the prevalence and abundance of pathogenic bacteria in bats have been largely ignored. This study used high-throughput sequencing techniques (metataxonomics) in combination with traditional culture methods (culturomics) to analyze the bacterial flora in bat feces from different species of bats in China, revealing that bats are natural hosts of pathogenic bacteria and carry many unknown bacteria. The results of this study can be used as guidance for future investigations of bacterial pathogens in bats.

The Threat of Potentially Pathogenic Bacteria in the Feces of Bats.

Bats have attracted global attention because of their zoonotic association with severe acute respiratory syndrome coronavirus (SARS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous and ongoing studies have predominantly focused on bat-borne viruses; however, the prevalence or abundance of bat-borne pathogenic bacteria and their potential public health significance have largely been neglected. For the first time, this study used both metataxonomics (16S rRNA marker gene sequencing) and culturomics (traditional culture methods) to systematically evaluate the potential public health significance of bat fecal pathogenic bacteria. To this end, fecal samples were obtained from five bat species across different locations in China, and their microbiota composition was analyzed. The results revealed that the bat microbiome was most commonly dominated by Proteobacteria, while the strictly anaerobic phylum Bacteroidetes occupied 35.3% of the relative abundance in Rousettus spp. and 36.3% in Hipposideros spp., but less than 2.7% in the other three bat species (Taphozous spp., Rhinolophus spp., and Myotis spp.). We detected 480 species-level phylotypes (SLPs) with PacBio sequencing, including 89 known species, 330 potentially new species, and 61 potentially higher taxa. In addition, a total of 325 species were identified by culturomics, and these were classified into 242 named species and 83 potentially novel species. Of note, 32 of the 89 (36.0%) known species revealed by PacBio sequencing were found to be pathogenic bacteria, and 69 of the 242 (28.5%) known species isolated by culturomics were harmful to people, animals, or plants. Additionally, nearly 40 potential novel species which may be potential bacterial pathogens were identified. IMPORTANCE Bats are one of the most diverse and widely distributed groups of mammals living in close proximity to humans. In recent years, bat-borne viruses and the viral zoonotic diseases associated with bats have been studied in great detail. However, the prevalence and abundance of pathogenic bacteria in bats have been largely ignored. This study used high-throughput sequencing techniques (metataxonomics) in combination with traditional culture methods (culturomics) to analyze the bacterial flora in bat feces from different species of bats in China, revealing that bats are natural hosts of pathogenic bacteria and carry many unknown bacteria. The results of this study can be used as guidance for future investigations of bacterial pathogens in bats.

Correlation Between Local Air Temperature and the COVID-19 Pandemic in Hubei, China.

Objective: To clarify the correlation between temperature and the COVID-19 pandemic in Hubei. Methods: We collected daily newly confirmed COVID-19 cases and daily temperature for six cities in Hubei Province, assessed their correlations, and established regression models. Results: For temperatures ranging from -3.9 to 16.5°C, daily newly confirmed cases were positively correlated with the maximum temperature ~0-4 days prior or the minimum temperature ~11-14 days prior to the diagnosis in almost all selected cities. An increase in the maximum temperature 4 days prior by 1°C was associated with an increase in the daily newly confirmed cases (~129) in Wuhan. The influence of temperature on the daily newly confirmed cases in Wuhan was much more significant than in other cities. Conclusion: Government departments in areas where temperatures range between -3.9 and 16.5°C and rise gradually must take more active measures to address the COVID-19 pandemic.

Dissecting the novel mechanism of reduning injection in treating Coronavirus Disease 2019 (COVID-19) based on network pharmacology and experimental verification.

ETHNOPHARMACOLOGICAL RELEVANCE: Reduning injection (RDNI) is a patented Traditional Chinese medicine that contains three Chinese herbal medicines, respectively are the dry aboveground part of Artemisia annua L., the flower of Lonicera japonica Thunb., and the fruit Gardenia jasminoides J.Ellis. RDNI has been recommended for treating Coronavirus Disease 2019 (COVID-19) in the "New Coronavirus Pneumonia Diagnosis and Treatment Plan". AIM OF THE STUDY: To elucidate and verify the underlying mechanisms of RDNI for the treatment of COVID-19. METHODS: This study firstly performed anti-SARS-CoV-2 experiments in Vero E6 cells. Then, network pharmacology combined with molecular docking was adopted to explore the potential mechanisms of RDNI in the treatment for COVID-19. After that, western blot and a cytokine chip were used to validate the predictive results. RESULTS: We concluded that half toxic concentration of drug CC50 (dilution ratio) = 1:1280, CC50 = 2.031 mg crude drugs/mL (0.047 mg solid content/mL) and half effective concentration of drug (EC50) (diluted multiples) = 1:25140.3, EC50 = 103.420 µg crude drugs/mL (2.405 µg solid content/mL). We found that RDNI can mainly regulate targets like carbonic anhydrases (CAs), matrix metallopeptidases (MMPs) and pathways like PI3K/AKT, MAPK, Forkhead box O s and T cell receptor signaling pathways to reduce lung damage. We verified that RDNI could effectively inhibit the overexpression of MAPKs, PKC and p65 nuclear factor-κB. The injection could also affect cytokine levels, reduce inflammation and display antipyretic activity. CONCLUSION: RDNI can regulate ACE2, Mpro and PLP in COVID-19. The underlying mechanisms of RDNI in the treatment for COVID-19 may be related to the modulation of the cytokine levels and inflammation and its antipyretic activity by regulating the expression of MAPKs, PKC and p65 nuclear factor NF-κB.

Antibody responses in COVID-19 patients.

Measuring virus-specific antibody responses to emerging pathogens is a well-established and highly useful tool to diagnose such infections, understand interactions between the immune system and pathogens, and provide potential clues for the development of vaccines or therapeutic agents against such pathogens. Since the beginning of 2020, the discovery of SARS-CoV-2 as the emerging virus responsible for the COVID-19 pandemic has provided new insight into the complexity of antibody responses to this dangerous virus. The current review aims to sort out diverse and sometimes seemingly confusing findings to put together a cohesive understanding on the profile of antibody responses elicited in COVID-19 patients.

Beta- and Novel Delta-Coronaviruses Are Identified from Wild Animals in the Qinghai-Tibetan Plateau, China.

Outbreaks of severe virus infections with the potential to cause global pandemics are increasingly concerning. One type of those commonly emerging and re-emerging pathogens are coronaviruses (SARS-CoV, MERS-CoV and SARS-CoV-2). Wild animals are hosts of different coronaviruses with the potential risk of cross-species transmission. However, little is known about the reservoir and host of coronaviruses in wild animals in Qinghai Province, where has the greatest biodiversity among the world's high-altitude regions. Here, from the next-generation sequencing data, we obtained a known beta-coronavirus (beta-CoV) genome and a novel delta-coronavirus (delta-CoV) genome from faecal samples of 29 marmots, 50 rats and 25 birds in Yushu Tibetan Autonomous Prefecture, Qinghai Province, China in July 2019. According to the phylogenetic analysis, the beta-CoV shared high nucleotide identity with Coronavirus HKU24. Although the novel delta-CoV (MtCoV) was closely related to Sparrow deltacoronavirus ISU42824, the protein spike of the novel delta-CoV showed highest amino acid identity to Sparrow coronavirus HKU17 (73.1%). Interestingly, our results identified a novel host (Montifringilla taczanowskii) for the novel delta-CoV and the potential cross-species transmission. The most recent common ancestor (tMRCA) of MtCoVs along with other closest members of the species of Coronavirus HKU15 was estimated to be 289 years ago. Thus, this study increases our understanding of the genetic diversity of beta-CoVs and delta-CoVs, and also provides a new perspective of the coronavirus hosts.

A timely update of global COVID-19 vaccine development.

This commentary provides an overview and links to presentations of a recent virtual congress series organized by the International Society for Vaccines (ISV) focused on COVID-19 vaccines. The series provided the academic community and vaccine developers as well as the wider general public with balanced information of the global response and resources for COVID-19 vaccines under development featuring: 1) NGOs and the regulatory perspective, 2) the status of vaccine development efforts, and 3) panel discussions to present and discuss challenges. ISV is a non-profit scientific organization whose members work on all areas relevant to vaccines. ISV plans to host additional virtual symposia including regional meetings and incorporating other topics along with COVID-19 vaccines.

Antibody responses in COVID-19 patients.

Measuring virus-specific antibody responses to emerging pathogens is a well-established and highly useful tool to diagnose such infections, understand interactions between the immune system and pathogens, and provide potential clues for the development of vaccines or therapeutic agents against such pathogens. Since the beginning of 2020, the discovery of SARS-CoV-2 as the emerging virus responsible for the COVID-19 pandemic has provided new insight into the complexity of antibody responses to this dangerous virus. The current review aims to sort out diverse and sometimes seemingly confusing findings to put together a cohesive understanding on the profile of antibody responses elicited in COVID-19 patients.

Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019.

OBJECTIVES: Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19. MATERIALS AND METHODS: Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively. RESULTS: The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval [CI]: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the model achieved AUCs of 0.955 (0.884-1.00) and 0.923 (0.864-0.983) and accuracies of 97.0 and 81.6% in the Honghu and Nanchang subgroups, respectively. CONCLUSION: Our deep learning-based model can accurately predict disease severity as well as disease progression in COVID-19 patients using CT imaging, offering promise for guiding clinical treatment.

Influencing subjective well-being for business and sustainable development using big data and predictive regression analysis.

Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people's well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people's personal lifestyles now offers a new dimension into well-being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people's perceived well-being, long-term health conditions, one's marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data.

Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019.

BACKGROUND: Information regarding risk factors associated with severe coronavirus disease (COVID-19) is limited. This study aimed to develop a model for predicting COVID-19 severity. METHODS: Overall, 690 patients with confirmed COVID-19 were recruited between 1 January and 18 March 2020 from hospitals in Honghu and Nanchang; finally, 442 patients were assessed. Data were categorised into the training and test sets to develop and validate the model, respectively. FINDINGS: A predictive HNC-LL (Hypertension, Neutrophil count, C-reactive protein, Lymphocyte count, Lactate dehydrogenase) score was established using multivariate logistic regression analysis. The HNC-LL score accurately predicted disease severity in the Honghu training cohort (area under the curve [AUC]=0.861, 95% confidence interval [CI]: 0.800-0.922; P<0.001); Honghu internal validation cohort (AUC=0.871, 95% CI: 0.769-0.972; P<0.001); and Nanchang external validation cohort (AUC=0.826, 95% CI: 0.746-0.907; P<0.001) and outperformed other models, including CURB-65 (confusion, uraemia, respiratory rate, BP, age ≥65 years) score model, MuLBSTA (multilobular infiltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hypertension, and age) score model, and neutrophil-to-lymphocyte ratio model. The clinical significance of HNC-LL in accurately predicting the risk of future development of severe COVID-19 was confirmed. INTERPRETATION: We developed an accurate tool for predicting disease severity among COVID-19 patients. This model can potentially be used to identify patients at risks of developing severe disease in the early stage and therefore guide treatment decisions. FUNDING: This work was supported by the National Nature Science Foundation of China (grant no. 81972897) and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015).

Development and Validation of the HNC-LL Score for Predicting the Severity of Coronavirus Disease 2019 (preprint)

Background: The severity of coronavirus disease 2019 (COVID-19) varies widely, ranging from asymptomatic to fatal. However, there is limited information regarding the risk factors associated with severe disease. In this study, we aimed to develop a model for predicting COVID-19 severity. Methods: A total of 690 patients with confirmed COVID-19 were recruited between January 1 and March 18, 2020 from hospitals in Honghu and Nanchang, and finally, 442 patients were analyzed. Data were partitioned into the training set and test sets to develop and validate the model, respectively. Results: A predictive HNC-LL (Hypertension–Neutrophil count–C-reactive protein– Lymphocyte count– Lactate dehydrogenase) score was established based on multivariate logistic regression analysis results. The HNC-LL score accurately predicted disease severity in the Honghu training cohort (area under the curve [AUC] = 0.861, 95% confidence interval [CI]: 0.800–0.922; P <0.001); the Honghu internal validation cohort (AUC = 0.871, 95% CI: 0.769–0.972; P <0.001); and the Nanchang external validation cohort (AUC = 0.826, 95% CI: 0.746–0.907; P <0.001), and outperformed other models including the CURB-65 score model, MuLBSTA score model, and neutrophil-to-lymphocyte ratio model. Moreover, the clinical significance of HNC-LL in accurately predicting patients with severe COVID-19 in the early phase was confirmed. Conclusions: We developed an accurate tool for predicting disease severity in patients with COVID-19. This model can potentially be used to identify patients at risk of developing severe disease in the early stage and therefore, guide treatment decisions.Funding Statement: This work was supported by the National Nature Science Foundation of China (Grant Nos. 81972897) and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015).Declaration of Interests: The authors declare that they do not have any conflicts of interest.Ethics Approval Statement: This retrospective analysis was approved by Medical Ethics committee of Nanfang Hospital of Southern Medical University, and the requirement for informed consent was waived by the ethics committee.