A binding site for phosphoinositides described by multiscale simulations explains their modulation of voltage-gated sodium channels.

Voltage-gated sodium channels (Naáµ¥) are membrane proteins which open to facilitate the inward flux of sodium ions into excitable cells. In response to stimuli, Naáµ¥ channels transition from the resting, closed state to an open, conductive state, before rapidly inactivating. Dysregulation of this functional cycle due to mutations causes diseases including epilepsy, pain conditions, and cardiac disorders, making Naáµ¥ channels a significant pharmacological target. Phosphoinositides are important lipid cofactors for ion channel function. The phosphoinositide PI(4,5)P2 decreases Naáµ¥1.4 activity by increasing the difficulty of channel opening, accelerating fast inactivation and slowing recovery from fast inactivation. Using multiscale molecular dynamics simulations, we show that PI(4,5)P2 binds stably to inactivated Naáµ¥ at a conserved site within the DIV S4-S5 linker, which couples the voltage-sensing domain (VSD) to the pore. As the Naáµ¥ C-terminal domain is proposed to also bind here during recovery from inactivation, we hypothesize that PI(4,5)P2 prolongs inactivation by competitively binding to this site. In atomistic simulations, PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, responsible for fast inactivation, slowing the conformational changes required for the channel to recover to the resting state. We further show that in a resting state Naáµ¥ model, phosphoinositides bind to VSD gating charges, which may anchor them and impede VSD activation. Our results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the rate of recovery from inactivation, an important step for the development of novel therapies to treat Naáµ¥-related diseases.

Enabling Longitudinal Exploratory Analysis of Clinical COVID Data.

As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data.

BACKGROUND: Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any biomedical field that is challenged by the need for integrated spatiotemporal data to examine individual-level determinants of health and disease. RESULTS: We have developed an open-source software application-FHIR PIT (Health Level 7 Fast Healthcare Interoperability Resources Patient data Integration Tool)-to enable studies on the impact of individual-level environmental exposures on health and disease. FHIR PIT was motivated by the need to integrate patient data derived from our institution's clinical warehouse with a variety of public data sources on environmental exposures and then openly expose the data via ICEES (Integrated Clinical and Environmental Exposures Service). FHIR PIT consists of transformation steps or building blocks that can be chained together to form a transformation and integration workflow. Several transformation steps are generic and thus can be reused. As such, new types of data can be incorporated into the modular FHIR PIT pipeline by simply reusing generic steps or adding new ones. We validated FHIR PIT in the context of a driving use case designed to investigate the impact of airborne pollutant exposures on asthma. Specifically, we replicated published findings demonstrating racial disparities in the impact of airborne pollutants on asthma exacerbations. CONCLUSIONS: While FHIR PIT was developed to support our driving use case on asthma, the software can be used to integrate any type and number of spatiotemporal data sources at a level of granularity that enables individual-level study. We expect FHIR PIT to facilitate research in environmental health and numerous other biomedical disciplines.

C-Reactive Protein Causes Adult-Onset Obesity Through Chronic Inflammatory Mechanism.

Obesity is characterized by low-grade chronic inflammation. As an acute-phase reactant to inflammation and infection, C-reactive protein (CRP) has been found to be the strongest factor associated with obesity. Here we show that chronic elevation of human CRP at baseline level causes the obesity. The obesity phenotype is confirmed by whole-body magnetic resonance imaging (MRI), in which the total fat mass is 6- to 9- fold higher in the CRP rats than the control rats. Univariate linear regression analysis showed different growth rates between the CRP rats and the control rats, and that the difference appears around 11 weeks old, indicating that they developed adult-onset obesity. We also found that chronic elevation of CRP can prime molecular changes broadly in the innate immune system, energy expenditure systems, thyroid hormones, apolipoproteins, and gut flora. Our data established a causal role of CRP elevation in the development of adult-onset obesity.

Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon.

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program ('Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

Fast Healthcare Interoperability Resources (FHIR) as a Meta Model to Integrate Common Data Models: Development of a Tool and Quantitative Validation Study.

BACKGROUND: In a multisite clinical research collaboration, institutions may or may not use the same common data model (CDM) to store clinical data. To overcome this challenge, we proposed to use Health Level 7's Fast Healthcare Interoperability Resources (FHIR) as a meta-CDM-a single standard to represent clinical data. OBJECTIVE: In this study, we aimed to create an open-source application termed the Clinical Asset Mapping Program for FHIR (CAMP FHIR) to efficiently transform clinical data to FHIR for supporting source-agnostic CDM-to-FHIR mapping. METHODS: Mapping with CAMP FHIR involves (1) mapping each source variable to its corresponding FHIR element and (2) mapping each item in the source data's value sets to the corresponding FHIR value set item for variables with strict value sets. To date, CAMP FHIR has been used to transform 108 variables from the Informatics for Integrating Biology & the Bedside (i2b2) and Patient-Centered Outcomes Research Network data models to fields across 7 FHIR resources. It is designed to allow input from any source data model and will support additional FHIR resources in the future. RESULTS: We have used CAMP FHIR to transform data on approximately 23,000 patients with asthma from our institution's i2b2 database. Data quality and integrity were validated against the origin point of the data, our enterprise clinical data warehouse. CONCLUSIONS: We believe that CAMP FHIR can serve as an alternative to implementing new CDMs on a project-by-project basis. Moreover, the use of FHIR as a CDM could support rare data sharing opportunities, such as collaborations between academic medical centers and community hospitals. We anticipate adoption and use of CAMP FHIR to foster sharing of clinical data across institutions for downstream applications in translational research.

A novel approach for exposing and sharing clinical data: the Translator Integrated Clinical and Environmental Exposures Service.

OBJECTIVE: This study aimed to develop a novel, regulatory-compliant approach for openly exposing integrated clinical and environmental exposures data: the Integrated Clinical and Environmental Exposures Service (ICEES). MATERIALS AND METHODS: The driving clinical use case for research and development of ICEES was asthma, which is a common disease influenced by hundreds of genes and a plethora of environmental exposures, including exposures to airborne pollutants. We developed a pipeline for integrating clinical data on patients with asthma-like conditions with data on environmental exposures derived from multiple public data sources. The data were integrated at the patient and visit level and used to create de-identified, binned, "integrated feature tables," which were then placed behind an OpenAPI. RESULTS: Our preliminary evaluation results demonstrate a relationship between exposure to high levels of particulate matter ≤2.5 µm in diameter (PM2.5) and the frequency of emergency department or inpatient visits for respiratory issues. For example, 16.73% of patients with average daily exposure to PM2.5 >9.62 µg/m3 experienced 2 or more emergency department or inpatient visits for respiratory issues in year 2010 compared with 7.93% of patients with lower exposures (n = 23 093). DISCUSSION: The results validated our overall approach for openly exposing and sharing integrated clinical and environmental exposures data. We plan to iteratively refine and expand ICEES by including additional years of data, feature variables, and disease cohorts. CONCLUSIONS: We believe that ICEES will serve as a regulatory-compliant model and approach for promoting open access to and sharing of integrated clinical and environmental exposures data.

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

Anxious individuals predict the onset of aggression earlier in a CCTV surveillance task.

Attentional biases in anxious individuals can facilitate the detection of threatening stimuli. A particular field of research that may benefit from enhanced threat detection is in closed-circuit television (CCTV) surveillance, in which operators search through multiple camera feeds to attempt to identify threatening situations before they occur. The present study examined whether the enhanced threat detection of anxious individuals extends to the ability to detect threat in a multiple-scene CCTV task. Anxiety was measured in a nonclinical sample using the State-Trait Inventory for Cognitive and Somatic Anxiety. Participants were asked to try to find aggressive incidents that were simulated using the game Grand Theft Auto V in displays showing 1, 4, or 9 simultaneous videos. The results revealed that higher levels of trait cognitive, state cognitive, and trait somatic anxiety were related to earlier responses, with no change in confidence or accuracy. Increasing the number of screens to be monitored was associated with detecting the events later and a reduced confidence in responses. These results suggest that, in nonclinical populations, a moderate degree of anxiety may be beneficial in predicting acts of aggression during CCTV monitoring tasks. Trait (i.e., stable) levels of anxiety may inform recruitment of operators for surveillance tasks. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Effects of trait anxiety and situational stress on attentional shifting are buffered by working memory capacity.

BACKGROUND AND OBJECTIVES: Attentional Control Theory (ACT) predicts that trait anxiety and situation stress combine to reduce performance efficiency on tasks requiring rapid shifts in attention. Recent evidence has also suggested that working memory capacity (WMC) might moderate this relationship. We controlled for methodological difficulties in the existing literature to investigate the relationships between trait anxiety, situational stress, and WMC on attentional shifting. DESIGN AND METHOD: Seventy undergraduate students participated in the study. Trait anxiety was operationalized using questionnaire scores, situational stress was manipulated through a pressured counting task, and WMC was based on performance on the Automated Operation Span Task (AOSPAN). The shifting task involved a modified version of the Sternberg paradigm as the primary task and an oddball tone-discrimination task as the secondary task. Dependent variables were performance effectiveness (accuracy) and processing efficiency (accuracy divided by response time) on the secondary task. RESULTS: There was no effect of anxiety, stress, or WMC in predicting performance effectiveness; however, a significant three-way interaction on processing efficiency was observed. At higher WMC, anxiety and situational stress were not associated with processing efficiency. Conversely, at lower WMC, higher trait anxiety was associated with poorer efficiency but only for those who reported higher situational stress; for those who reported lower situational stress higher trait anxiety predicted facilitated efficiency. CONCLUSIONS: Results are interpreted with respect to ACT and directions for future research are discussed.