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Objectives To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations. Target Audience Investigators seeking to explore quantum computing and create quantum-based applications for healthcare and biomedical research. Scope Quantum computing requires specialized hardware, known as quantum processing units, that use quantum bits (qubits) instead of classical bits to perform computations. This article will cover (1) proposed applications where quantum computing offers advantages to classical computing in biomedicine; (2) an introduction to how quantum computers operate, tailored for biomedical researchers; (3) recent progress that has expanded access to quantum computing; and (4) challenges, opportunities, and proposed solutions to integrate quantum computing in biomedical applications. [ABSTRACT FROM AUTHOR]

Background The benefit of primary and booster vaccination in people who experienced a prior Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains unclear. The objective of this study was to estimate the effectiveness of primary (two-dose series) and booster (third dose) mRNA vaccination against Omicron (lineage BA.1) infection among people with a prior documented infection. Methods and findings We conducted a test-negative case-control study of reverse transcription PCRs (RT-PCRs) analyzed with the TaqPath (Thermo Fisher Scientific) assay and recorded in the Yale New Haven Health system from November 1, 2021, to April 30, 2022. Overall, 11,307 cases (positive TaqPath analyzed RT-PCRs with S-gene target failure [SGTF]) and 130,041 controls (negative TaqPath analyzed RT-PCRs) were included (median age: cases: 35 years, controls: 39 years). Among cases and controls, 5.9% and 8.1% had a documented prior infection (positive SARS-CoV-2 test record [greater than or equal to]90 days prior to the included test), respectively. We estimated the effectiveness of primary and booster vaccination relative to SGTF-defined Omicron (lineage BA.1) variant infection using a logistic regression adjusted for date of test, age, sex, race/ethnicity, insurance, comorbidities, social venerability index, municipality, and healthcare utilization. The effectiveness of primary vaccination 14 to 149 days after the second dose was 41.0% (95% confidence interval (CI): 14.1% to 59.4%, p 0.006) and 27.1% (95% CI: 18.7% to 34.6%, p < 0.001) for people with and without a documented prior infection, respectively. The effectiveness of booster vaccination ([greater than or equal to]14 days after booster dose) was 47.1% (95% CI: 22.4% to 63.9%, p 0.001) and 54.1% (95% CI: 49.2% to 58.4%, p < 0.001) in people with and without a documented prior infection, respectively. To test whether booster vaccination reduced the risk of infection beyond that of the primary series, we compared the odds of infection among boosted ([greater than or equal to]14 days after booster dose) and booster-eligible people ([greater than or equal to]150 days after second dose). The odds ratio (OR) comparing boosted and booster-eligible people with a documented prior infection was 0.79 (95% CI: 0.54 to 1.16, p 0.222), whereas the OR comparing boosted and booster-eligible people without a documented prior infection was 0.54 (95% CI: 0.49 to 0.59, p < 0.001). This study's limitations include the risk of residual confounding, the use of data from a single system, and the reliance on TaqPath analyzed RT-PCR results. Conclusions In this study, we observed that primary vaccination provided significant but limited protection against Omicron (lineage BA.1) infection among people with and without a documented prior infection. While booster vaccination was associated with additional protection against Omicron BA.1 infection in people without a documented prior infection, it was not found to be associated with additional protection among people with a documented prior infection. These findings support primary vaccination in people regardless of documented prior infection status but suggest that infection history may impact the relative benefit of booster doses., Author(s): Margaret L. Lind 1,*, Alexander J. Robertson 1, Julio Silva 2, Frederick Warner 3,4, Andreas C. Coppi 3,4, Nathan Price 3,4, Chelsea Duckwall 1, Peri Sosensky 1, Erendira C. [...]

The ongoing coronavirus disease outbreak demonstrates the need for novel applications of real-time data to produce timely information about incident cases. Using health information technology (HIT) and real-world data, we sought to produce an interface that could, in near real time, identify patients presenting with suspected respiratory tract infection and enable monitoring of test results related to specific pathogens, including severe acute respiratory syndrome coronavirus 2. This tool was built upon our computational health platform, which provides access to near real-time data from disparate HIT sources across our health system. This combination of technology allowed us to rapidly prototype, iterate, and deploy a platform to support a cohesive organizational response to a rapidly evolving outbreak. Platforms that allow for agile analytics are needed to keep pace with evolving needs within the health care system.

How to develop highly informative serology assays to evaluate the quality of immune protection against coronavirus disease‐19 (COVID‐19) has been a global pursuit over the past years. Here, a microfluidic high‐plex immuno‐serolomic assay is developed to simultaneously measure50 plasma or serum samples for50 soluble markers including 35proteins, 11 anti‐spike/receptor binding domian (RBD) IgG antibodies spanningmajor variants, and controls. This assay demonstrates the quintuplicate test in a single run with high throughput, low sample volume, high reproducibilityand accuracy. It is applied to the measurement of 1012 blood samples including in‐depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein analysis reveals distinct immune mediator modules that exhibit a reduced degree of diversity in protein‐protein cooperation in patients with hematologic malignancies or receiving B cell depletion therapy. Serological analysis identifies that COVID‐infected patients with hematologic malignancies display impaired anti‐RBD antibody response despite high level of anti‐spike IgG, which can be associated with limited clonotype diversity and functional deficiency in B cells. These findings underscore the importance to individualize immunization strategies for these high‐risk patients and provide an informative tool to monitor their responses at the systems level. [ABSTRACT FROM AUTHOR]

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs. [ABSTRACT FROM AUTHOR]

Background: The impact variant-specific immune evasion and waning protection have on declining COVID-19 vaccine effectiveness remains unclear. Using whole-genome-sequencing (WGS), we examined the contribution of these factors on the decline observed following the introduction of the Delta variant. Further, we evaluated the utility of calendar-period-based classification as an alternative to WGS.Methods: We conducted a test-negative-case-control study among people who received SARS-CoV-2 RT-PCR testing in the Yale New Haven Health System between April 1 and August 24, 2021. Variant classification was performed using WGS and calendar-period.Results: Overall, 2,029 cases (RT-PCR positive, sequenced samples [infections]) and 343,985 controls (negative RT-PCRs) were included. VE 14-89 days after 2nd dose was significantly higher against WGS-classified Alpha-infection (84.4%, CI: 75.6-90.0%) than Delta-infection (68.9%, CI: 58.0-77.1%, p-value = 0.013). The odds of WGS-classified Delta-infection were significantly higher 90-149 than 14-89 days after 2nd dose (p-value = 0.003). VE estimates against calendar-period-classified infections approximated estimates against WGS-classified infections, however, calendar-period-based classification was subject to outcome misclassification (35%: Alpha-period, 4%: Delta-period).Conclusions: Both waning protection and variant-specific immune evasion contributed to the lower effectiveness. While VE estimates against calendar-period-classified infections mirrored those against WGS-classified infections, our analysis highlights the need for WGS when variants are co-circulating and misclassification is likely. [ABSTRACT FROM AUTHOR]

Background The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI. Methods and findings We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors. Conclusions Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., Author(s): Chenxi Huang 1, Karthik Murugiah 2, Shiwani Mahajan 1, Shu-Xia Li 1, Sanket S. Dhruva 3,4, Julian S. Haimovich 5, Yongfei Wang 1, Wade L. Schulz 1,6, Jeffrey M. [...]

Real‐world data (RWD) and real‐world evidence (RWE) are becoming essential tools for informing regulatory decision making in health care and offer an opportunity for all stakeholders in the healthcare ecosystem to evaluate medical products throughout their lifecycle. Although considerable interest has been given to regulatory decisions supported by RWE for treatment authorization, especially in rare diseases, less attention has been given to RWD/RWE related to in vitro diagnostic (IVD) products and clinical decision support systems (CDSS). This review examines current regulatory practices in relation to IVD product development and discusses the use of CDSS in assisting clinicians to retrieve, filter, and analyze patient data in support of complex decisions regarding diagnosis and treatment. The review then explores how utilizing RWD could augment regulatory body understanding of test performance, clinical outcomes, and benefit‐risk profiles, and how RWD could be leveraged to augment CDSS and improve safety, quality, and efficiency of healthcare practices. Whereas we present examples of RWD assisting in the regulation of IVDs and CDSS, we also highlight key challenges within the current healthcare system which are impeding the potential of RWE to be fully realized. These challenges include issues such as data availability, reliability, accessibility, harmonization, and interoperability, often for reasons specific to diagnostics. Finally, we review ways that these challenges are actively being addressed and discuss how private‐public collaborations and the implementation of standardized language and protocols are working toward producing more robust RWD and RWE to support regulatory decision making. [ABSTRACT FROM AUTHOR]

Objective: The study sought to conduct an informatics analysis on the National Evaluation System for Health Technology Coordinating Center test case of cardiac ablation catheters and to demonstrate the role of informatics approaches in the feasibility assessment of capturing real-world data using unique device identifiers (UDIs) that are fit for purpose for label extensions for 2 cardiac ablation catheters from the electronic health records and other health information technology systems in a multicenter evaluation.Materials and Methods: We focused on data capture and transformation and data quality maturity model specified in the National Evaluation System for Health Technology Coordinating Center data quality framework. The informatics analysis included 4 elements: the use of UDIs for identifying device exposure data, the use of standardized codes for defining computable phenotypes, the use of natural language processing for capturing unstructured data elements from clinical data systems, and the use of common data models for standardizing data collection and analyses.Results: We found that, with the UDI implementation at 3 health systems, the target device exposure data could be effectively identified, particularly for brand-specific devices. Computable phenotypes for study outcomes could be defined using codes; however, ablation registries, natural language processing tools, and chart reviews were required for validating data quality of the phenotypes. The common data model implementation status varied across sites. The maturity level of the key informatics technologies was highly aligned with the data quality maturity model.Conclusions: We demonstrated that the informatics approaches can be feasibly used to capture safety and effectiveness outcomes in real-world data for use in medical device studies supporting label extensions. [ABSTRACT FROM AUTHOR]

The article focuses on myopericarditis in young adults presenting to the emergency department after receiving a second COVID-19 mRNA vaccine. Topics include the media reports from Israel, France, and the U.S. military have raised the possibility of a link between COVID-19 mRNA vaccines in young adults, the adults seen in emergency departments of a U.S. health care system who developed myopericarditis, and the local institutional health care system database for all diagnoses of myopericarditis.

Background: SARS-CoV-2 serologic assays are becoming increasingly available and may serve as a diagnostic aid in a multitude of settings relating to past infection status. However, there is limited literature detailing the longitudinal performance of EUA-cleared serologic assays in US populations, particularly in cohorts with a remote history of PCR-confirmed SARS-CoV-2 infection (e.g., >2 months). Methods: We evaluated the diagnostic sensitivities and specificities of the Elecsys® Anti-SARS-CoV-2 (anti-N) and Elecsys Anti-SARS-CoV-2 S (anti-S1-RBD) assays, using 174 residual clinical samples up to 267 days post-PCR diagnosis of SARS-CoV-2 infection (n = 154) and a subset of samples obtained prior to the COVID-19 pandemic as negative controls (n = 20). Results: The calculated diagnostic sensitivities for the anti-N and anti-S1-RBD assays were 89% and 93%, respectively. Of the 154 samples in the SARS-CoV-2-positive cohort, there were 6 discrepant results between the anti-N and anti-S1-RBD assays, 5 of which were specimens collected ≥200 days post-PCR positivity and only had detectable levels of anti-S1-RBD antibodies. When only considering specimens collected ≥100 days post-PCR positivity (n = 41), the sensitivities for the anti-N and anti-S1-RBD assays were 85% and 98%, respectively. Conclusions: The anti-S1-RBD assay demonstrated superior sensitivity at time points more remote to the PCR detection date, with 6 more specimens from the SARS-CoV-2-positive cohort detected, 5 of which were collected more than 200 days post-PCR positivity. While analytical differences and reagent lot-to-lot variability are possible, this may indicate that, in some instances, anti-S1-RBD antibodies may persist longer in vivo and may be a better target for detecting remote SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

Background: Intraoperative data may improve models predicting postoperative events. We evaluated the effect of incorporating intraoperative variables to the existing preoperative model on the predictive performance of the model for coronary artery bypass graft.Methods: We analyzed 378 572 isolated coronary artery bypass graft cases performed across 1083 centers, using the national Society of Thoracic Surgeons Adult Cardiac Surgery Database between 2014 and 2016. Outcomes were operative mortality, 5 postoperative complications, and composite representation of all events. We fitted models by logistic regression or extreme gradient boosting (XGBoost). For each modeling approach, we used preoperative only, intraoperative only, or pre+intraoperative variables. We developed 84 models with unique combinations of the 3 variable sets, 2 variable selection methods, 2 modeling approaches, and 7 outcomes. Each model was tested in 20 iterations of 70:30 stratified random splitting into development/testing samples. Model performances were evaluated on the testing dataset using the C statistic, area under the precision-recall curve, and calibration metrics, including the Brier score.Results: The mean patient age was 65.3 years, and 24.7% were women. Operative mortality, excluding intraoperative death, occurred in 1.9%. In all outcomes, models that considered pre+intraoperative variables demonstrated significantly improved Brier score and area under the precision-recall curve compared with models considering pre or intraoperative variables alone. XGBoost without external variable selection had the best C statistics, Brier score, and area under the precision-recall curve values in 4 of the 7 outcomes (mortality, renal failure, prolonged ventilation, and composite) compared with logistic regression models with or without variable selection. Based on the calibration plots, risk restratification for mortality showed that the logistic regression model underestimated the risk in 11 114 patients (9.8%) and overestimated in 12 005 patients (10.6%). In contrast, the XGBoost model underestimated the risk in 7218 patients (6.4%) and overestimated in 0 patients (0%).Conclusions: In isolated coronary artery bypass graft, adding intraoperative variables to preoperative variables resulted in improved predictions of all 7 outcomes. Risk models based on XGBoost may provide a better prediction of adverse events to guide clinical care. [ABSTRACT FROM AUTHOR]

Summary: Pathogen‐reduced (PR) platelets are routinely used in many countries. Some studies reported changes in platelet and red blood cell (RBC) transfusion requirements in patients who received PR platelets when compared to conventional (CONV) platelets. Over a 28‐month period we retrospectively analysed platelet utilisation, RBC transfusion trends, and transfusion reaction rates data from all transfused adult patients transfused at the Yale‐New Haven Hospital, New Haven, CT, USA. We determined the number of RBC and platelet components administered between 2 and 24, 48, 72 or 96 h. A total of 3767 patients received 21 907 platelet components (CONV = 8912; PR = 12 995); 1,087 patients received only CONV platelets (1578 components) and 1,466 patients received only PR platelets (2604 components). The number of subsequently transfused platelet components was slightly higher following PR platelet components (P < 0·05); however, fewer RBCs were transfused following PR platelet administration (P < 0·05). The mean time‐to‐next platelet component transfusion was slightly shorter following PR platelet transfusion (P = 0·002). The rate of non‐septic transfusion reactions did not differ (all P > 0·05). Septic transfusion reactions (N = 5) were seen only after CONV platelet transfusions (P = 0·011). These results provide evidence for comparable clinical efficacy of PR and CONV platelets. PR platelets eliminated septic transfusion reactions without increased risk of other types of transfusions with only slight increase in platelet utilisation. [ABSTRACT FROM AUTHOR]

The authors describe a clinical pharmacogenetics program throughout their health system which includes academic and community-based inpatient facilities and affiliated outpatient providers to meet the demand for precision medicine. They talk about the number of patients that have been clinically tested with a panel-based genotyping approach since January 2019, the complexity of clinical genetic testing and delivery of actionable results, and the need for additional real-world deployments.

Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research. Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting. Results: Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second. Conclusion: This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification. [ABSTRACT FROM AUTHOR]

Medical devices are required to have unique identifiers, which have the potential to provide data to improve patient safety. The authors discuss why this potential is not being realized and suggest ways to overcome the barriers. [ABSTRACT FROM AUTHOR]

While the adoption of next generation sequencing has rapidly expanded, the informatics infrastructure used to manage the data generated by this technology has not kept pace. Historically, relational databases have provided much of the framework for data storage and retrieval. Newer technologies based on NoSQL architectures may provide significant advantages in storage and query efficiency, thereby reducing the cost of data management. But their relative advantage when applied to biomedical data sets, such as genetic data, has not been characterized. To this end, we compared the storage, indexing, and query efficiency of a common relational database (MySQL), a document-oriented NoSQL database (MongoDB), and a relational database with NoSQL support (PostgreSQL). When used to store genomic annotations from the dbSNP database, we found the NoSQL architectures to outperform traditional, relational models for speed of data storage, indexing, and query retrieval in nearly every operation. These findings strongly support the use of novel database technologies to improve the efficiency of data management within the biological sciences. [ABSTRACT FROM AUTHOR]

Next generation sequencing (NGS) has become a common technology in the clinical laboratory, particularly for the analysis of malignant neoplasms. However, most mutations identified by NGS are variants of unknown clinical significance (VOUS). Although the approach to define these variants differs by institution, software algorithms that predict variant effect on protein function may be used. However these algorithms commonly generate conflicting results, potentially adding uncertainty to interpretation. In this review, we examine several computational tools used to predict whether a variant has clinical significance. In addition to describing the role of these tools in clinical diagnostics, we assess their efficacy in analyzing known pathogenic and benign variants in hematologic malignancies. [ABSTRACT FROM AUTHOR]

Entry of reovirus virions has been well studied in several tissue culture systems. After attachment to junctional adhesion molecule A (JAM-A), virions undergo clathrin-mediated endocytosis followed by proteolytic disassembly of the capsid and penetration to the cytoplasm. However, during in vivo infection of the intestinal tract, and likely in the tumor microenvironment, capsid proteolysis (uncoating) is initiated extracellularly. We used multiple approaches to determine if uncoated reovirus particles, called intermediate subviral particles (ISVPs), enter cells by directly penetrating the limiting membrane or if they take advantage of endocytic pathways to establish productive infection. We found that entry and infection by reovirus ISVPs was inhibited by dynasore, an inhibitor of dynamin-dependent endocytosis, as well as by genistein and dominant-negative caveolin-1, which block caveolar endocytosis. Inhibition of caveolar endocytosis also reduced infection by reovirus virions. Extraction of membrane cholesterol with methyl-β-cyclodextrin inhibited infection by virions but had no effect when infection was initiated with ISVPs. We found this pathway to be independent of both clathrin and caveolin. Together, these data suggest that reovirus virions can use both dynamin-dependent and dynamin-independent endocytic pathways during cell entry, and they reveal that reovirus ISVPs can take advantage of caveolar endocytosis to establish productive infection. [ABSTRACT FROM AUTHOR]

Epigenetic mechanisms regulate the expression of virulence traits in diverse pathogens, including protozoan and fungi. In the human fungal pathogen Candida albicans, virulence traits such as antifungal resistance, white-opaque switching, and adhesion to lung cells are regulated by histone deacetylases (HDACs). However, the role of HDACs in the regulation of the yeast-hyphal morphogenetic transitions, a critical virulence attribute of C. albicans, remains poorly explored. In this study, we wished to determine the relevance of other HDACs on C. albicans morphogenesis. We generated mutants in the HDACs HOS1, HOS2, RPD31, and HDA1 and determined their ability to filament in response to different environmental stimuli. We found that while HOS1 and RPD31 have no or a more limited role in morphogenesis, the HDACs HOS2 and HDA1 have opposite roles in the regulation of hyphal formation. Our results demonstrate an important role for HDACs on the regulation of yeast-hyphal transitions in the human pathogen C. albicans. [ABSTRACT FROM AUTHOR]

Background: Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth.Objective: The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform.Methods: We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics.Results: Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases.Conclusions: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research. [ABSTRACT FROM AUTHOR]

Abstract: Mice infected with coxsackievirus B1 Tucson (CVB1T) develop chronic, post-viral myopathy (PVM) with clinical manifestations of hind limb muscle weakness and myositis. The objective of the current study was to establish the genetic basis of myopathogenicity in CVB1T. Using a reverse genetics approach, full attenuation of PVM could only be achieved by simultaneously mutating four sites located at C706U in the 5′ untranslated region (5′ UTR) and at Y87F, V136A, and T276A in the VP1 capsid. Engineering these four myopathic determinants into an amyopathic CVB1T variant restored the ability to cause PVM. Moreover, these same four determinants controlled PVM expression in a second strain of mice, indicating that the underlying mechanism is operational in mice of different genetic backgrounds. Modeling studies predict that C706U alters both local and long range pairing in the 5′ UTR, and that VP1 determinants are located on the capsid surface. However, these differences did not affect viral titers, temperature stability, pH stability, or the antibody response to virus. These studies demonstrate that PVM develops from a complex interplay between viral determinants in the 5′ UTR and VP1 capsid and have uncovered intriguing similarities between genetic determinants that cause PVM and those involved in pathogenesis of other enteroviruses. [Copyright &y& Elsevier]