ABSTRACT
COVID-19 induces acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) and leads to severe immunological changes that threatens the lives of COVID-19 victims. Studies have shown that both the regulatory T cells and macrophages were deranged in COVID-19-induced ALI. Herbal drugs have long been utilized to adjust the immune microenvironment in ALI. However, the underlying mechanisms of herbal drug mediated ALI protection are largely unknown. This study aims to understand the cellular mechanism of a traditional Chinese medicine, Qi-Dong-Huo-Xue-Yin (QD), in protecting against LPS induced acute lung injury in mouse models. Our data showed that QD intrinsically promotes Foxp3 transcription via promoting acetylation of the Foxp3 promoter in CD4+ T cells and consequently facilitates CD4+CD25+Foxp3+ Tregs development. Extrinsically, QD stabilized ß-catenin in macrophages to expedite CD4+CD25+Foxp3+ Tregs development and modulated peripheral blood cytokines. Taken together, our results illustrate that QD promotes CD4+CD25+Foxp3+ Tregs development via intrinsic and extrinsic pathways and balanced cytokines within the lungs to protect against LPS induced ALI. This study suggests a potential application of QD in ALI related diseases.
ABSTRACT
Background Relation extraction is a fundamental task for extracting gene-disease associations from biomedical text. Existing tools have limited capacity, as they can extract gene-disease associations only from single sentences or abstract texts. Results In this work, we propose RENET2, a deep learning-based relation extraction method, which implements section filtering and ambiguous relations modeling to extract gene-disease associations from full-text articles. We designed a novel iterative training data expansion strategy to build an annotated full-text dataset to resolve the scarcity of labels on full-text articles. In our experiments, RENET2 achieved an F1-score of 72.13% for extracting gene-disease associations from an annotated full-text dataset, which was 27.22%, 30.30% and 29.24% higher than the best existing tools BeFree, DTMiner and BioBERT, respectively. We applied RENET2 to (1) ~1.89M full-text articles from PMC and found ~3.72M gene-disease associations; and (2) the LitCovid articles set and ranked the top 15 proteins associated with COVID-19, supported by recent articles. Conclusion RENET2 is an efficient and accurate method for full-text gene-disease association extraction. The source-code, manually curated abstract/full-text training data, and results of RENET2 are available at https://github.com/sujunhao/RENET2 .
Subject(s)
COVID-19ABSTRACT
Studies of novel coronavirus disease (COVID-19) have reported varying estimates of epidemiological parameters such as serial intervals and reproduction numbers. By compiling a unique line-list database of transmission pairs in mainland China, we demonstrated that serial intervals of COVID-19 have shortened substantially from a mean of 7.8 days to 2.6 days within a month. This change is driven by enhanced non-pharmaceutical interventions, in particular case isolation. We also demonstrated that using real-time estimation of serial intervals allowing for variation over time would provide more accurate estimates of reproduction numbers, than by using conventional definition of fixed serial interval distributions. These findings are essential to improve the assessment of transmission dynamics, forecasting future incidence, and estimating the impact of control measures.
Subject(s)
Coronavirus Infections , COVID-19ABSTRACT
As a novel coronavirus (COVID-19) continues to spread widely and claim lives worldwide, its transmission characteristics remain uncertain. Here, we present and analyze the serial intervals-the time period between the onset of symptoms in an index (infector) case and the onset of symptoms in a secondary (infectee) case-of 339 confirmed cases of COVID-19 identified from 264 cities in mainland China prior to February 19, 2020. Here, we provide the complete dataset in both English and Chinese to support further COVID-19 research and modeling efforts.
Subject(s)
COVID-19ABSTRACT
Question: What are the characteristics of household and social transmissions of COVID-19 areas outside of epidemic centers? Findings: Based on 1,407 COVID-19 reported infection events in China outside of Hubei Province between 20 January and 19 February 2020, we estimate the distribution of secondary infection sizes, frequency of super spreading events, serial intervals and age-stratified hazard of infection. Young and older people have higher risks of being infected with households while males 65+ of age are responsible for a disproportionate number of household infections. Meaning: This report is the first large-scale analysis of the household and social transmission events in the COVID-19 pandemic.
Subject(s)
COVID-19ABSTRACT
As a novel coronavirus (COVID-19) continues to emerge throughout China and threaten the globe, its transmission characteristics remain uncertain. Here, we analyze the serial intervals-the time period between the onset of symptoms in an index (infector) case and the onset of symptoms in a secondary (infectee) case-of 468 infector-infectee pairs with confirmed COVID-19 cases reported by health departments in 18 Chinese provinces between January 21, 2020, and February 8, 2020. The reported serial intervals range from -11 days to 20 days, with a mean of 3.96 days (95% confidence interval: 3.53-4.39), a standard deviation of 4.75 days (95% confidence interval: 4.46-5.07), and 12.6% of reports indicating pre-symptomatic transmission.