Acute Coronary Syndrome and COVID-19: A Case Report of Refractory Hypercoagulability.

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.

Cocaine-Related Myocardial Infarction and Ventricular Rupture.

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.

MultiAlign: a multiple LC-MS analysis tool for targeted omics analysis.

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.

Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra.

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.

An efficient data format for mass spectrometry-based proteomics.

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.

A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics.

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 approximately 0.83 with an SD of <0.038. Furthermore, we demonstrate that these results are achievable with a small set of 13 variables and can achieve high proteome coverage. AVAILABILITY: http://omics.pnl.gov/software/STEPP.php CONTACT: bj@pnl.gov SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning based prediction for peptide drift times in ion mobility spectrometry.

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.

Blunted heart rate response as a predictor of cardiac death in patients undergoing vasodilator stress technetium-99m sestamibi gated SPECT myocardial perfusion imaging.

INTRODUCTION: 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. METHODS: 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 <1.20, it was considered blunted (BHR). The images were interpreted using the standard ASNC 17 segment model. Patients were followed up for a mean time period of 2.3 +/- 1.5 years. RESULTS: Sixty-four percent (2,890/4,484) of patients demonstrated BHR during dipyridamole stress testing. Cardiac death, the primary end point, occurred in 6.8% of patients. Patients with BHR had a significantly lower cardiac death-free survival as compared to NO BHR group in total population (83% vs 94%; P < .001) as well as in subgroup with normal ejection fraction (89% vs 96%; P < .001). BHR was an independent predictor of cardiac death after adjusting for multiple clinical, perfusion, and function-related gated SPECT variables. CONCLUSION: Blunted heart rate response during vasodilator stress SPECT MPI is an important prognostic marker for cardiac death.

Fluoropyrimidine-associated cardiotoxicity: revisited.

BACKGROUND: The syndrome of 5-fluorouracil (5-FU)-associated cardiotoxicity remains poorly defined. PATIENTS AND METHODS: We performed a literature review (1969 - 2007) and compiled data derived from 377 evaluable cases out of 448 reported cases. RESULTS: Patient age ranged from 14 to 86 years. Of the patients 65% were 55 years old and the male:female ratio was 1.5:1. The most commonly treated tumors were gastrointestinal (60%), head and neck (22%) and breast (4%). Of the patients 14% had a history of heart disease whereas cardiac risk factors were found in 37%. Mode of administration included: continuous infusion (72%); bolus (22.5%); intermediate infusion (3%); oral (2%); and intraperitoneal (1 patient). The dosages of 5-FU used were < 750 mg/m(2)/day (36%), 751 - 999 (16%), 1,000 (26%), 1,001 - 1,499 (4%) and 1,500 (16%). Of the patients 54% received 5-FU in combination with other chemotherapeutic agents (cisplatin 44%) whereas 51% received 5-FU alone or with leucovorin. Only 4% patients had undergone previous or concomitant radiation therapy to the mediastinum. Of cardiac incidents that happened 69% were seen during or within 72 h of the first cycle of 5-FU. Angina occurred in 45% of patients whereas myocardial infarction was seen in 22%, arrhythmias in 23, acute pulmonary edema in 5, cardiac arrest and pericarditis in 1.4 and heart failure in 2. Electro-cardiographic evidence of ischemia or ST-T changes were recorded in 69% of patients, but abnormal cardiac enzymes were found in only 12%. The cardiac symptoms were reproducible in 47%, including in one patient subsequently treated with 5-FU p.o. Symptoms were also elicited when the same patients were treated with lower doses or different schedules. Of the patients 68% responded to conservative anti-anginal therapy, although prophylactic coronary vasodilators had limited efficacy. Overall, 8% of patients showing cardiotoxicity on 5-FU administration died. Furthermore, 13% reexposed to 5-FU died. CONCLUSIONS: Our review suggests that 5-FU cardiotoxicity is an infrequent but real phenomenon that is independent of dose and may be related to a continuous infusion schedule. The presence of cardiac risk factors is not predictive. Patients should be observed closely and 5-FU administration discontinued if cardiac symptoms develop. A rechallenge with 5-FU should be reserved only for those patients in whom there is no reasonable alternative therapy and should be performed in the setting of aggressive prophylaxis and close monitoring.

Long-term efficacy and safety of sirolimus-eluting vs bare-metal stents.

BACKGROUND: 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. METHODS: 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. RESULTS: 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, P < 0.001), 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). 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.

A feature vector integration approach for a generalized support vector machine pairwise homology algorithm.

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.

A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics.

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 0.8 with a SD of <0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. AVAILABILITY: http://omics.pnl.gov/software/STEPP.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SVM-HUSTLE--an iterative semi-supervised machine learning approach for pairwise protein remote homology detection.

MOTIVATION: As the amount of biological sequence data continues to grow exponentially we face the increasing challenge of assigning function to this enormous molecular 'parts list'. The most popular approaches to this challenge make use of the simplifying assumption that similar functional molecules, or proteins, sometimes have similar composition, or sequence. However, these algorithms often fail to identify remote homologs (proteins with similar function but dissimilar sequence) which often are a significant fraction of the total homolog collection for a given sequence. We introduce a Support Vector Machine (SVM)-based tool to detect homology using semi-supervised iterative learning (SVM-HUSTLE) that identifies significantly more remote homologs than current state-of-the-art sequence or cluster-based methods. As opposed to building profiles or position specific scoring matrices, SVM-HUSTLE builds an SVM classifier for a query sequence by training on a collection of representative high-confidence training sets, recruits additional sequences and assigns a statistical measure of homology between a pair of sequences. SVM-HUSTLE combines principles of semi-supervised learning theory with statistical sampling to create many concurrent classifiers to iteratively detect and refine, on-the-fly, patterns indicating homology. RESULTS: When compared against existing methods for identifying protein homologs (BLAST, PSI-BLAST, COMPASS, PROF_SIM, RANKPROP and their variants) on two different benchmark datasets SVM-HUSTLE significantly outperforms each of the above methods using the most stringent ROC(1) statistic with P-values less than 1e-20. SVM-HUSTLE also yields results comparable to HHSearch but at a substantially reduced computational cost since we do not require the construction of HMMs. AVAILABILITY: The software executable to run SVM-HUSTLE can be downloaded from http://www.sysbio.org/sysbio/networkbio/svm_hustle

Symptom-limited exercise combined with dipyridamole stress: prognostic value in assessment of known or suspected coronary artery disease by use of gated SPECT imaging.

BACKGROUND: 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. METHODS AND RESULTS: 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). CONCLUSIONS: A protocol that combines symptom-limited exercise and dipyridamole stress with gated SPECT imaging provides highly effective risk stratification for adverse outcomes.

Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms.

A significant challenge in homology detection is to identify sequences that share a common evolutionary ancestor, despite significant primary sequence divergence. Remote homologs will often have less than 30% sequence identity, yet still retain common structural and functional properties. We demonstrate a novel method for identifying remote homologs using a support vector machine (SVM) classifier trained by fusing sequence similarity scores and subcellular location prediction. SVMs have been shown to perform well in a variety of applications where binary classification of data is the goal. At the same time, data fusion methods have been shown to be highly effective in enhancing discriminative power of data. Combining these two approaches in the application SVM-SimLoc resulted in identification of significantly more remote homologs (p-value<0.006) than using either sequence similarity or subcellular location independently.

Enabling high-throughput data management for systems biology: the Bioinformatics Resource Manager.

UNLABELLED: 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.stm.

Pertussis vaccine controversies and acellular pertussis vaccine.

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.