Time-course RNA-Seq profiling reveals isoform-level gene expression dynamics of the cGAS-STING pathway.

The cGAS-STING pathway, orchestrating complicated transcriptome-wide immune responses, is essential for host antiviral defense but can also drive immunopathology in severe COVID-19. Here, we performed time-course RNA-Seq experiments to dissect the transcriptome expression dynamics at the gene-isoform level after cGAS-STING pathway activation. The in-depth time-course transcriptome after cGAS-STING pathway activation within 12 h enabled quantification of 48,685 gene isoforms. By employing regression models, we obtained 13,232 gene isoforms with expression patterns significantly associated with the process of cGAS-STING pathway activation, which were named activation-associated isoforms. The combination of hierarchical and k-means clustering algorithms revealed four major expression patterns of activation-associated isoforms, including two clusters with increased expression patterns enriched in cell cycle, autophagy, antiviral innate-immune functions, and COVID-19 coronavirus disease pathway, and two clusters showing decreased expression pattern that mainly involved in ncRNA metabolism, translation process, and mRNA processing. Importantly, by merging four clusters of activation-associated isoforms, we identified three types of genes that underwent isoform usage alteration during the cGAS-STING pathway activation. We further found that genes exhibiting protein-coding and non-protein-coding gene isoform usage alteration were strongly enriched for the factors involved in innate immunity and RNA splicing. Notably, overexpression of an enriched splicing factor, EFTUD2, shifted transcriptome towards the cGAS-STING pathway activated status and promoted protein-coding isoform abundance of several key regulators of the cGAS-STING pathway. Taken together, our results revealed the isoform-level gene expression dynamics of the cGAS-STING pathway and uncovered novel roles of splicing factors in regulating cGAS-STING pathway mediated immune responses.

The impact of COVID-19 pandemic on HIV care continuum in Jiangsu, China.

BACKGROUND: The COVID-19 pandemic seriously threatens general public health services globally. This study aimed to evaluate the impact of the COVID-19 pandemic on the HIV care continuum in Jiangsu province, China. METHODS: Data on newly diagnosed HIV persons for analysis were retrieved from Chinas' web-based Comprehensive Response Information Management System (CRIMS) for HIV/AIDS from 2016 to 2020. We recorded data for the first 3 months (January to March, 2020) of strictly implementing COVID-19 measures from publicly available disease databases of the Jiangsu provincial Health Committee. We used seasonal autoregressive integrated moving average (SARIMA) and exponential smoothing in forecasting the parameters. Subgroup differences were accessed using Chi-square tests. RESULTS: Compared to the estimated proportions, the HIV testing rates decreased by 49.0% (919,938) in the first three months of implementing COVID-19 measures. Of an estimated 1555 new HIV diagnosis expected in the same period, only 63.0% (980) new diagnoses were recorded. According to actual data recorded during the said period, 980 positively tested persons received confirmatory tests, of which 71.4% (700) were reportedly linked to care. And only 49.5% (235) out of the expected 475 newly diagnosed HIV persons received CD4 cell count testing. Meanwhile 91.6% (208) of newly diagnosed HIV persons who received CD4 count tests reportedly initiated antiretroviral therapy (ART) compared to the 227 expected. Compared to the same period from 2016 to 2019, PLWH less than 30 years old and migrants were more likely to be affected by the COVID-19 policies. CONCLUSIONS: The COVID-19 pandemic negatively impacted HIV healthcare systems in Jiangsu, China. Further measures that can counter the impact of the pandemic are needed to maintain the HIV care continuum.

Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic.

Nowadays people are mostly focused on their work while ignoring their health which in turn is creating a drastic effect on their health in the long run. Remote health monitoring through telemedicine can help people discover potential health threats in time. In the COVID-19 pandemic, remote health monitoring can help obtain and analyze biomedical signals including human body temperature without direct body contact. This technique is of great significance to achieve safe and efficient health monitoring in the COVID-19 pandemic. Existing remote biomedical signal monitoring methods cannot effectively analyze the time series data. This paper designs a remote biomedical signal monitoring framework combining the Internet of Things (IoT), 5G communication and artificial intelligence techniques. In the constructed framework, IoT devices are used to collect biomedical signals at the perception layer. Subsequently, the biomedical signals are transmitted through the 5G network to the cloud server where the GRU-AE deep learning model is deployed. It is noteworthy that the proposed GRU-AE model can analyze multi-dimensional biomedical signals in time series. Finally, this paper conducts a 24-week monitoring experiment for 2000 subjects of different ages to obtain real data. Compared with the traditional biomedical signal monitoring method based on the AutoEncoder model, the GRU-AE model has better performance. The research has an important role in promoting the development of biomedical signal monitoring techniques, which can be effectively applied to some kinds of remote health monitoring scenario.