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The SARS-CoV-2 coronavirus is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network, and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs, and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The majority types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.
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BackgroundCOVID-19 is a highly transmissible infectious disease that has infected over 122 million individuals worldwide. To combat this pandemic, governments around the world have imposed lockdowns. However, the impact of these lockdowns on the rates of COVID-19 transmission in communities is not well known. Here, we used COVID-19 case counts from 3,000+ counties in the United States (US) to determine the relationship between lockdown as well as other county factors and the rate of COVID-19 spread in these communities. MethodsWe merged county-specific COVID-19 case counts with US census data and the date of lockdown for each of the counties. We then applied a Functional Principal Component (FPC) analysis on this dataset to generate scores that described the trajectory of COVID-19 spread across the counties. We used machine learning methods to identify important factors in the county including the date of lockdown that significantly influenced the FPC scores. FindingsWe found that the first FPC score accounted for up to 92.81% of the variations in the absolute rates of COVID-19 as well as the topology of COVID-19 spread over time at a county level. The relation between incidence of COVID-19 and time at a county level demonstrated a hockey-stick appearance with an inflection point approximately 7 days prior to the county reporting at least 5 new cases of COVID-19; beyond this inflection point, there was an exponential increase in incidence. Among the risk factors, lockdown and total population were the two most significant features of the county that influenced the rate of COVID-19 infection, while the median family income, median age and within-county move also substantially affect COVID spread. InterpretationLockdowns are an effective way of controlling the COVID-19 spread in communities. However, significant delays in lockdown cause a dramatic increase in the case counts. Thus, the timing of the lockdown relative to the case count is an important consideration in controlling the pandemic in communities. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed using the term "coronavirus", OR "COVID-19", OR "COVID-19 infection", OR "SARS-CoV-2" combined with "Lockdown" or "sociodemographic factor" or "Vulnerability" for original articles published before March 18, 2021. Similar searches were done in medRxiv, Google Scholar, and Web of Science. Only papers published in English were reviewed. The most similar relevant works to our study were Acharya et al.1 and Karmakar et al.2, which investigated the associations between population-level social factors and COVID-19 incidence and mortality. Unlike our current study, which employed a longitudinal design, both of studies were cross-sectional in nature and thus fixed on a single time point. In addition, neither of these studies investigated the impact of lockdown measures on COVID-19 infection patterns. Another relevant study is Alfano et al.s work3, which focused on the efficacy of lockdown on COVID-19 case rates. However, this study did not evaluate the timing of lockdown on this endpoint. Added value of this studyTo our knowledge, this is the first study to use functional principal component analysis (FPCA) to investigate COVID-19 infection trajectories (in a longitudinal manner) and their relationships with different sociodemographic factors and lockdown policy at a county level. The FPCA transformed a longitudinal vector with high-dimensions into a "single" surrogate variable, which retained 93% of the information. We used an advanced statistical model (segmented regression) to investigate the effects of lockdown on incidence of COVID-19 across the US. We found that the relationship had a "hockey stick" appearance with an inflection point at [~]7 days prior to a county reporting at least 5 cases of COVID-19. We also applied a machine learning model (i.e., elastic net) to explore joint effects of lockdown and other sociodemographic factors on COVID-19 infection patterns, which estimated the impact of each of factors, adjusted for each other. Implications of all the available evidenceOur study suggests that lockdown is an effective policy to reduce case counts of COVID-19 in communities; however, significant delays in its implementation result in exponential growth of COVID-19. The inflection point is approximately 7 days prior to a county reporting at least 5 cases of COVID-19. These data will help policy-makers to determine the optimal timing of lockdowns for their communities.
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Objective To investigate the exposure levels of organic pollutants in child-bearing age women body. Methods The blood and urine specimens were collected simultaneously from each of 8 health women, aged 23-32 years, the organics were extracted with hexane and determined qualitatively by gas chromatography-mass spectrometer (GC-MS) for each specimen. Results In blood, 34 kinds of organic compounds had been detected , at average of (8.63?5.01 )kinds per person. Di-n-butyl phthalate (100%) and 14-bate-H-pregna (75.0%) revealed the highest detectable rates. In urine, 39 kinds of organic compounds had been identified, at average of (10.63?1.30) kinds per person, di-n-butyl phthalate(100%), HANFETT(100%), 14-bate-H-pregna(87.5%), docosane (87.5%), di-isobutyl phthalate(75.0%) were the chemicals noticed more frequently. Some kinds of these identified organics compounds were environmental toxic pollutants. Conclusion This study suggested that toxic organic pollutants had existed in child-bearing age women body, phthalic acid esters were the chemicals checked out more frequently. Their potential adverse health effects on women and offspring should be paid highly attention to.