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Silverman Stilling posted an update 6 months, 3 weeks ago
BACKGROUND Corona Virus Disease-2019 (COVID-19) has spread widely throughout the world since the end of 2019. Nucleic acid testing (NAT) has played an important role in patient diagnosis and management of COVID-19. In some circumstances, thermal inactivation at 56 °C has been recommended to inactivate Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) before NAT. However, this procedure could theoretically disrupt nucleic acid integrity of this single-stranded RNA virus and cause false negatives in real-time polymerase chain reaction (RT-PCR) tests. METHODS We investigated whether thermal inactivation could affect the results of viral NAT. We examined the effects of thermal inactivation on the quantitative RT-PCR results of SARS-CoV-2 particularly with regard to the rates of false-negative results for specimens carrying low viral loads. We additionally investigated the effects of different specimen types, sample preservation times and a chemical inactivation approach on NAT. RESULTS Our work showed increased Ct values in specimens from diagnosed COVID-19 patients in RT-PCR tests after thermal incubation. Moreover, about half of the weak-positive samples (7 of 15 samples, 46.7%) were RT-PCR negative after heat inactivation in at least one parallel testing. The use of guanidinium-based lysis for preservation of these specimens had a smaller impact on RT-PCR results with fewer false negatives (2 of 15 samples, 13.3%) and significantly less increase in Ct values than heat inactivation. CONCLUSION Thermal inactivation adversely affected the efficiency of RT-PCR for SARS-CoV-2 detection. Given the limited applicability associated with chemical inactivators, other approaches to ensure the overall protection of laboratory personnel need consideration. © 2020 American Association for Clinical Chemistry.MOTIVATION Single-cell RNA-sequencing (scRNA-Seq) profiles transcriptome of individual cells, which enables the discovery of cell types or subtypes by using unsupervised clustering. Current algorithms perform dimension reduction before cell clustering because of noises, high dimensionality, and linear inseparability of scRNA-seq data. However, independence of dimension reduction and clustering fails to fully characterize patterns in data, resulting in an undesirable performance. RESULTS In this study, we propose a flexible and accurate algorithm for scRNA-Seq data by jointly learning dimension reduction and cell clustering (aka DRjCC), where dimension reduction is performed by projected matrix decomposition and cell type clustering by nonnegative matrix factorization. We first formulate joint learning of dimension reduction and cell clustering into a constrained optimization problem and then derive the optimization rules. The advantage of DRjCC is that feature selection in dimension reduction is guided by cell clustering, significantly improving the performance of cell type discovery. Eleven scRNA-seq datasets are adopted to validate the performance of algorithms, where the number of single cells varies from 49 to 68,579 with the number of cell types ranging from 3 to 14. The experimental results demonstrate that DRjCC significantly outperforms 13 state-of-the-art methods in terms of various measurements on cell type clustering (on average 17.44% by improvement). Furthermore, DRjCC is efficient and robust across different scRNA-seq datasets from various tissues. The proposed model and methods provide an effective strategy to analyze scRNA-seq data (The software is coded using matlab, and is free available for academichttps//github.com/xkmaxidian/DRjCC). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION The elucidation of all inter protein interactions would significantly enhance our knowledge of cellular processes at a molecular level. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study we have devised a new scheme for scoring protein-protein interactions. RESULTS Our method, PIZSA (Protein Interaction Z Score Assessment) is a binary classification scheme for identification of native protein quaternary assemblies (binders/non-binders) based on statistical potentials. The scoring scheme incorporates residue-residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other contact based potential scoring functions. The method has been extensively benchmarked and is among the top 6 methods, outperforming 31 other statistical, physics based and machine learning scoring schemes. The PIZSA potentials can also distinguish crystallization artefacts from biological interactions. AVAILABILITY PIZSA is implemented as a webserver at http//cospi.iiserpune.ac.in/pizsa and can be downloaded as a standalone package from http//cospi.iiserpune.ac.in/pizsa/Download/Download.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.BACKGROUND The aim of this study was to investigate the imaging manifestations of early-stage coronavirus disease 2019 (COVID-19) and to provide imaging basis for early detection of suspected cases and stratified intervention. MATERIAL AND METHODS From 20 January 2020 to 2 February 2020, 6 patients diagnosed with COVID-19, including 1 male and 5 females, were retrospectively reviewed in Zhejiang Hospital. These cases were clinically assessed and classified as common COVID-19. All patients underwent thoracic high-resolution computed tomography (HRCT) within 2 days after the onset of symptoms, and their images were viewed by 2 radiologists who were blind to their clinical records. Uprosertib cost RESULTS CT images of 6 confirmed patients were collected. Two of the 6 patients (33.3%) had bilateral lung involvements and 4 (66.7%) had single-lung involvement. Two cases (33.3%) had a single lesion, 2 cases (33.3%) had 2 lesions, and 2 cases (33.3%) had multiple lesions. There were 2 cases (33.3%) with focal subpleural distribution and 1 case (16.
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