5%) died by 28 days after diagnosis. Specific underlying comorbidities (age >65 [aOR 3.0, 95%CI 1.7-5.5, p<0.001], congestive heart failure [aOR 3.2, 95%CI 1.4-7.0, p=0.004], chronic lung disease [aOR 2.5, 95%CI 1.2-5.2, p=0.018], obesity [aOR 1.9, 95% CI 1.0-3.4, p=0.039]) and presenting findings (lymphopenia [aOR 1.9, 95%CI 1.1-3.5, p=0.033], abnormal chest imaging [aOR 2.9, 95%CI 1.1-7.5, p=0.027]) were independently associated with mortality. Multiple measures of immunosuppression intensity were not associated with mortality.
Mortality among SOT recipients hospitalized for COVID-19 was 20.5%. Age and underlying comorbidities rather than immunosuppression intensity-related measures were major drivers of mortality.
Mortality among SOT recipients hospitalized for COVID-19 was 20.5%. Age and underlying comorbidities rather than immunosuppression intensity-related measures were major drivers of mortality.
Ribbon is an alignment visualization tool that shows how alignments are positioned within both the reference and read contexts, giving an intuitive view that enables a better understanding of structural variants and the read evidence supporting them. Ribbon was born out of a need to curate complex structural variant calls and determine whether each was well supported by long-read evidence, and it uses the same intuitive visualization method to shed light on contig alignments from genome-to-genome comparisons.
Ribbon is freely available online at http//genomeribbon.com/ and is open-source at https//github.com/marianattestad/ribbon.
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Rapid increase of the data size in metagenome researches has raised the demand for new tools to process large datasets efficiently. In order to accelerate the metagenome profiling process in the scenario of big data, we developed SOAPMetaS, a marker gene-based multiple-sample metagenome profiling tool built on Apache Spark. SOAPMetaS demonstrates high performance and scalability to process large datasets. It can process 80 samples of FASTQ data, summing up to 416 GiB, in around half an hour; and the accuracy of species profiling results of SOAPMetaS is similar to that of MetaPhlAn2. SOAPMetaS can deal with a large volume of metagenome data more efficiently than common-used single-machine tools.
Source code is implemented in Java and freely available at https//github.com/BGI-flexlab/SOAPMetaS .
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Plasma chemokines are biomarkers of greater disease severity, higher bacterial burden and delayed sputum culture conversion in pulmonary tuberculosis (PTB). Whether plasma chemokines could also serve as biomarkers of unfavorable treatment outcomes in PTB is not known.
A cohort of newly diagnosed, sputum smear and culture positive adult individuals with drug-sensitive PTB were recruited under the Effect of diabetes on Tuberculosis Severity study in Chennai, India. Plasma chemokine levels measured before treatment initiation were compared between 68 cases with unfavorable outcomes (treatment failure, death or recurrence) and 136 control individuals who had recurrence-free cure. VLS-1488 manufacturer A second validation cohort comprising of newly diagnosed, culture positive adults with drug-sensitive TB was used to measure plasma chemokine levels in 20 cases and 40 controls.
Six chemokines (CCL2, CCL3, CCL4, CXCL8, CXCL10 and CX3CL1) were associated with increased risk, while CXCL1 was associated with decreased risk of unfavorable outcomes in unadjusted and adjusted analyses in the test cohort. Similarly, CCL3, CXCL8 and CXCL10 were associated with increased risk of unfavorable treatment outcomes in the validation cohort. Receiver operating characteristic analysis revealed combinations of CCL3, CXCL8 and CXCL10 exhibited very high sensitivity and specificity in differentiating cases versus controls.
Our study reveals a plasma chemokine signature that can be used as a novel biomarker for predicting adverse treatment outcomes in PTB.
Our study reveals a plasma chemokine signature that can be used as a novel biomarker for predicting adverse treatment outcomes in PTB.
Protein carbonylation is one of the most important oxidative stress-induced post-translational modifications (PTMs), which is generally characterized as stability, irreversibility and relative early formation. It plays significant role in orchestrating various biological processes and has been already demonstrated to be related to many diseases. However, the experimental technologies for carbonylation sites identification are not only costly and time-consuming, but also unable of processing a large number of proteins at a time. Thus, rapidly and effectively identifying carbonylation sites by computational methods will provide key clues for the analysis of occurrence and development of diseases.
In this study, we developed a predictor called iCarPS to identify carbonylation sites based on sequence information. A novel feature encoding scheme called residues conical coordinates combined with their physicochemical properties was proposed to formulate carbonylated protein and non-carbonylated protein samples. To remove potential redundant features and improve the prediction performance, a feature selection technique was utilized. The accuracy and robustness of iCarPS were proved by experiments on training and independent datasets. Comparison with other published methods demonstrated that the proposed method is powerful and could provide powerful performance for carbonylation sites identification.
Based on the proposed model, a user-friendly webserver and a software package were constructed, which can be freely accessed at http//lin-group.cn/server/iCarPS.
Based on the proposed model, a user-friendly webserver and a software package were constructed, which can be freely accessed at http//lin-group.cn/server/iCarPS.
Rapid developments in sequencing technologies have boosted generating high volumes of sequence data. To archive and analyze those data, one primary step is sequence comparison. Alignment-free sequence comparison based on k-mer frequencies offers a computationally efficient solution, yet in practice, the k-mer frequency vectors for large k of practical interest lead to excessive memory and storage consumption.
We report CRAFT, a general genomic/metagenomic search engine to learn compact representations of sequences and perform fast comparison between DNA sequences. Specifically, given genome or high throughput sequencing (HTS) data as input, CRAFT maps the data into a much smaller embedding space and locates the best matching genome in the archived massive sequence repositories. With 102 - 104-fold reduction of storage space, CRAFT performs fast query for gigabytes of data within seconds or minutes, achieving comparable performance as six state-of-the-art alignment-free measures.
CRAFT offers a user-friendly graphical user interface with one-click installation on Windows and Linux operating systems, freely available at https//github.VLS-1488 manufacturer
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