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The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.

Background:The ongoing outbreak of COVID-19 has spread rapidly and sparked global concern. While the transmission of SARS-CoV-2 through human respiratory droplets and contact with infected persons is clear, the aerosol transmission of SARS-CoV-2 has been little studied.Methods: Thirty-five aerosol samples of three different types (total suspended particle, size segregated and deposition aerosol) were collected in Patient Areas (PAA) and Medical Staff Areas (MSA) of Renmin Hospital of Wuhan University (Renmin) and Wuchang Fangcang Field Hospital (Fangcang), and Public Areas (PUA) in Wuhan, China during COVID-19 outbreak. A robust droplet digital polymerase chain reaction (ddPCR) method was employed to quantitate the viral SARS-CoV-2 RNA genome and determine aerosol RNA concentration. Results:The ICU, CCU and general patient rooms inside Renmin, patient hall inside Fangcang had undetectable or low airborne SARS-CoV-2 concentration but deposition samples inside ICU and air sample in Fangcang patient toilet tested positive. The airborne SARS-CoV-2 in Fangcang MSA had bimodal distribution with higher concentration than those in Renmin during the outbreak but turned negative after patients number reduced and rigorous sanitization implemented. PUA had undetectable airborne SARS-CoV-2 concentration but obviously increased with accumulating crowd flow. Conclusions:Room ventilation, open space, proper use and disinfection of toilet can effectively limit aerosol transmission of SARS-CoV-2. Gathering of crowds with asymptomatic carriers is a potential source of airborne SARS-CoV-2. The virus aerosol deposition on protective apparel or floor surface and their subsequent resuspension is a potential transmission pathway and effective sanitization is critical in minimizing aerosol transmission of SARS-CoV-2.

Long non-coding RNA was dismissed as merely transcriptional "noise" in the past decades. Numerous researches have shown that lncRNAs regulated gene expression at the epigenetic level. Moreover, lncRNAs played important roles in proliferation, apoptosis and invasiveness of tumor cells, and participated in metastatic capacity of cancers. Recent studies revealed HOX transcript antisense RNA, a lncRNA with regulatory functions of transcription, could bind PRC2 and LSD1/CoREST/REST complexes and direct to the specific gene sites, resulted in H3K27 methylation and H3K4 demethylation and ultimately gene silencing. Aberrant HOTAIR expression was associated with various sites of cancers such as breast, hepatocellular, gastric, colorectal, pancreatic et al; and affected survival and prognosis of cancer patients. In this review, we introduce an overall view of HOTAIR by describing the known molecular mechanisms and potential functions of HOTAIR and summarizing the latest progresses on the research of HOTAIR in various human cancers.Key words: HOTAIR, LncRNA, HOX genes, cancer Long non-coding RNAs (LncRNAs) are commonly defined as RNA moleculars larger than 200 nucleotides. LncRNA was first found among transcribed DNA product of the mouse by Okazaki [1]. Many identified lncRNAs were transcribed by RNA polymerase II (RNA pol II) and spliced [2,3]. One view existed in the past few decades that lncRNAs were as diverse as their better known counterpart messenger RNAs (mRNAs), having no protein-coding capacity, were described as transcriptional "noise" [4,5,6]. However, a number of studies have shown that some lncRNAs were involved in embryogenesis and differentiation [7,8]. These lncRNAs are expressed in specific cell types [9, 10, 11] and located in specific subcellular compartments [12,13,14]. Moreover, recent studies have indicated that lncRNAs participated in a wide range of biological pathways and cellular processes. They could regulate gene expression and function, including dosage compensation [15,16] [18,36].HOX transcript antisense RNA (HOTAIR) is a lncRNA which has regulatory functions of transcription and are transcribed from the antisense strand of homeobox C gene locus in chromosome 12. HOTAIR recruits Polycomb Repressive Complex 2 (PRC2) and histone demethylase complex [LSD1 (lysine specific demethylase 1)/CoREST (Co-repressor of RE1-silencing transcription factor)/REST]; then leads to histone H3 tri-methylated at lysine 27 (H3K27me3) and histone H3 dimethyl Lys4 (H3K4me2); consequently results in gene silencing. In addition to its scaffold function to assemble transcription regulators, and a latest study reported that HO-TAIR could also serve as a platform to control protein levels via the ubiquitin-proteasome pathway. HOTAIR facilitated the ubiquitination of Ataxin-1 by Dzip3 and Snurportin-1 by Mex3b, and then accelerated their degradation. Moreover, HOTAIR levels were highly upregulated in senescent cells. It

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