Uptonhenson7094

The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads is valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this paper we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes.

We show that for small datasets OGRE outperforms other read binners in terms of the number of species included in a cluster, also referred to as cluster purity, and the fraction of all reads that is placed in one of the clusters. Furthermore, OGRE is able to process metagenomic datasets that are too large for other read binners into clusters with high cluster purity.

OGRE is the only method that can successfully cluster reads in species-specific clusters for large metagenomic datasets without running into computation time- or memory issues.

Code is made available on Github (https//github.com/Marleen1/OGRE).

Code is made available on Github (https//github.com/Marleen1/OGRE).

Heart failure with preserved ejection fraction (HFpEF) is a multifactorial disease that constitutes several distinct phenotypes, including a common cardiometabolic phenotype with obesity and type 2 diabetes mellitus. Treatment options for HFpEF are limited, and development of novel therapeutics is hindered by the paucity of suitable preclinical HFpEF models that recapitulate the complexity of human HFpEF. Metabolic drugs, like Glucagon Like Peptide Receptor Agonist (GLP-1RA) and Sodium Glucose Transporter 2 inhibitors (SGLT2i), have emerged as promising drugs to restore metabolic perturbations and may have value in the treatment of the cardiometabolic HFpEF phenotype. We aimed to develop a multifactorial HFpEF mouse model that closely resembles the cardiometabolic HFpEF phenotype, and evaluated the GLP-1 RA liraglutide and a SGLT2i dapagliflozin.

Aged (18-22 months old) female C57BL/6J mice were fed a standardized chow (CTRL) or high fat diet (HFD) for 12 weeks. After 8 weeks HFD, Angiotensin-II (ANGII), his study we developed a murine model that includes advanced age, female sex, in concert with co-morbidities elevated blood pressure, obesity and T2DM. We demonstrate that this model recapitulates the human cardiometabolic HFpEF phenotype. We showed that contemporary glucose lowering drugs, liraglutide and dapagliflozin, which are both under study for HFpEF, have positive results. Our model may be useful to evaluate novel cardiometabolic, anti-fibrotic, and anti-inflammatory treatments for HFpEF.

The HEART (history, electrocardiogram [ECG], age, risk factors, troponin) pathway is a useful tool in the emergency department to identify patients that are safe for outpatient evaluation of chest pain. A dedicated HEART Clinic to follow-up versus primary care remains a topic that requires further delineation. We sought to identify how many patients discharged on the HEART pathway specifically followed up with the established HEART Clinic.

This is a secondary analysis of a previously published dataset. In an initial validation study of the HEART Pathway, 625 consecutive subjects were identified via chart review, 449 of which were included. TG101348 cell line We identified subjects for inclusion in this study if they were found to have a HEART score of 3 or less. Subjects were excluded if they were admitted or if their follow-up was beyond 6weeks.

Of the 449 subjects, 185 met criteria for study inclusion. 125 (67.6%) had follow-up with an average time of 7.94days (95% CI 6.54-9.34). Of those, half had additional testing such as ECG, cardiac computed tomography angiography, and treadmill stress testing. The most common clinics for follow-up were the Family Medicine, Internal Medicine, and HEART Clinic representing 35.8, 29, and 18% of the follow-ups, respectively. No subject died, had a myocardial infarction, or required reperfusion.

Of the subjects discharged on the HEART Pathway, 67.6% followed up. Of those subjects that followed up, 18% did so at the HEART Clinic.

Of the subjects discharged on the HEART Pathway, 67.6% followed up. Of those subjects that followed up, 18% did so at the HEART Clinic.

The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been performed to identify viruses by the enzymes that they encode, and which are key to pathogen propagation.

We built a comprehensive scientific resource, SARS2020, that integrates coronavirus-related research, genomic sequences, and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the Severe Acute Respiratory Syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics.

SARS2020 is available at http//design.rxnfinder.org/sars2020/.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Record Linkage has versatile applications in real-world data analysis contexts, where several data sets need to be linked on the record level in the absence of any exact identifier connecting related records. An example are medical databases of patients, spread across institutions, that have to be linked on personally identifiable entries like name, date of birth or ZIP code. At the same time, privacy laws may prohibit the exchange of this personally identifiable information (PII) across institutional boundaries, ruling out the outsourcing of the record linkage task to a trusted third party. We propose to employ privacy-preserving record linkage (PPRL) techniques that prevent, to various degrees, the leakage of PII while still allowing for the linkage of related records.

We develop a framework for fault-tolerant PPRL using secure multi-party computation with the medical record keeping software Mainzelliste as the data source. Our solution does not rely on any trusted third party and all PII is guaranteed to not leak under common cryptographic security assumptions.