Augmented renal clearance of aminoglycosides using population-based pharmacokinetic modelling with Bayesian estimation in the paediatric ICU.

OBJECTIVE: To evaluate augmented renal clearance (ARC) using aminoglycoside clearance (CLAMINO24h) derived from pharmacokinetic (PK) modelling. METHODS: A retrospective study at two paediatric hospitals of patients who received tobramycin or gentamicin from 1999 to 2016 was conducted. Compartmental PK models were constructed using the Pmetrics package, and Bayesian posteriors were used to estimate CLAMINO24h. ARC was defined as a CLAMINO24h of ≥130 mL/min/1.73 m2. Risk factors for ARC were identified using multivariate logistic regression. RESULTS: The final population model was fitted to 275 aminoglycoside serum concentrations. Overall clearance (L/h) was=CL0×(TBW/70)0.75×AGEH/(TMH + AGEH) + CL1 (0.5/SCr), where TBW is total body weight, H is the Hill coefficient, TM is a maturation term and SCr is serum creatinine. Median CLAMINO24h in those with versus without ARC was 157.36 and 93.42 mL/min/1.73 m2, respectively (P<0.001). ARC was identified in 19.5% of 118 patients. For patients with ARC, median baseline SCr was lower than for those without ARC (0.38 versus 0.41 mg/dL, P=0.073). Risk factors for ARC included sepsis [adjusted OR (aOR) 3.77, 95% CI 1.01-14.07, P=0.048], increasing age (aOR 1.11, 95% CI 1-1.23, P=0.04) and low log-transformed SCr (aOR 0.16, 95% CI 0.05-0.52, P=0.002). Median 24 h AUC (AUC24h) was significantly lower in patients with ARC at 45.27 versus 56.95 mg·h/L, P<0.01. CONCLUSIONS: ARC was observed in one of every five patients. Sepsis, increasing age and low SCr were associated with ARC. Increased clearance was associated with an attenuation of AUC24h in this population. Future studies are needed to define optimal dosing in paediatric patients with ARC.

Increased Energy Expenditure, Ucp1 Expression, and Resistance to Diet-induced Obesity in Mice Lacking Nuclear Factor-Erythroid-2-related Transcription Factor-2 (Nrf2).

The NRF2 (also known as NFE2L2) transcription factor is a critical regulator of genes involved in defense against oxidative stress. Previous studies suggest thatNrf2plays a role in adipogenesisin vitro, and deletion of theNrf2gene protects against diet-induced obesity in mice. Here, we demonstrate that resistance to diet-induced obesity inNrf2(-/-)mice is associated with a 20-30% increase in energy expenditure. Analysis of bioenergetics revealed thatNrf2(-/-)white adipose tissues exhibit greater oxygen consumption. White adipose tissue showed a >2-fold increase inUcp1gene expression. Oxygen consumption is also increased nearly 2.5-fold inNrf2-deficient fibroblasts. Oxidative stress induced by glucose oxidase resulted in increasedUcp1expression. Conversely, antioxidant chemicals (such asN-acetylcysteine and Mn(III)tetrakis(4-benzoic acid)porphyrin chloride) and SB203580 (a known suppressor ofUcp1expression) decreasedUcp1and oxygen consumption inNrf2-deficient fibroblasts. These findings suggest that increasing oxidative stress by limitingNrf2function in white adipocytes may be a novel means to modulate energy balance as a treatment of obesity and related clinical disorders.

Correlates of quality of life among individuals with epilepsy enrolled in self-management research: From the US Centers for Disease Control and Prevention Managing Epilepsy Well Network.

RATIONALE: Epilepsy is a chronic neurological condition that causes substantial burden on patients and families. Quality of life may be reduced due to the stress of coping with epilepsy. For nearly a decade, the Centers for Disease Control (CDC) Prevention Research Center's Managing Epilepsy Well (MEW) Network has been conducting research on epilepsy self-management to address research and practice gaps. Studies have been conducted by independent centers across the U.S. Recently, the MEW Network sites, collaboratively, began compiling an integrated database to facilitate aggregate secondary analysis of completed and ongoing studies. In this preliminary analysis, correlates of quality of life in people with epilepsy (PWE) were analyzed from pooled baseline data from the MEW Network. METHODS: For this analysis, data originated from 6 epilepsy studies conducted across 4 research sites and comprised 459 PWE. Descriptive comparisons assessed common data elements that included gender, age, ethnicity, race, education, employment, income, seizure frequency, quality of life, and depression. Standardized rating scales were used for quality of life (QOLIE-10) and for depression (Patient Health Questionnaire, PHQ-9). RESULTS: While not all datasets included all common data elements, baseline descriptive analysis found a mean age of 42 (SD 13.22), 289 women (63.0%), 59 African Americans (13.7%), and 58 Hispanics (18.5%). Most, 422 (92.8%), completed at least high school, while 169 (61.7%) were unmarried, divorced/separated, or widowed. Median 30-day seizure frequency was 0.71 (range 0-308). Depression at baseline was common, with a mean PHQ-9 score of 8.32 (SD 6.04); 69 (29.0%) had depression in the mild range (PHQ-9 score 5-9) and 92 (38.7%) had depression in the moderate to severe range (PHQ-9 score >9). Lower baseline quality of life was associated with greater depressive severity (p<.001), more frequent seizures (p<.04) and lower income (p<.05). CONCLUSIONS: The MEW Network Integrated Database offers a unique opportunity for secondary analysis of data from multiple community-based epilepsy research studies. While findings must be tempered by potential sample bias, i.e. a relative under-representation of men and relatively small sample of some racial/ethnic subgroups, results of analyses derived from this first integrated epilepsy self-management database have potential to be useful to the field. Associations between depression severity and lower QOL in PWE are consistent with previous studies derived from clinical samples. Self-management efforts that focus on mental health comorbidity and seizure control may be one way to address modifiable factors that affect quality of life in PWE.

Semantic Provenance Graph for Reproducibility of Biomedical Research Studies: Generating and Analyzing Graph Structures from Published Literature.

OBJECTIVE: To characterize the scientific reproducibility of biomedical research studies by query and analysis of semantic provenance graphs generated from provenance metadata terms extracted from PubMed articles. METHODS: We develop a new semantic provenance graph generation algorithm that uses a provenance ontology developed as part of the Provenance for Clinical and Health Research (ProvCaRe) project. The ProvCaRe project has processed and extracted provenance metadata from more than 1.6 million full text articles from the PubMed database. RESULTS: The semantic provenance graph generation algorithm is evaluated using provenance terms extracted from 75 selected articles describing sleep medicine research studies. In addition, we use eight provenance queries to evaluate the quality of semantic provenance graphs generated by the new algorithm. CONCLUSION: The ProvCaRe project has created a unique resource to characterize the reproducibility of biomedical research studies and the semantic provenance graph generation algorithm enables users to effectively query and analyze the provenance metadata in the ProvCaRe knowledge repository.

ProvCaRe: Characterizing scientific reproducibility of biomedical research studies using semantic provenance metadata.

OBJECTIVE: Reproducibility of research studies is key to advancing biomedical science by building on sound results and reducing inconsistencies between published results and study data. We propose that the available data from research studies combined with provenance metadata provide a framework for evaluating scientific reproducibility. We developed the ProvCaRe platform to model, extract, and query semantic provenance information from 435, 248 published articles. METHODS: The ProvCaRe platform consists of: (1) the S3 model and a formal ontology; (2) a provenance-focused text processing workflow to generate provenance triples consisting of subject, predicate, and object using metadata extracted from articles; and (3) the ProvCaRe knowledge repository that supports "provenance-aware" hypothesis-driven search queries. A new provenance-based ranking algorithm is used to rank the articles in the search query results. RESULTS: The ProvCaRe knowledge repository contains 48.9 million provenance triples. Seven research hypotheses were used as search queries for evaluation and the resulting provenance triples were analyzed using five categories of provenance terms. The highest number of terms (34%) described provenance related to population cohort followed by 29% of terms describing statistical data analysis methods, and only 5% of the terms described the measurement instruments used in a study. In addition, the analysis showed that some articles included a higher number of provenance terms across multiple provenance categories suggesting a higher potential for reproducibility of these research studies. CONCLUSION: The ProvCaRe knowledge repository (https://provcare. CASE: edu/) is one of the largest provenance resources for biomedical research studies that combines intuitive search functionality with a new provenance-based ranking feature to list articles related to a search query.

ProvCaRe Semantic Provenance Knowledgebase: Evaluating Scientific Reproducibility of Research Studies.

Scientific reproducibility is critical for biomedical research as it enables us to advance science by building on previous results, helps ensure the success of increasingly expensive drug trials, and allows funding agencies to make informed decisions. However, there is a growing "crisis" of reproducibility as evidenced by a recent Nature journal survey of more than 1500 researchers that found that 70% of researchers were not able to replicate results from other research groups and more than 50% of researchers were not able reproduce their own research results. In 2016, the National Institutes of Health (NIH) announced the "Rigor and Reproducibility" guidelines to support reproducibility in biomedical research. A key component of the NIH Rigor and Reproducibility guidelines is the recording and analysis of "provenance" information, which describes the origin or history of data and plays a central role in ensuring scientific reproducibility. As part of the NIH Big Data to Knowledge (BD2K)-funded data provenance project, we have developed a new informatics framework called Provenance for Clinical and Healthcare Research (ProvCaRe) to extract, model, and analyze provenance information from published literature describing research studies. Using sleep medicine research studies that have made their data available through the National Sleep Research Resource (NSRR), we have developed an automated pipeline to identify and extract provenance metadata from published literature that is made available for analysis in the ProvCaRe knowledgebase. NSRR is the largest repository of sleep data from over 40,000 studies involving 36,000 participants and we used 75 published articles describing 6 research studies to populate the ProvCaRe knowledgebase. We evaluated the ProvCaRe knowledgebase with 28,474 "provenance triples" using hypothesis-driven queries to identify and rank research studies based on the provenance information extracted from published articles.

Nuclear factor-erythroid-2 related transcription factor-1 (Nrf1) is regulated by O-GlcNAc transferase.

The Nrf1 (Nuclear factor E2-related factor 1) transcription factor performs a critical role in regulating cellular homeostasis. Using a proteomic approach, we identified Host Cell Factor-1 (HCF1), a co-regulator of transcription, and O-GlcNAc transferase (OGT), the enzyme that mediates protein O-GlcNAcylation, as cellular partners of Nrf1a, an isoform of Nrf1. Nrf1a directly interacts with HCF1 through the HCF1 binding motif (HBM), while interaction with OGT is mediated through HCF1. Overexpression of HCF1 and OGT leads to increased Nrf1a protein stability. Addition of O-GlcNAc decreases ubiquitination and degradation of Nrf1a. Transcriptional activation by Nrf1a is increased by OGT overexpression and treatment with PUGNAc. Together, these data suggest that OGT can act as a regulator of Nrf1a.

Scientific Reproducibility in Biomedical Research: Provenance Metadata Ontology for Semantic Annotation of Study Description.

Scientific reproducibility is key to scientific progress as it allows the research community to build on validated results, protect patients from potentially harmful trial drugs derived from incorrect results, and reduce wastage of valuable resources. The National Institutes of Health (NIH) recently published a systematic guideline titled "Rigor and Reproducibility " for supporting reproducible research studies, which has also been accepted by several scientific journals. These journals will require published articles to conform to these new guidelines. Provenance metadata describes the history or origin of data and it has been long used in computer science to capture metadata information for ensuring data quality and supporting scientific reproducibility. In this paper, we describe the development of Provenance for Clinical and healthcare Research (ProvCaRe) framework together with a provenance ontology to support scientific reproducibility by formally modeling a core set of data elements representing details of research study. We extend the PROV Ontology (PROV-O), which has been recommended as the provenance representation model by World Wide Web Consortium (W3C), to represent both: (a) data provenance, and (b) process provenance. We use 124 study variables from 6 clinical research studies from the National Sleep Research Resource (NSRR) to evaluate the coverage of the provenance ontology. NSRR is the largest repository of NIH-funded sleep datasets with 50,000 studies from 36,000 participants. The provenance ontology reuses ontology concepts from existing biomedical ontologies, for example the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), to model the provenance information of research studies. The ProvCaRe framework is being developed as part of the Big Data to Knowledge (BD2K) data provenance project.

An Ontology-Enabled Natural Language Processing Pipeline for Provenance Metadata Extraction from Biomedical Text (Short Paper).

Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility. Reproducibility of results reported by previous research studies is a foundational component of scientific advancement. This is highlighted by the recent initiative by the US National Institutes of Health called "Principles of Rigor and Reproducibility". In this paper, we describe an effective approach to extract provenance metadata from published biomedical research literature using an ontology-enabled NLP platform as part of the Provenance for Clinical and Healthcare Research (ProvCaRe). The ProvCaRe-NLP tool extends the clinical Text Analysis and Knowledge Extraction System (cTAKES) platform using both provenance and biomedical domain ontologies. We demonstrate the effectiveness of ProvCaRe-NLP tool using a corpus of 20 peer-reviewed publications. The results of our evaluation demonstrate that the ProvCaRe-NLP tool has significantly higher recall in extracting provenance metadata as compared to existing NLP pipelines such as MetaMap.

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig.

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This "neuroscience Big data" represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability-the ability to efficiently process increasing volumes of data; (b) Adaptability-the toolkit can be deployed across different computing configurations; and (c) Ease of programming-the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.

Insight: An ontology-based integrated database and analysis platform for epilepsy self-management research.

We present Insight as an integrated database and analysis platform for epilepsy self-management research as part of the national Managing Epilepsy Well Network. Insight is the only available informatics platform for accessing and analyzing integrated data from multiple epilepsy self-management research studies with several new data management features and user-friendly functionalities. The features of Insight include, (1) use of Common Data Elements defined by members of the research community and an epilepsy domain ontology for data integration and querying, (2) visualization tools to support real time exploration of data distribution across research studies, and (3) an interactive visual query interface for provenance-enabled research cohort identification. The Insight platform contains data from five completed epilepsy self-management research studies covering various categories of data, including depression, quality of life, seizure frequency, and socioeconomic information. The data represents over 400 participants with 7552 data points. The Insight data exploration and cohort identification query interface has been developed using Ruby on Rails Web technology and open source Web Ontology Language Application Programming Interface to support ontology-based reasoning. We have developed an efficient ontology management module that automatically updates the ontology mappings each time a new version of the Epilepsy and Seizure Ontology is released. The Insight platform features a Role-based Access Control module to authenticate and effectively manage user access to different research studies. User access to Insight is managed by the Managing Epilepsy Well Network database steering committee consisting of representatives of all current collaborating centers of the Managing Epilepsy Well Network. New research studies are being continuously added to the Insight database and the size as well as the unique coverage of the dataset allows investigators to conduct aggregate data analysis that will inform the next generation of epilepsy self-management studies.