27 research outputs found

    PICU Survivorship: Factors Affecting Feasibility and Cohort Retention in a Long-Term Outcomes Study

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    Our understanding of longitudinal outcomes of Pediatric Intensive Care Unit (PICU) survivors is limited by the heterogeneity of follow-up intervals, populations, and outcomes assessed. We sought to demonstrate (1) the feasibility of longitudinal multidimensional outcome assessment and (2) methods to promote cohort retention. The objective of this presented study was to provide details of follow-up methodology in a PICU survivor cohort and not to present the outcomes at long-term follow-up for this cohort. We enrolled 152 children aged 0 to 17 years admitted to the PICU in a prospective longitudinal cohort study. We examined resource utilization, family impact of critical illness, and neurodevelopment using the PICU Outcomes Portfolio (POP) Survey which included a study-specific survey and validated tools: 1. Functional Status Scale, 2. Pediatric Evaluation of Disability Inventory Computer Adaptive Test, 3. Pediatric Quality of Life Inventory, 4. Strengths and Difficulties Questionnaire, and 5. Vanderbilt Assessment Scales for Attention Deficit-Hyperactivity Disorder. POP Survey completion rates were 89%, 78%, and 84% at 1, 3, and 6 months. Follow-up rates at 1, 2, and 3 years were 80%, 55%, and 43%. Implementing a longitudinal multidimensional outcome portfolio for PICU survivors is feasible within an urban, tertiary-care, academic hospital. Our attrition after one year demonstrates the long-term follow-up challenges in this population. Our findings inform ongoing efforts to implement core outcome sets after pediatric critical illness

    Mortality and PICU hospitalization among pediatric gunshot wound victims in Chicago

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    Firearm injury accounts for significant morbidity with high mortality among children admitted to the PICU. Understanding risk factors for PICU admission is an important step toward developing prevention and intervention strategies to minimize the burden of pediatric gunshot wound (GSW) injury. Objectives: The primary objective of this study was to characterize outcomes and the likelihood of PICU admission among children with GSWs. Design setting and participants: Retrospective cohort study of GSW patients 0-18 years old evaluated at the University of Chicago Comer Children\u27s Hospital Pediatric Trauma Center from 2010 to 2017. Main outcomes and measures: Demographic and injury severity measures were acquired from an institutional database. We describe mortality and hospitalization characteristics for the cohort. We used logistic regression models to test the association between PICU admission and patient characteristics. Results: During the 8-year study period, 294 children experienced GSWs. We did not observe trends in overall mortality over time, but mortality for children with GSWs was higher than all-cause PICU mortality. Children 0-6 years old experienced longer hospitalizations compared with children 13-16 years old (5 vs 3 d; p = 0.04) and greater frequency of PICU admission (83.3% vs 52.9%; p = 0.001). Adjusting for severity of illness, children less than 7 years old were four-fold more likely to be admitted to the PICU than children 13-16 years old (aOR range, 3.9-4.6). Conclusions and relevance: Despite declines in pediatric firearm mortality across the United States, mortality did not decrease over time in our cohort and was higher than all-cause PICU mortality. Younger children with GSWs experience longer hospitalizations and require PICU care more often than older children. Our findings suggest that the youngest victims of firearm-related injury may be particularly at-risk of the long-term sequelae of critical illness and injury

    Combining patient visual timelines with deep learning to predict mortality.

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    BackgroundDeep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality.Methods and findingsAll adult consecutive patient admissions from 2008-2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P ConclusionsWe converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine

    The quantitative changes in the yeast Hsp70 and Hsp90 interactomes upon DNA damage

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    The molecular chaperones Hsp70 and Hsp90 participate in many important cellular processes, including how cells respond to DNA damage. Here we show the results of applied quantitative affinity-purification mass spectrometry (AP-MS) proteomics to understand the protein network through which Hsp70 and Hsp90 exert their effects on the DNA damage response (DDR). We characterized the interactomes of the yeast Hsp70 isoform Ssa1 and Hsp90 isoform Hsp82 before and after exposure to methyl methanesulfonate. We identified 256 chaperone interactors, 146 of which are novel. Although the majority of chaperone interaction remained constant under DNA damage, 5 proteins (Coq5, Ast1, Cys3, Ydr210c and Rnr4) increased in interaction with Ssa1 and/or Hsp82. This data presented here are related to [1] (Truman et al., in press). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository (Vizcaino et al. (2013) [2]) with the dataset identifier PXD001284

    Spatial and Functional Relationships Among Pol V-Associated Loci, Pol IV-Dependent siRNAs, and Cytosine Methylation in the \u3cem\u3eArabidopsis\u3c/em\u3e Epigenome

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    Multisubunit RNA polymerases IV and V (Pols IV and V) mediate RNA-directed DNA methylation and transcriptional silencing of retrotransposons and heterochromatic repeats in plants. We identified genomic sites of Pol V occupancy in parallel with siRNA deep sequencing and methylcytosine mapping, comparing wild-type plants with mutants defective for Pol IV, Pol V, or both Pols IV and V. Approximately 60% of Pol V-associated regions encompass regions of 24-nucleotide (nt) siRNA complementarity and cytosine methylation, consistent with cytosine methylation being guided by base-pairing of Pol IV-dependent siRNAs with Pol V transcripts. However, 27% of Pol V peaks do not overlap sites of 24-nt siRNA biogenesis or cytosine methylation, indicating that Pol V alone does not specify sites of cytosine methylation. Surprisingly, the number of methylated CHH motifs, a hallmark of RNA-directed de novo methylation, is similar in wild-type plants and Pol IV or Pol V mutants. In the mutants, methylation is lost at 50%–60% of the CHH sites that are methylated in the wild type but is gained at new CHH positions, primarily in pericentromeric regions. These results indicate that Pol IV and Pol V are not required for cytosine methyltransferase activity but shape the epigenome by guiding CHH methylation to specific genomic sites

    Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

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    Abstract Background Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls. Results With a variety of options that include peak processing, deisotoping, isotope composition, etc, Decon2LS supports processing of multiple raw data formats. Deisotoping can be performed on an individual scan, an individual dataset, or on multiple datasets using batch processing. Other processing options include creating a two dimensional view of mass and liquid chromatography (LC) elution time features, generating spectrum files for tandem MS data, creating total intensity chromatograms, and visualizing theoretical peptide profiles. Application of Decon2LS to deisotope different datasets obtained across different instruments yielded a high number of features that can be used to identify and quantify peptides in the biological sample. Conclusion Decon2LS is an efficient software package for discovering and visualizing features in proteomics studies that require automated interpretation of mass spectra. Besides being easy to use, fast, and reliable, Decon2LS is also open-source, which allows developers in the proteomics and bioinformatics communities to reuse and refine the algorithms to meet individual needs. Decon2LS source code, installer, and tutorials may be downloaded free of charge at http://http:/ncrr.pnl.gov/software/