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Novel coronavirus disease 2019 (COVID-19) is known to cause severe bilateral pneumonia and acute respiratory distress syndrome (ARDS), leading to difficulty breathing requiring mechanical ventilation and ICU management. In many patients, it has been found to cause severe hypercoagulability. We present a case of COVID-19 positive patient who developed myocardial infarction (MI) despite being on multiple anticoagulants. A 51-year-old, Middle-Eastern male diabetic patient presented to the emergency room with complaints of sudden onset left leg pain, paresthesias, and swelling for one day. On physical examination, the left leg was cool to touch from forefoot to mid-calf, with noticeable mottling over the forefoot and a nonpalpable dorsalis pedis. The patient was started on therapeutic enoxaparin and diltiazem in ED. Chest X-ray showed bilateral pulmonary infiltrates beginning peripherally and COVID-19 pneumonitis. The patient underwent a mechanical thrombectomy and was loaded with aspirin/clopidogrel, heparin drip, and enoxaparin. Despite being on triple anticoagulation, the patient had new-onset STEMI and elevated troponin levels. On angiography, the patient was found to have occluded mid-left anterior descending, most likely from acute on chronic thrombosis related to the patient’s COVID-19 status. As flow could not be re-established, the patient was kept on long-term protective anticoagulation-triple therapy (an oral anticoagulant and dual antiplatelet therapy) and received pulmonary care for COVID-19 infection. The patient was discharged on long-term triple anticoagulation and COVID-19 precautions with scheduled retesting and follow-up.

Cardiovascular complications following cocaine use are well described. We present a case of myocardial infarction and ventricular rupture in a young individual with limited underlying coronary disease and habitual cocaine use. The role of each is discussed.

Tandem mass spectrometry (MS/MS) experiments yield multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra they generate. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about peptide spectra that are common across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with new ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ∼1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archives rather than be analyzed as disparate datasets, as is mostly the case today.

The diverse range of mass spectrometry (MS) instrumentation along with corresponding proprietary and nonproprietary data formats has generated a proteomics community driven call for a standardized format to facilitate management, processing, storing, visualization, and exchange of both experimental and processed data. To date, significant efforts have been extended towards standardizing XML-based formats for mass spectrometry data representation, despite the recognized inefficiencies associated with storing large numeric datasets in XML. The proteomics community has periodically entertained alternate strategies for data exchange, e.g., using a common application programming interface or a database-derived format. However, these efforts have yet to gain significant attention, mostly because they have not demonstrated significant performance benefits over existing standards, but also due to issues such as extensibility to multidimensional separation systems, robustness of operation, and incomplete or mismatched vocabulary. Here, we describe a format based on standard database principles that offers multiple benefits over existing formats in terms of storage size, ease of processing, data retrieval times, and extensibility to accommodate multidimensional separation systems.

Statins are a very effective and well-tolerated class of lipid-lowering agents that have been shown to reduce the risk of first and recurrent cardiovascular events. Their major adverse side effect is that they can produce a variety of skeletal muscle complaints ranging from mild myalgia to life-threatening rhabdomyolysis. Exercise can also injure skeletal muscle, and many cases of presumed statin myopathy are associated with physical activity. This literature review summarizes current concepts of statin myopathy and discuses how physical exercise may both exacerbate symptoms and possibly assist in defining the mechanisms of statin-associated muscle injury. The results suggest that exercise may cause many of the creatine kinase (CK) elevations during statin therapy and also exacerbate these CK increases. Physicians should also consider, based on circumstantial evidence, withholding statins prior to prolonged vigorous exercise to avoid clinically important rhabdomyolysis.

Pertussis still continues to cause significant morbidity and mortality worldwide. Because of the high reactogenicity of whole cell pertussis vaccine, it had evoked public controversy in several countries. In 1970 Japan abandoned use of whole cell pertussis vaccine and mounted efforts to develop better vaccine. To date, nearly 24 acellular pertussis vaccines have been developed, using different number and quantity of components. No acellular vaccine is most or least immunogenic with respect to all included antigens. Vaccine efficacy and duration of immunity is comparable with whole cell pertussis vaccine. The adverse events are two thirds less compared to whole cell vaccine.

Summary: The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. It also allows the analysis within the same framework of experimental high-throughput expression data using the suite of (trained) inference methods; hence SEBINI should be useful to software developers wishing to evaluate, compare, refine or combine inference techniques, and to bioinformaticians analyzing experimental data. SEBINI provides a platform that aids in more accurate reconstruction of biological networks, with less effort, in less time. Availability: A demonstration website is located at . The Java source code and PostgreSQL database schema are available freely for non-commercial use. Contact: ronald.taylor@pnl.gov

Understanding protein interactions is essential to gain insights into the biological processes at the whole cell level. The high-throughput experimental techniques for determining protein-protein interactions (PPI) are error prone and expensive with low overlap amongst them. Although several computational methods have been proposed for predicting protein interactions there is definite room for improvement. Here we present DomainSVM, a predictive method for PPI that uses computationally inferred domain-domain interaction values in a Support Vector Machine framework to predict protein interactions. DomainSVM method utilizes evidence of multiple interacting domains to predict a protein interaction. It outperforms existing methods of PPI prediction by achieving very high explanation ratios, precision, specificity, sensitivity and F-measure values in a 10 fold cross-validation study conducted on the positive and negative PPIs in yeast. A functional comparison study using GO annotations on the positive and the negative test sets is presented in addition to discussing novel PPI predictions in the pathogen Salmonella Typhimurium.

Motivation: Ion mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochemical properties of peptides to accurately predict a peptide's drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting. Results: When tested on an experimentally created high confidence database of 8675 peptide sequences with measured drift times, both techniques statistically significantly outperform the intrinsic size parameters-based calculations, the currently held practice in the field, on all charge states (+2, +3 and +4). Availability: The software executable, imPredict, is available for download from http:/omics.pnl.gov/software/imPredict.php Contact: rds@pnl.gov Supplementary information: Supplementary data are available at Bioinformatics online.

The prognostic value of a blunted heart rate response (BHR) during ECG-gated vasodilator stress SPECT MPI in relation to ventricular function on long-term cardiovascular events is not well established. We performed this study to evaluate the incremental prognostic value of BHR during pharmacological stress SPECT MPI. Consecutive patients who underwent dipyridamole stress Tc-99m sestamibi ECG-gated SPECT MPI (without exercise) were identified. The ratio of peak stress heart rate to baseline was noted. If the ratio was

The total quantity of digital information in the world is growing at an alarming rate. Scientists and engineers are contributing heavily to this data “tsunami” by gathering data using computing and instrumentation at incredible rates. As data volumes and complexity grow, it is increasingly arduous to extract valuable information from the data and derive knowledge from that data. Addressing these demands of ever-growing data volumes and complexity requires game-changing advances in software, hardware, and algorithms. Solution technologies also must scale to handle the increased data collection and processing rates and simultaneously accelerate timely and effective analysis results. This need for ever faster data processing and manipulation as well as algorithms that scale to high-volume data sets have given birth to a new paradigm or discipline known as “data-intensive computing.” In this chapter, we define data-intensive computing, identify the challenges of massive data, outline solutions for hardware, software, and analytics, and discuss a number of applications in the areas of biology, cyber security, and atmospheric research.

Independent, greedy collection of data events using simple heuristics results in massive over-sampling of the prominent data features in large-scale studies over what should be achievable through “intelligent,” online acquisition of such data. As a result, data generated are more aptly described as a collection of a large number of small experiments rather than a true large-scale experiment. Nevertheless, achieving “intelligent,” online control requires tight interplay between state-of-the-art, data-intensive computing infrastructure developments and analytical algorithms. In this paper, we propose a Software Architecture for Mass spectrometry-based Proteomics coupled with Liquid chromatography Experiments (SAMPLE) to develop an “intelligent” online control and analysis system to significantly enhance the information content from each sensor (in this case, a mass spectrometer). Using online analysis of data events as they are collected and decision theory to optimize the collection of events during an experiment, we aim to maximize the information content generated during an experiment by the use of pre-existing knowledge to optimize the dynamic collection of events.

The massive data sets produced by the high- throughput, multidimensional mass spectrometry instruments used in proteomics create challenges in data acquisition, storage and analysis. Data compression can help mitigate some of these problems but at the cost of less efficient data access, which directly impacts the computational time of data analysis. We have developed a compression methodology that 1) is optimized for a targeted mass spectrometry proteomics data set and 2) provides the benefits of size and speed from compression while increasing analysis efficiency by allowing extraction of segments of uncompressed data from a file without having to uncompress the entire file. This paper describes our compression algorithm, presents comparative metrics of compression size and speed, and explores approaches for applying the algorithm to a generalized data set.

Systems biology research demands the availability of tools and technologies that span a comprehensive range of computational capabilities, including data management, transfer, processing, integration, and interpretation. To address these needs, we have created the bioinformatics resource manager (BRM), a scalable, flexible, and easy to use tool for biologists to undertake complex analyses. This paper describes the underlying software architecture of the BRM that integrates multiple commodity platforms to provide a highly extensible and scalable software infrastructure for bioinformatics. The architecture integrates a J2EE 3-tier application with an archival experimental data management system, the GAGGLE framework for desktop tool integration, and the MeDICi integration framework for high-throughput data analysis workflows. This architecture facilitates a systems biology software solution that enables the entire spectrum of scientific activities, from experimental data access to high throughput processing and analysis of data for biologists and experimental scientists.

BACKGROUND: Although previous data suggest persistent clinical benefit of sirolimus-eluting stents (SES) in reducing target vessel revascularization without an increase in myocardial infarction (MI), cumulative stent thrombosis and mortality, the long-term safety and efficacy of CYPHER stent use in routine clinical practice, including off-label stent implantation, remains uncertain. METHODS: We examined clinical characteristics, angiographic findings, in hospital and long-term clinical outcomes in 3572 patients of whom 2,550 were treated with SES, and 1,022 who were treated with bare metal or heparin-coated stents (BMS). The study groups included 1,058 SES patients (41.5%) and 488 BMS patients (47.7%) with off-label indications, including acute MI, multivessel percutaneous coronary intervention, saphenous vein grafts and complex lesion morphology. A propensity-score method was utilized to adjust for differences in baseline characteristics. Patients were followed for up to 5 years for the occurrence of all-cause mortality, MI and repeat target vessel revascularization. RESULTS: Compared to the BMS patients, the SES group patients demonstrated significantly improved event-free survival with respect to all-cause mortality (RR, 1.39; 95% CI, 1.07 to 1.80, p=0.014) and repeat target vessel revascularization (RR, 2.72; 95% CI, 1.99 to 3.73, p CONCLUSIONS : Use of SES in routine clinical practice, including off-label indications, is associated with improved long-term mortality, reduced need for repeat target vessel revascularization and no increase in MI compared to BMS.

Although previous studies have documented persistent clinical benefit of sirolimus-eluting stents (SES)in reducing the need for target vessel revascularization without an increase in myocardial infarction (MI) or mortality, the long-term safety and efficacy of CYPHER stent use in routine clinical practice, including off-label stent implantation, remains uncertain.We compared long-term clinical outcomes in 2,550 patients treated with one or more SES with 1,022 patients treated with one or more bare metal or heparin-coated stents (BMS). The study groups included 1,058 SES patients (41.5%) and 488 BMS patients (47.7%) with off-label indications. A propensity-score method was utilized to adjust for differences in baseline characteristics. Patients were followed for up to five years for the occurrence of all-cause mortality, MI and repeat target vessel revascularization.Compared to BMS patients, SES patients demonstrated significantly improved event-free survival with respect to all-cause mortality (RR, 1.39; 95% CI, 1.07 to 1.80, P = 0.014) and repeat target vessel revascularization (RR, 2.72; 95% CI, 1.99 to 3.73, P0.001), with no significant difference in the incidence of cumulative MI. A landmark analysis, examining composite adverse events occurring six months after stent implantation in the two study groups, demonstrated no increased late hazard associated with SES use (relative risk, 1.08; 95% CI, 0.80 to 1.46).Use of SES in routine clinical practice, including off-label indications, is associated with improved long-term mortality, reduced need for repeat target vessel revascularization and no increase in MI compared to BMS.

Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). Results: We present a support vector machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ∼0.83 with an SD of Availability: http://omics.pnl.gov/software/STEPP.php Contact: bj@pnl.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Due to the exponential growth of sequenced genomes, the need to quickly provide accurate annotation for existing and new sequences is paramount to facilitate biological research. Current sequence comparison approaches fail to detect homologous relationships when sequence similarity is low. Support vector machine (SVM) algorithms approach this problem by transforming all proteins into a feature space of equal dimension based on protein properties, such as sequence similarity scores against a basis set of proteins or motifs. This multivariate representation of the protein space is then used to build a classifier specific to a pre-defined protein family. However, this approach is not well suited to large-scale annotation. We have developed a SVM approach that formulates remote homology as a single classifier that answers the pairwise comparison problem by integrating the two feature vectors for a pair of sequences into a single vector representation that can be used to build a classifier that separates sequence pairs into homologs and non-homologs. This pairwise SVM approach significantly improves the task of remote homology detection on the benchmark dataset, quantified as the area under the receiver operating characteristic curve; 0.97 versus 0.73 and 0.70 for PSI-BLAST and Basic Local Alignment Search Tool (BLAST), respectively.

Combining vasodilator and exercise stress reduces noncardiac side effects, improves image quality, and enhances the detection of ischemia, compared with suboptimal exercise or vasodilator stress alone. However, prognostic data with combined protocols are limited.Consecutive patients (n = 2064) who underwent symptom-limited exercise and dipyridamole stress with gated single-photon emission computed tomography (SPECT) imaging, without early revascularization, were studied. Subsequent cardiac death or nonfatal myocardial infarction was related to exercise and gated SPECT variables. Cox proportional hazards regression modeling was performed to identify predictors of adverse outcome. Annualized event rates in patients with normal and abnormal images were 0.96% and 2.71%, respectively (P.001). With abnormal imaging, annualized event rates were 0.86% and 3.13% in patients with average to high and fair or poor functional capacity, respectively (P = .019). Abnormal imaging, a severely reduced post-stress ejection fraction, transient ischemic dilation, and fair or poor functional capacity emerged as predictors of adverse outcome. Accordingly, patients were stratified into low-risk, intermediate-risk, and high-risk cohorts with annualized event rates of 0.94%, 2.24%, and 8.19%, respectively (P.001 in any two-way comparison).A protocol that combines symptom-limited exercise and dipyridamole stress with gated SPECT imaging provides highly effective risk stratification for adverse outcomes.

Summary: The Bioinformatics Resource Manager (BRM) is a software environment that provides the user with data management, retrieval and integration capabilities. Designed in collaboration with biologists, BRM simplifies mundane analysis tasks of merging microarray and proteomic data across platforms, facilitates integration of users’ data with functional annotation and interaction data from public sources and provides connectivity to visual analytic tools through reformatting of the data for easy import or dynamic launching capability. BRM is developed using Java™ and other open-source technologies for free distribution.Availability: BRM, sample data sets and a user manual can be downloaded from http://www.sysbio.org/dataresources/brm.stmContact: anuj.shah@pnl.gov, brm@pnl.gov

Cutting-edge biological and bioinformatics research seeks a systems perspective through the analysis of multiple types of high-throughput and other experimental data for the same sample. Systems-level analysis requires the integration and fusion of such data, typically through advanced statistics and mathematics. Visualization is a complementary computational approach that supports integration and analysis of complex data or its derivatives. We present a bioinformatics visualization prototype, Juxter, which depicts categorical information derived from or assigned to these diverse data for the purpose of comparing patterns across categorizations. The visualization allows users to easily discern correlated and anomalous patterns in the data. These patterns, which might not be detected automatically by algorithms, may reveal valuable information leading to insight and discovery. We describe the visualization and interaction capabilities and demonstrate its utility in a new field, metagenomics, which combines molecular biology and genetics to identify and characterize genetic material from multi-species microbial samples.

For the SC|06 analytics challenge, we demonstrate an end-to-end solution for processing data produced by high-throughput mass spectrometry (MS)-based proteomics so biological hypotheses can be explored. This approach is based on a tool called the Bioinformatics Resource Manager (BRM) which will interact with high-performance architecture and experimental data sources to provide high-throughput analytics to a specific experimental dataset. Peptide identification was achieved by a high-performance code, Polygraph, which has been shown to scale well beyond 1000 processors. Visual analytics applications such as PQuad, Cytoscape, or others may be used to visualize protein identities in the context of pathways using data from public repositories such as Kyoto Encyclopedia of Genes and Genomes (KEGG). The end result was that a user can go from experimental spectra to pathway data in a single workflow reducing time-to-solution for analyzing biological data from weeks to minutes.

Background MultiAlign is a free software tool that aligns multiple liquid chromatography-mass spectrometry datasets to one another by clustering mass and chromatographic elution features across datasets. Applicable to both label-free proteomics and metabolomics comparative analyses, the software can be operated in several modes. For example, clustered features can be matched to a reference database to identify analytes, used to generate abundance profiles, linked to tandem mass spectra based on parent precursor masses, and culled for targeted liquid chromatography-tandem mass spectrometric analysis. MultiAlign is also capable of tandem mass spectral clustering to describe proteome structure and find similarity in subsequent sample runs. Results MultiAlign was applied to two large proteomics datasets obtained from liquid chromatography-mass spectrometry analyses of environmental samples. Peptides in the datasets for a microbial community that had a known metagenome were identified by matching mass and elution time features to those in an established reference peptide database. Results compared favorably with those obtained using existing tools such as VIPER, but with the added benefit of being able to trace clusters of peptides across conditions to existing tandem mass spectra. MultiAlign was further applied to detect clusters across experimental samples derived from a reactor biomass community for which no metagenome was available. Several clusters were culled for further analysis to explore changes in the community structure. Lastly, MultiAlign was applied to liquid chromatography-mass spectrometry-based datasets obtained from a previously published study of wild type and mitochondrial fatty acid oxidation enzyme knockdown mutants of human hepatocarcinoma to demonstrate its utility for analyzing metabolomics datasets. Conclusion MultiAlign is an efficient software package for finding similar analytes across multiple liquid chromatography-mass spectrometry feature maps, as demonstrated here for both proteomics and metabolomics experiments. The software is particularly useful for proteomic studies where little or no genomic context is known, such as with environmental proteomics.