Comprehensive Bioinformatics analysis of angiotensin-converting enzyme 2 (ACE2) (preprint)

ACE2, a member of the angiotensin converting enzyme family, plays an irreplaceable role in the renin-angiotensin system. And the variations of ACE2 are regarded as the key factor to human diseases such as the novel coronavirus pneumonia, cardiovascular disease, and tumors. Here, we summarized the mutation, expression, modification and function of the human ACE2 based on comprehensive bioinformatics analysis. Especially, the relationship between ACE2 expression and diseases, especially tumor was further discussed. ACE2 is highly conserved in different genera and families. We explored the correlation between ACE2 and disease based on the datasets of GCBI and GEO (Gene expression omnibus), and found the expression of ACE2 is related to heart failure. High prevalence of ACE2 mutations is observed in diffuse large B-cell lymphoma, uterine carcinosarcoma (UCS), and stomach adenocarcinoma (STAD). We first identified that highly expressed of ACE2 was linked to poor prognosis of overall survival for tumors of brain lower grade glioma (LGG). Specially, the expression level of ACE2 in kidney-related tumor tissues is much higher than that of normal kidney tissues. ACE2 is negatively correlated with the infiltration level of cancer-associated fibroblasts in most kinds of cancers, such as uterine corpus endometrial carcinoma (UCEC), esophageal carcinoma (ESCA), ovarian serous cystadenocarcinoma (OV) and kidney renal clear cell carcinoma (KIRC); positively correlation in testicular germ cell tumors (TGCT). The different phosphorylation sites of ACE2 were analyzed in CPTAC dataset, and the DNA methylation of ACE2 in colon adenocarcinoma (COAD), kidney renal papillary cell carcinoma (KIRP), and rectum adenocarcinoma (READ) was lower than that of normal control by using SMART database. Moreover, we summarized the interaction proteins and targeted miRNAs of ACE2 through bioinformatics. Then we found the endocrine process and the regulation of systemic arterial blood pressure were involved in the functional mechanisms of ACE2 by using KEGG and GO analysis. Our study offers a relatively comprehensive understanding of ACE2.

Ozone Pollution of Megacity Shanghai during City-Wide Lockdown Assessed Using TROPOMI Observations of NO2 and HCHO

An unprecedented city-wide lockdown took place in Shanghai from April to May 2022 to curb the spread of COVID-19, which caused socio-economic disruption but a significant reduction of anthropogenic emissions in this metropolis. However, the ground-based monitoring data showed that the concentration of ozone (O3) remained at a high level. This study applied Tropospheric Monitoring Instrument (TROPOMI) observations to examine changes in tropospheric vertical column density (VCD) of nitrogen dioxide (NO2) and formaldehyde (HCHO), which are precursors of O3. Compared with the same period in 2019-2021, VCDs of NO2 and HCHO decreased respectively by ~50% and ~20%. Multiple regression analysis showed that the lockdown effect played a dominant role in this dramatic decline rather than meteorological impacts. Using the exponentially-modified Gaussian method, this study quantified nitrogen oxides (NOX) emission in Shanghai as 32.60 mol/s with a decrease of 50-80%, which was mainly contributed by the transportation and industrial sectors. The significant reduction of NOX emission in Shanghai is much higher than that of volatile organic compounds (VOCs), which led to dramatic changes in formaldehyde-to-nitrogen dioxide ratio (HCHO/NO2, FNR). Thus, when enforcing regulation on NOx emission control in the future, coordinately reducing VOCs emission should be implemented to mitigate urban O3 pollution.

Homogeneous ensemble models for predicting infection levels and mortality of COVID-19 patients: Evidence from China

Background Persistence of long-term COVID-19 pandemic is putting high pressure on healthcare services worldwide for several years. This article aims to establish models to predict infection levels and mortality of COVID-19 patients in China. Methods Machine learning models and deep learning models have been built based on the clinical features of COVID-19 patients. The best models are selected by area under the receiver operating characteristic curve (AUC) scores to construct two homogeneous ensemble models for predicting infection levels and mortality, respectively. The first-hand clinical data of 760 patients are collected from Zhongnan Hospital of Wuhan University between 3 January and 8 March 2020. We preprocess data with cleaning, imputation, and normalization. Results Our models obtain AUC = 0.7059 and Recall (Weighted avg) = 0.7248 in predicting infection level, while AUC=0.8436 and Recall (Weighted avg) = 0.8486 in predicting mortality ratio. This study also identifies two sets of essential clinical features. One is C-reactive protein (CRP) or high sensitivity C-reactive protein (hs-CRP) and the other is chest tightness, age, and pleural effusion. Conclusions Two homogeneous ensemble models are proposed to predict infection levels and mortality of COVID-19 patients in China. New findings of clinical features for benefiting the machine learning models are reported. The evaluation of an actual dataset collected from January 3 to March 8, 2020 demonstrates the effectiveness of the models by comparing them with state-of-the-art models in prediction.

Estimating city NOX emissions from TROPOMI high spatial resolution observations – A case study on Yangtze River Delta, China

In this study, TROPOspheric Monitoring Instrument (TROPOMI) observations were resampled to obtain 0.01° × 0.01° NO2 VCD (vertical column density) over Yangtze River Delta (YRD), China. Based on this high spatial resolution satellite observations, NO2 VCDs in megacities cluster of YRD region were examined with a reduction of ~35% during COVID-19 lockdown. The adjusted Exponentially-Modified Gaussian (EMG) model was used to estimate the NOX emission in typical cities under regionally polluted YRD region. Taking 100 km of mass integration interval as an example, during 2018–2019, the averaged NOX emission of Shanghai, Hangzhou, Nanjing, and Ningbo is 139.65 mol/s, 84.49 mol/s, 79.87 mol/s and 88.73 mol/s, respectively. This estimation has a good correlation with Multi-resolution Emission Inventory for China (MEIC) emission with R more than 0.9 but lower results mainly due to the underestimation of NO2 VCD by TROPOMI in polluted areas. It was also found that the NOX emissions of Ningbo are higher than expected, which is closely related to massive ship emissions. This study indicates that this approach based on adjusted EMG model can enhance the ability to quantify NOX emissions at city level by utilizing the high spatial resolution observations of TROPOMI.

Impact of Prior Infection on Protection and Transmission of SARS-CoV-2 in Golden Hamsters (preprint)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over 100 million confirmed human infections, and 2 million more deaths globally since its emergence in the end of 2019. Several studies have shown that prior infection provided protective immunity against SARS-CoV-2 in non-human primate models. However, the effect of prior infection on blocking SARS-CoV-2 transmission is not clear. Here, we evaluated the impact of prior infection on protection and transmission of the SARS-CoV-2 virus in golden hamsters. Our results showed that prior infection provided protective immunity against SARS-CoV-2 re-challenge, but it was not sterizing immunity. The transmission experiment results showed that SARS-CoV-2 was efficiently transmitted from naive hamsters to prior infected hamsters by direct contact and airborne route, but not by indirect contact. Further, the virus was efficiently transmitted from prior infected hamsters to naive hamsters by direct contact, but not by airborne route and indirect contact. Surprisingly, the virus can be transmitted between prior infected hamsters by direct contact during a short period of early infection. Taken together, our study demonstrated that prior infected hamsters with good immunity can still be naturally re-infected, and the virus can be transmitted between prior infected hamsters and the naive through different transmission routes, implying the potential possibility of human re-infection and the risk of virus transmission between prior infected population and the healthy. Our study will help to calculate the herd immunity threshold more accurately, make more reasonable public health decisions, formulate an optimized population vaccination program, as well as aid the implementation of appropriate public health and social measures to control COVID-19.

Clinical Characteristics and Risk Factors of Disseminated Intravascular Coagulation in Patients with COVID-19 (preprint)

Background: Coronavirus disease 2019 (COVID-19) has spread globally. However, the association between COVID-19 and disseminated intravascular coagulation (DIC) has been scarcely addressed. We aimed to systematically characterize the clinical features and examine risk factors for DIC development in COVID-19 patients.Methods: In this single-centered, retrospective, and observational study, all patients with DIC (N=59) and 270 patients without DIC were matched by propensity score matching based on age, sex, and comorbidities. Demographic data, symptoms, radiological, laboratory examinations, and clinical outcomes were compared between patients with and without DIC. Furthermore, univariable and multivariable logistic regression were used to explore the risk factors associated with DIC development in COVID-19 patients.Results: Higher proportion of patients with DIC and COVID-19 (54 of 59 [91·53%]) developed into death than non DIC patients (58 of 270 [21·48%]). Patients with DIC presented aggravated inflammation responses, liver damage, and especially coagulation dysfunction. Moreover, in addition to previously reported coagulation-related markers, such as FDP, D-dimer, and platelet, we also identified several novel risk factors associated with DIC development, including decreased fibrinogen (OR=0·476, 95%CI=0·380-0·596, P<0·0001) and ALB (0·901, 0·845- 0·961, P=0·0015), and elevated IL-6 (1·010, 1·005-1·015, P=0·00017) and TNF-α (1·053, 1·016-1·091, P=0·0045).Conclusions: Patients with DIC and COVID-19 were predisposed to poor clinical outcomes. These risk factors identified may be helpful for early surveillance of disease progression and making standardized treatment strategies.

Clinical Characteristics and Risk Factors of Fatal Patients with COVID-19: a Retrospective Cohort Study in Wuhan, China (preprint)

Background The coronavirus disease 2019 (COVID-19) has caused global pandemic, resulting in considerable mortality. The risk factors, clinical treatments and especially comprehensive risk models for COVID-19 death are urgently warranted.Methods In this retrospective study, 281 non-survivors and 712 survivors with propensity score matching by age, sex and comorbidities were enrolled from January 13, 2020 to March 31, 2020.Results Higher SOFA, qSOFA, APACHE II and SIRS scores, hypoxia, elevated inflammatory cytokines, multi-organ dysfunction, decreased immune cells subsets and complications were significantly associated with the higher COVID-19 death risk. In addition to traditional predictors for death risk, including APACHE II (AUC = 0.83), SIRS (AUC = 0.75), SOFA (AUC = 0.70) and qSOFA scores (AUC = 0.61), another four prediction models that included immune cells subsets (AUC = 0.90), multiple organ damage biomarkers (AUC = 0.89), complications (AUC = 0.88) and inflammatory-related indexes (AUC = 0.75) were established. Additionally, the predictive accuracy of combining these risk factors (AUC = 0.950) was also significantly higher than that of each risk group alone, outperforming previous risk models, which was significant for early clinical management for COVID-19.Conclusions The potential risk factors could help to predict the clinical prognosis of COVID-19 patients at an early stage. The combined model might be more suitable for the death risk evaluation of COVID-19.

Distribution Of Medical Institutions In Cold Regions Of China Based On Public Health Emergencies (preprint)

Background：Recently, investments in the construction of medical resources have been increasing annually China, and consequently, the allocation of these resources has improved. However, the outbreak of covid-19 in 2020 highlights the problems in the distribution of medical institutions. After the occurrence of public health emergencies, the joint action of different levels of medical and health institutions can bring the role of urban medical and health system into full play. Therefore, after a global public health emergency, the study of medical institution distribution needs to be reconsidered.Methods：With the continuous application and development of GIS (Geographic Information System), the application of GIS in civil planning is relatively mature, and research investigating distribution has been conducted in depth. Based on this foundation, this paper analyzes the factors impacting distribution, such as the transportation system, land use characteristics and personal factors, by a weighted spatial separation model of a representative city in a cold region in China. Results：The data were sorted, edited and visually processed through the constructed geodatabase to perform an analysis of the spatial distributions of the factors impacting the accessibility of medical institutions in the study area. A weighted spatial separation model was built and applied to comprehensively consider several factors affecting accessibility, the accessibility of these medical institutions is significantly impacted when the spatial population distribution is considered as a factor in the weighted spatial separation model.Conclusions：The accessibility of medical institutions in this representative cold city in China was comparatively analyzed in this paper through theoretical research, software computations/simulations and model analysis based on the GIS paradigm. This study will help optimize the layout of medical institutions and improve medical equality.Trial registration: An ethics review and approval for this study was not required according to the local legislation and institutional requirements.

Distribution of Medical Institutions in Cold Regions of China Based on Public Health Emergencies (preprint)

Background：Recently, investments in the construction of medical resources have been increasing annually China, and consequently, the allocation of these resources has improved. However, the outbreak of covid-19 in 2020 highlights the problems in the distribution of medical institutions. After the occurrence of public health emergencies, the joint action of different levels of medical and health institutions can bring the role of urban medical and health system into full play. Therefore, after a global public health emergency, the study of medical institution distribution needs to be reconsidered.Methods：With the continuous application and development of GIS, the application of GIS in civil planning is relatively mature, and research investigating distribution has been conducted in depth. Based on this foundation, this paper analyzes the factors impacting distribution, such as the transportation system, land use characteristics and personal factors, by a weighted spatial separation model of a representative city in a cold region in China. Results：The data were sorted, edited and visually processed through the constructed geodatabase to perform an analysis of the spatial distributions of the factors impacting the accessibility of medical institutions in the study area. A weighted spatial separation model was built and applied to comprehensively consider several factors affecting accessibility, the accessibility of these medical institutions is significantly impacted when the spatial population distribution is considered as a factor in the weighted spatial separation model.Conclusions：The accessibility of medical institutions in this representative cold city in China was comparatively analyzed in this paper through theoretical research, software computations/simulations and model analysis based on the GIS paradigm. This study will help optimize the layout of medical institutions and improve medical equality.Trial registration: An ethics review and approval for this study was not required according to the local legislation and institutional requirements.

Distribution of Medical Institutions in Cold Regions of China Based on Public Health Emergencies (preprint)

Background: Recently, investments in the construction of medical resources have been increasing annually China, and consequently, the allocation of these resources has improved. However, the outbreak of covid-19 in 2020 highlights the problems in the distribution of medical institutions. After the occurrence of public health emergencies, the joint action of different levels of medical and health institutions can bring the role of urban medical and health system into full play. Therefore, after a global public health emergency, the study of medical institution distribution needs to be reconsidered.Methods: With the continuous application and development of GIS, the application of GIS in civil planning is relatively mature, and research investigating distribution has been conducted in depth. Based on this foundation, this paper analyzes the factors impacting distribution, such as the transportation system, land use characteristics and personal factors, by a weighted spatial separation model of a representative city in a cold region in China.Results: A weighted spatial separation model was built and applied to comprehensively consider several factors affecting accessibility, including the spatial coverage separation, the service areas separation, the road network separation, the population separation and the weather separation. To calculate the accessibility of medical institutions using a weighted spatial separation model, Harbin was chosen as a case study. The accessibility of medical institutions was analyzed.Conclusions: The accessibility of medical institutions in this representative cold city in China was comparatively analyzed in this paper through theoretical research, software computations/simulations and model analysis based on the GIS paradigm. This study will help optimize the layout of medical institutions and improve medical equality.Trial registration: An ethics review and approval for this study was not required according to the local legislation and institutional requirements.

Multi-epitope vaccine design using an immunoinformatics approach for 2019 novel coronavirus in China (SARS-CoV-2) (preprint)

A new coronavirus SARS-CoV-2 has caused over 9.2 million infection cases and 475758 deaths worldwide. Due to the rapid dissemination and the unavailability of specific therapy, there is a desperate need for vaccines to combat the epidemic of SARS-CoV-2. An in silico approach based on the available virus genome was applied to identify 19 high immunogenic B-cell epitopes and 499 human-leukocyte-antigen (HLA) restricted T-cell epitopes. Thirty multi-epitope peptide vaccines were designed by iNeo Suite, and manufactured by solid-phase synthesis. Docking analysis showed stable hydrogen bonds of epitopes with their corresponding HLA alleles. When four vaccine peptide candidates from the spike protein of SARS-CoV-2 were selected to immunize mice, a significantly larger amount of IgG in serum as well as an increase of CD19+ cells in ILNs was observed in peptide-immunized mice compared to the control mice. The ratio of IFN-{gamma}-secreting lymphocytes in CD4+ or CD8+ cells in the peptides-immunized mice were higher than that in the control mice. There were also a larger number of IFN-{gamma}-secreting T cells in spleen in the peptides-immunized mice. This study screened antigenic B-cell and T-cell epitopes in all encoded proteins of SARS-CoV-2, and further designed multi-epitope based peptide vaccine against viral structural proteins. The obtained vaccine peptides successfully elicited specific humoral and cellular immune responses in mice. Primate experiments and clinical trial are urgently required to validate the efficacy and safety of these vaccine peptides. ImportanceSo far, a new coronavirus SARS-CoV-2 has caused over 9.2 million infection cases and 475758 deaths worldwide. Due to the rapid dissemination and the unavailability of specific therapy, there is a desperate need for vaccines to combat the epidemic of SARS-CoV-2. Different from the development approaches for traditional vaccines, the development of our peptide vaccine is faster and simpler. In this study, we performed an in silico approach to identify the antigenic B-cell epitopes and human-leukocyte-antigen (HLA) restricted T-cell epitopes, and designed a panel of multi-epitope peptide vaccines. The resulting SARS-CoV-2 multi-epitope peptide vaccine could elicit specific humoral and cellular immune responses in mice efficiently, displaying its great potential in our fight of COVID-19.