Abnormalities of Aspiration and Swallowing Function in Survivors of Acute Respiratory Failure.

The mechanisms responsible for aspiration are relatively unknown in patients recovering from acute respiratory failure (ARF) who required mechanical ventilation. Though many conditions may contribute to swallowing dysfunction, alterations in laryngeal structure and swallowing function likely play a role in the development of aspiration. At four university-based tertiary medical centers, we conducted a prospective cohort study of ARF patients who required intensive care and mechanical ventilation for at least 48 h. Within 72 h after extubation, a Fiberoptic Flexible Endoscopic Evaluation of Swallowing (FEES) examination was performed. Univariate and multivariable analyses examined the relationship between laryngeal structure and swallowing function abnormalities. Aspiration was the primary outcome, defined as a Penetration- Aspiration Scale (PAS) score of 6 or greater. Two other salient signs of dysphagia-spillage and residue-were secondary outcomes. A total of 213 patients were included in the final analysis. Aspiration was detected in 70 patients (33%) on at least one bolus. The most commonly aspirated consistency was thin liquids (27%). In univariate analyses, several abnormalities in laryngeal anatomy and structural movement were significantly associated with aspiration, spillage, and residue. In a multivariable analysis, the only variables that remained significant with aspiration were pharyngeal weakness (Odds ratio = 2.57, 95%CI = 1.16-5.84, p = 0.019) and upper airway edema (Odds ratio = 3.24, 95%CI = 1.44-7.66, p = 0.004). These results demonstrated that dysphagia in ARF survivors is multifactorial and characterized by both anatomic and physiologic abnormalities. These findings may have important implications for the development of novel interventions to treat dysphagia in ARF survivors.Clinical Trials Registration ClinicalTrials.gov Identifier: NCT02363686, Aspiration in Acute Respiratory Failure Survivors.

AltitudeOmics: Red Blood Cell Metabolic Adaptation to High Altitude Hypoxia.

Red blood cells (RBCs) are key players in systemic oxygen transport. RBCs respond to in vitro hypoxia through the so-called oxygen-dependent metabolic regulation, which involves the competitive binding of deoxyhemoglobin and glycolytic enzymes to the N-terminal cytosolic domain of band 3. This mechanism promotes the accumulation of 2,3-DPG, stabilizing the deoxygenated state of hemoglobin, and cytosol acidification, triggering oxygen off-loading through the Bohr effect. Despite in vitro studies, in vivo adaptations to hypoxia have not yet been completely elucidated. Within the framework of the AltitudeOmics study, erythrocytes were collected from 21 healthy volunteers at sea level, after exposure to high altitude (5260 m) for 1, 7, and 16 days, and following reascent after 7 days at 1525 m. UHPLC-MS metabolomics results were correlated to physiological and athletic performance parameters. Immediate metabolic adaptations were noted as early as a few hours from ascending to >5000 m, and maintained for 16 days at high altitude. Consistent with the mechanisms elucidated in vitro, hypoxia promoted glycolysis and deregulated the pentose phosphate pathway, as well purine catabolism, glutathione homeostasis, arginine/nitric oxide, and sulfur/H2S metabolism. Metabolic adaptations were preserved 1 week after descent, consistently with improved physical performances in comparison to the first ascendance, suggesting a mechanism of metabolic memory.

A graphical model method for integrating multiple sources of genome-scale data.

Making effective use of multiple data sources is a major challenge in modern bioinformatics. Genome-wide data such as measures of transcription factor binding, gene expression, and sequence conservation, which are used to identify binding regions and genes that are important to major biological processes such as development and disease, can be difficult to use together due to the different biological meanings and statistical distributions of the heterogeneous data types, but each can provide valuable information for understanding the processes under study. Here we present methods for integrating multiple data sources to gain a more complete picture of gene regulation and expression. Our goal is to identify genes and cis-regulatory regions which play specific biological roles. We describe a graphical mixture model approach for data integration, examine the effect of using different model topologies, and discuss methods for evaluating the effectiveness of the models. Model fitting is computationally efficient and produces results which have clear biological and statistical interpretations. The Hedgehog and Dorsal signaling pathways in Drosophila, which are critical in embryonic development, are used as examples.

SNPEVG: a graphical tool for GWAS graphing with mouse clicks.

BACKGROUND: Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers generate large quantities of tests results. Global and local graphical viewing of the test results is an effective approach to digest and interpret GWAS results. RESULTS: SNPEVG is a set of graphical tools for instant global and local viewing and graphing of GWAS results for all chromosomes and for each trait. The current version includes three programs, SNPEVG1, SNPEVG2 and SNPEVG3. SNPEVG1 is a graphical tool for SNP effect viewing of P-values allowing multiple traits. The total number of graphs that can be generated by one 'Run' is n(c + 2), where n is number of 'traits' with 0 < n ≤ 100, and c is the number of chromosomes. SNP effect viewing and graphing is accomplished through a user friendly graphical user interface (GUI) that provides a wide-range of options for the user to choose. The GUI can produce the Manhattan plot, the Q-Q plot of all SNP effects, and graphs for SNP effects by chromosome by clicking one command. Any or all the graphs can be saved with publication quality by clicking one command. SNPEVG2 is for the viewing and graphing of multiple traits on the same graph with options to graph any or all of the traits, customizable colors and user specified Y1 or Y2 axis for each traits. The SNPEVG3 program uses the output file of single-locus test results from the epiSNP computer package as the input file. Each chromosome figure can display three genetic effects (genotypic, additive and dominance effects), and the number of observations. CONCLUSIONS: The SNPEVG package is a versatile, flexible and efficient graphical tool for rapid digestion of large quantities of GWAS results with mouse clicks.

Core liver homeostatic co-expression networks are preserved but respond to perturbations in an organism- and disease-specific manner.

Findings about chronic complex diseases are difficult to extrapolate from animal models to humans. We reason that organs may have core network modules that are preserved between species and are predictably altered when homeostasis is disrupted. To test this idea, we perturbed hepatic homeostasis in mice by dietary challenge and compared the liver transcriptome with that in human fatty liver disease and liver cancer. Co-expression module preservation analysis pointed to alterations in immune responses and metabolism (core modules) in both human and mouse datasets. The extent of derailment in core modules was predictive of survival in the cancer genome atlas (TCGA) liver cancer dataset. We identified module eigengene quantitative trait loci (module-eQTL) for these predictive co-expression modules, targeting of which may resolve homeostatic perturbations and improve patient outcomes. The framework presented can be used to understand homeostasis at systems levels in pre-clinical models and in humans. A record of this paper's transparent peer review process is included in the supplemental information.

Ontology quality assurance through analysis of term transformations.

MOTIVATION: It is important for the quality of biological ontologies that similar concepts be expressed consistently, or univocally. Univocality is relevant for the usability of the ontology for humans, as well as for computational tools that rely on regularity in the structure of terms. However, in practice terms are not always expressed consistently, and we must develop methods for identifying terms that are not univocal so that they can be corrected. RESULTS: We developed an automated transformation-based clustering methodology for detecting terms that use different linguistic conventions for expressing similar semantics. These term sets represent occurrences of univocality violations. Our method was able to identify 67 examples of univocality violations in the Gene Ontology. AVAILABILITY: The identified univocality violations are available upon request. We are preparing a release of an open source version of the software to be available at http://bionlp.sourceforge.net.

Development of an Accurate Bedside Swallowing Evaluation Decision Tree Algorithm for Detecting Aspiration in Acute Respiratory Failure Survivors.

BACKGROUND: The bedside swallowing evaluation (BSE) is an assessment of swallowing function and airway safety during swallowing. After extubation, the BSE often is used to identify the risk of aspiration in acute respiratory failure (ARF) survivors. RESEARCH QUESTION: We conducted a multicenter prospective study of ARF survivors to determine the accuracy of the BSE and to develop a decision tree algorithm to identify aspiration risk. STUDY DESIGN AND METHODS: Patients extubated after ≥ 48 hours of mechanical ventilation were eligible. Study procedures included the BSE followed by a gold standard evaluation, the flexible endoscopic evaluation of swallowing (FEES). RESULTS: Overall, 213 patients were included in the final analysis. Median time from extubation to BSE was 25 hours (interquartile range, 21-45 hours). The FEES was completed 1 hour after the BSE (interquartile range, 0.5-2 hours). A total of 33% (70/213; 95% CI, 26.6%-39.2%) of patients aspirated on at least one FEES bolus consistency test. Thin liquids were the most commonly aspirated consistency: 27% (54/197; 95% CI, 21%-34%). The BSE detected any aspiration with an accuracy of 52% (95% CI, 45%-58%), a sensitivity of 83% (95% CI, 74%-92%), and negative predictive value (NPV) of 81% (95% CI, 72%-91%). Using recursive partitioning analyses, a five-variable BSE-based decision tree algorithm was developed that improved the detection of aspiration with an accuracy of 81% (95% CI, 75%-87%), sensitivity of 95% (95% CI, 90%-98%), and NPV of 97% (95% CI, 95%-99%). INTERPRETATION: The BSE demonstrates variable accuracy to identify patients at high risk for aspiration. Our decision tree algorithm may enhance the BSE and may be used to identify patients at high risk for aspiration, yet requires further validation. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT02363686; URL: www.clinicaltrials.gov.

Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies.

BACKGROUND: Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide opportunities to detect epistatic SNPs associated with quantitative traits and to detect the exact mode of an epistasis effect. Computational difficulty is the main bottleneck for epistasis testing in large scale GWAS. RESULTS: The EPISNPmpi and EPISNP computer programs were developed for testing single-locus and epistatic SNP effects on quantitative traits in GWAS, including tests of three single-locus effects for each SNP (SNP genotypic effect, additive and dominance effects) and five epistasis effects for each pair of SNPs (two-locus interaction, additive x additive, additive x dominance, dominance x additive, and dominance x dominance) based on the extended Kempthorne model. EPISNPmpi is the parallel computing program for epistasis testing in large scale GWAS and achieved excellent scalability for large scale analysis and portability for various parallel computing platforms. EPISNP is the serial computing program based on the EPISNPmpi code for epistasis testing in small scale GWAS using commonly available operating systems and computer hardware. Three serial computing utility programs were developed for graphical viewing of test results and epistasis networks, and for estimating CPU time and disk space requirements. CONCLUSION: The EPISNPmpi parallel computing program provides an effective computing tool for epistasis testing in large scale GWAS, and the epiSNP serial computing programs are convenient tools for epistasis analysis in small scale GWAS using commonly available computer hardware.

Detection of SNP epistasis effects of quantitative traits using an extended Kempthorne model.

Epistasis effects (gene interactions) have been increasingly recognized as important genetic factors underlying complex traits. The existence of a large number of single nucleotide polymorphisms (SNPs) provides opportunities and challenges to screen DNA variations affecting complex traits using a candidate gene analysis. In this article, four types of epistasis effects of two candidate gene SNPs with Hardy-Weinberg disequilibrium (HWD) and linkage disequilibrium (LD) are considered: additive x additive, additive x dominance, dominance x additive, and dominance x dominance. The Kempthorne genetic model was chosen for its appealing genetic interpretations of the epistasis effects. The method in this study consists of extension of Kempthorne's definitions of 35 individual genetic effects to allow HWD and LD, genetic contrasts of the 35 extended individual genetic effects to define the 4 epistasis effects, and a linear model method for testing epistasis effects. Formulas to predict statistical power (as a function of contrast heritability, sample size, and type I error) and sample size (as a function of contrast heritability, type I error, and type II error) for detecting each epistasis effect were derived, and the theoretical predictions agreed well with simulation studies. The accuracy in estimating each epistasis effect and rates of false positives in the absence of all or three epistasis effects were evaluated using simulations. The method for epistasis testing can be a useful tool to understand the exact mode of epistasis, to assemble genome-wide SNPs into an epistasis network, and to assemble all SNP effects affecting a phenotype using pairwise epistasis tests.

SiMCAL 1 algorithm for analysis of gene expression data related to the phosphatidylserine receptor.

OBJECTIVE: SiMCAL 1 (simple multilevel clustering and linking, version 1) is a novel clustering algorithm for time-series microarray data, presented here with an application to a specific data set. The purpose of the algorithm is to present a complete feature set not found in either Jarvis-Patrick clustering, from which it is derived, or in other popular clustering methods such as hierarchical and k-means. The data concern the activity of the phosphatidylserine receptor (PSR) which is believed to be a crucial molecular switch in the mediation of inflammatory response in apoptosis and lysis. By analyzing the behavior of PSR-related genes in mouse macrophages, we hope to elucidate the mechanisms involved in this important biological process. METHODS AND MATERIALS: SiMCAL 1 is implemented in the Python programming language using the Numerical Python extensions, and the data are stored using the MySQL database management system. The data are derived from exposures of multiple Affymetrix mouse gene microarray chips to elevated levels of PSR antibody and control conditions. Code and data are available at (accessed: 17 January 2005). RESULTS: The algorithm meets its objectives: it is simple, in that it is computationally inexpensive; it is multilevel, in that it provides a small number of clearly defined hierarchical levels of clusters; and it offers linking between clusters at the same level in each hierarchy. Clustering and linking results indicate previously unknown co-regulation for genes expressing PGH synthase (COX2) and PGE2, appear to confirm increased production of proteins for clearance of apoptotic cells in the presence of PSR antibody, and correspond to other findings regarding the temporal relationship between PGE2 production and B cell proliferation and differentiation. These results are promising but should be taken as highly preliminary. CONCLUSION: Both the algorithm and its application to this problem show great potential for future development. We plan to improve and extend the SiMCAL family of algorithms, and to obtain new data so that the algorithm(s) may be further applied to this and other problems of interest.

Genome-wide analysis of single-locus and epistasis single-nucleotide polymorphism effects on anti-cyclic citrullinated peptide as a measure of rheumatoid arthritis.

The goal of this study was to identify single-locus and epistasis effects of SNP markers on anti-cyclic citrullinated peptide (anti-CCP) that is associated with rheumatoid arthritis, using the North American Rheumatoid Arthritis Consortium data. A square root transformation of the phenotypic values of anti-CCP with sex, smoking status, and a selected subset of 20 single-nucleotide polymorphism (SNP) markers in the model achieved residual normality (p > 0.05). Three single-locus effects of two SNPs were significant (p < 10-4). The epistasis analysis tested five effects of each pair of SNPs, the two-locus interaction, additive x additive, additive x dominance, dominance x additive, and dominance x dominance effects. A total of ten epistasis effects of eight pairs of SNPs on 11 autosomes and the X chromosome had significant epistasis effects (p < 10-7). Three of these epistasis effects reached significance levels of p < 10-8, p < 10-9, and p < 10-10, respectively. Two potential SNP epistasis networks were identified. The results indicate that the genetic factors underlying anti-CCP may include single-gene action and gene interactions and that the gene-interaction mechanism underlying anti-CCP could be a complex mechanism involving pairwise epistasis effects and multiple SNPs.