Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data

Long noncoding RNAs (lncRNAs) play critical regulatory roles in human development and disease. Although there are over 100,000 samples with available RNA sequencing (RNA-seq) data, many lncRNAs have yet to be annotated. The conventional approach to identifying novel lncRNAs from RNA-seq data is to find transcripts without coding potential but this approach has a false discovery rate of 30–75%. Other existing methods either identify only multi-exon lncRNAs, missing single-exon lncRNAs, or require transcriptional initiation profiling data (such as H3K4me3 ChIP-seq data), which is unavailable for many samples with RNA-seq data. Because of these limitations, current methods cannot accurately identify novel lncRNAs from existing RNA-seq data. To address this problem, we have developed software, Flnc, to accurately identify both novel and annotated full-length lncRNAs, including single-exon lncRNAs, directly from RNA-seq data without requiring transcriptional initiation profiles. Flnc integrates machine learning models built by incorporating four types of features: transcript length, promoter signature, multiple exons, and genomic location. Flnc achieves state-of-the-art prediction power with an AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the conventional approach to over 85%. Flnc is available via GitHub platform

A Universal Screening Strategy for SARS-CoV-2 Infection in Intensive Care Units: Korean Experience in a Single Hospital

Background: Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection is not differentiated clinically from other respiratory infections, and intensive care units (ICUs) are vulnerable to in-hospital transmission due to interventions inducing respiratory aerosols. This study evaluated the effectiveness of universal SARS-CoV-2 screening in ICUs in terms of screened-out cases and reduction in anxiety of healthcare personnel (HCP). Materials and Methods: This prospective single-armed observational study was conducted in 2 ICUs of a single hospital. The number of patients diagnosed with SARS-CoV-2 infection by the screening program and healthcare workers in ICUs that visited the SARS-CoV-2 screening clinic or infection clinic were investigated. Results: During the 7-week study period, no positive screening case was reported among a total of 142 patients. Among 86 HCP in the ICUs, only 2 HCP sought medical consultation for SARS-CoV-2 infection during the initial 2 weeks. Conclusion: A universal screening program for SARS-CoV-2 infection in ICUs with the coordination of other countermeasures in the hospital was reasonably effective in preventing in-hospital transmission in a pandemic situation and making clinical practices and HCP stable.Y

Clinical Application of the Standard Q COVID-19 Ag Test for the Detection of SARS-CoV-2 Infection

We evaluated the Standard Q COVID-19 Ag test for the diagnosis of coronavirus disease 2019 (COVID-19) compared to the reverse transcription-polymerase chain reaction (RT-PCR) test. We applied both tests to patients who were about to be hospitalized, had visited an emergency room, or had been admitted due to COVID-19 confirmed by RT-PCR. Two nasopharyngeal swabs were obtained; one was tested by RT-PCR and the other by the Standard Q COVID-19 Ag test. A total of 118 pairs of tests from 98 patients were performed between January 5 and 11, 2021. The overall sensitivity and specificity for detecting severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) for the Standard Q COVID-19 Ag test compared to RT-PCR were 17.5% (95% confidence interval [CI], 8.8-32.0%) and 100% (95% CI, 95.3-100.0%). Analysis of the results using RT-PCR cycle thresholds of <= 30 or <= 25 increased the sensitivity to 26.9% (95% CI, 13.7- 46.1%), and 41.1% (95% CI, 21.6-64.0%), respectively.Y