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Objective: The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method.Methods: An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MedLEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts.Results: Recall of the system for UMLS coding of all terms was .77 (95% CI.72-.81), and for coding terms that had corresponding UMLS codes recall was .83 (.79-.87). Recall of the system for extracting all terms was .84 (.81-.88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87-.91), and precision of the experts ranged from .61 to .91.Conclusion: Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval. [ABSTRACT FROM AUTHOR]

Abstract Background Mouse xenograft models, in which human cancer cells are implanted in immune-suppressed mice, have been popular for studying the mechanisms of novel therapeutic targets, tumor progression and metastasis. We hypothesized that we could exploit the interspecies genetic differences in these experiments. Our purpose is to elucidate stromal microenvironment signals from probes on human arrays unintentionally cross-hybridizing with mouse homologous genes in xenograft tumor models. Results By identifying cross-species hybridizing probes from sequence alignment and cross-species hybridization experiment for the human whole-genome arrays, deregulated stromal genes can be identified and then their biological significance were predicted from enrichment studies. Comparing these results with those found by the laser capture microdissection of stromal cells from tumor specimens resulted in the discovery of significantly enriched stromal biological processes. Conclusions Using this method, in addition to their primary endpoints, researchers can leverage xenograft experiments to better characterize the tumor microenvironment without additional costs. The Xhyb probes and R script are available at http://www.lussierlab.org/publications/Stroma

Abstract Background Biological data that are well-organized by an ontology, such as Gene Ontology, enables high-throughput availability of the semantic web. It can also be used to facilitate high throughput classification of biomedical information. However, to our knowledge, no evaluation has been published on automating classifications of human diseases genes using Gene Ontology. In this study, we evaluate automated classifications of well-defined human disease genes using their Gene Ontology annotations and compared them to a gold standard. This gold standard was independently conceived by Valle's research group, and contains 923 human disease genes organized in 14 categories of protein function. Results Two automated methods were applied to investigate the classification of human disease genes into independently pre-defined categories of protein function. One method used the structure of Gene Ontology by pre-selecting 74 Gene Ontology terms assigned to 11 protein function categories. The second method was based on the similarity of human disease genes clustered according to the information-theoretic distance of their Gene Ontology annotations. Compared to the categorization of human disease genes found in the gold standard, our automated methods can achieve an overall 56% and 47% precision with 62% and 71% recall respectively. However, approximately 15% of the studied human disease genes remain without GO annotations. Conclusion Automated methods can recapitulate a significant portion of classification of the human disease genes. The method using information-theoretic distance performs slightly better on the precision with some loss in recall. For some protein function categories, such as 'hormone' and 'transcription factor', the automated methods perform particularly well, achieving precision and recall levels above 75%. In summary, this study demonstrates that for semantic webs, methods to automatically classify or analyze a majority of human disease genes require significant progress in both the Gene Ontology annotations and particularly in the utilization of these annotations.

Abstract Background In recent years, there have been numerous initiatives undertaken to describe critical information needs related to the collection, management, analysis, and dissemination of data in support of biomedical research (J Investig Med 54:327-333, 2006); (J Am Med Inform Assoc 16:316–327, 2009); (Physiol Genomics 39:131-140, 2009); (J Am Med Inform Assoc 18:354–357, 2011). A common theme spanning such reports has been the importance of understanding and optimizing people, organizational, and leadership factors in order to achieve the promise of efficient and timely research (J Am Med Inform Assoc 15:283–289, 2008). With the emergence of clinical and translational science (CTS) as a national priority in the United States, and the corresponding growth in the scale and scope of CTS research programs, the acuity of such information needs continues to increase (JAMA 289:1278–1287, 2003); (N Engl J Med 353:1621–1623, 2005); (Sci Transl Med 3:90, 2011). At the same time, systematic evaluations of optimal people, organizational, and leadership factors that influence the provision of data, information, and knowledge management technologies and methods are notably lacking. Methods In response to the preceding gap in knowledge, we have conducted both: 1) a structured survey of domain experts at Academic Health Centers (AHCs); and 2) a subsequent thematic analysis of public-domain documentation provided by those same organizations. The results of these approaches were then used to identify critical factors that may influence access to informatics expertise and resources relevant to the CTS domain. Results A total of 31 domain experts, spanning the Biomedical Informatics (BMI), Computer Science (CS), Information Science (IS), and Information Technology (IT) disciplines participated in a structured surveyprocess. At a high level, respondents identified notable differences in theaccess to BMI, CS, and IT expertise and services depending on the establishment of a formal BMI academic unit and the perceived relationship between BMI, CS, IS, and IT leaders. Subsequent thematic analysis of the aforementioned public domain documents demonstrated a discordance between perceived and reported integration across and between BMI, CS, IS, and IT programs and leaders with relevance to the CTS domain. Conclusion Differences in people, organization, and leadership factors do influence the effectiveness of CTS programs, particularly with regard to the ability to access and leverage BMI, CS, IS, and IT expertise and resources. Based on this finding, we believe that the development of a better understanding of how optimal BMI, CS, IS, and IT organizational structures and leadership models are designed and implemented is critical to both the advancement of CTS and ultimately, to improvements in the quality, safety, and effectiveness of healthcare.

Abstract Background The ability to rapidly characterize an unknown microorganism is critical in both responding to infectious disease and biodefense. To do this, we need some way of anticipating an organism's phenotype based on the molecules encoded by its genome. However, the link between molecular composition (i.e. genotype) and phenotype for microbes is not obvious. While there have been several studies that address this challenge, none have yet proposed a large-scale method integrating curated biological information. Here we utilize a systematic approach to discover genotype-phenotype associations that combines phenotypic information from a biomedical informatics database, GIDEON, with the molecular information contained in National Center for Biotechnology Information's Clusters of Orthologous Groups database (NCBI COGs). Results Integrating the information in the two databases, we are able to correlate the presence or absence of a given protein in a microbe with its phenotype as measured by certain morphological characteristics or survival in a particular growth media. With a 0.8 correlation score threshold, 66% of the associations found were confirmed by the literature and at a 0.9 correlation threshold, 86% were positively verified. Conclusion Our results suggest possible phenotypic manifestations for proteins biochemically associated with sugar metabolism and electron transport. Moreover, we believe our approach can be extended to linking pathogenic phenotypes with functionally related proteins.

Pharmacologic interventions to halt/reverse the vascular remodeling and right ventricular dysfunction in pulmonary arterial hypertension (PAH) remains an unmet need. We previously demonstrated extracellular nicotinamide phosphoribosyltransferase (eNAMPT) as a DAMP (damage-associated molecular pattern protein) contributing to PAH pathobiology via TLR4 ligation. We examined the role of endothelial cell (EC)-specific eNAMPT in experimental PH and an eNAMPT-neutralizing mAb as a therapeutic strategy to reverse established PH. Hemodynamic/echocardiographic measurements and tissue analyses were performed in Sprague Dawley rats exposed to 10% hypoxia/Sugen (three weeks) followed by return to normoxia and weekly intraperitoneal delivery of the eNAMPT mAb (1 mg/kg). WT C57BL/6J mice and conditional EC-cNAMPT ec-/- mice were exposed to 10% hypoxia (three weeks). Biochemical and RNA sequencing studies were performed on rat PH lung tissues and human PAH PBMCs. Hypoxia/Sugen-exposed rats exhibited multiple indices of severe PH (right ventricular systolic pressure, Fulton index), including severe vascular remodeling, compared to control rats. PH severity indices and plasma levels of eNAMPT, IL-6, and TNF- α were all significantly attenuated by eNAMPT mAb neutralization. Compared to hypoxia-exposed WT mice, cNAMPT ec-/- KO mice exhibited significantly reduced PH severity and evidence of EC to mesenchymal transition (EndMT). Finally, biochemical and RNAseq analyses revealed eNAMPT mAb-mediated rectification of dysregulated inflammatory signaling pathways (TLR/NF-κB, MAP kinase, Akt/mTOR) and EndMT in rat PH lung tissues and human PAH PBMCs. These studies underscore EC-derived eNAMPT as a key contributor to PAH pathobiology and support the eNAMPT/TLR4 inflammatory pathway as a highly druggable therapeutic target to reduce PH severity and reverse PAH., (© The Author(s) 2021.)

Despite growing interest in intuitive eating-a non-dieting approach to eating that is based on feeding the body in accordance with physiological and satiety cues-research on its determinants is scarce. The present study aimed to examine the associations between dimensions of adult attachment (i.e., anxiety and avoidance) and intuitive eating, and the mediating role of self-compassion in these relationships. The sample comprised 201 French-Canadian young adult women (M = 25.1, SD = 4.6). Participants completed self-report questionnaires through an online survey. Results of the structural equation model demonstrated that attachment-related anxiety and avoidance were negatively associated with intuitive eating, and these relationships were at least partially mediated by self-compassion. Findings suggest that women who have high levels of attachment anxiety or avoidance engage in less intuitive eating partly because they are less self-compassionate. Results highlight the importance of self-compassion in facilitating adaptive eating behaviors in adult women, especially if they have an insecure attachment style to romantic partners.

Aquaporin-2 (AQP2) is a homotetrameric water channel responsible for the final water reuptake in the kidney. Disease-causing AQP2 mutations induce nephrogenic diabetes insipidus (NDI), a condition that challenges the bodily water balance by producing large urinary volumes. In this study, we characterize three new AQP2 mutations identified in our lab from NDI patients (A120D, A130V, T179N) along the previously reported A47V variant. Using Xenopus oocytes, we compared the key functional and biochemical features of these mutations against classical recessive (R187C) and dominant (R254Q) forms, and once again found clear functional recovery features (increased protein stability and function) for all mutations under study. This behaviour, attributed to heteromerization to wt-AQP2, challenge the classical model to NDI which often depicts recessive mutations as ill-structured proteins unable to oligomerize. Consequently, we propose a revised model to the cell pathophysiology of AQP2-related NDI which accounts for the functional recovery of recessive AQP2 mutations., (© 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society.)

Rationale: Despite the availability of multi-"omics" strategies, insights into the etiology and pathogenesis of sarcoidosis have been elusive. This is partly due to the lack of reliable preclinical models and a paucity of validated biomarkers. As granulomas are a key feature of sarcoidosis, we speculate that direct genomic interrogation of sarcoid tissues, may lead to identification of dysregulated gene pathways or biomarker signatures., Objective: To facilitate the development sarcoidosis genomic biomarkers by gene expression profiling of sarcoidosis granulomas in lung and lymph node tissues (most commonly affected organs) and comparison to infectious granulomas (coccidiodomycosis and tuberculosis)., Methods: Transcriptomic profiles of immune-related gene from micro-dissected sarcoidosis granulomas within lung and mediastinal lymph node tissues and compared to infectious granulomas from paraffin-embedded blocks. Differentially-expressed genes (DEGs) were profiled, compared among the three granulomatous diseases and analyzed for functional enrichment pathways., Results: Despite histologic similarities, DEGs and pathway enrichment markedly differed in sarcoidosis granulomas from lymph nodes and lung. Lymph nodes showed a clear immunological response, whereas a structural regenerative response was observed in lung. Sarcoidosis granuloma gene expression data corroborated previously reported genomic biomarkers (STAB1, HBEGF, and NOTCH4), excluded others and identified new genomic markers present in lung and lymph nodes, ADAMTS1, NPR1 and CXCL2. Comparisons between sarcoidosis and pathogen granulomas identified pathway divergences and commonalities at gene expression level., Conclusion: These findings suggest the importance of tissue and disease-specificity evaluation when exploring sarcoidosis genomic markers. This relevant translational information in sarcoidosis and other two histopathological similar infections provides meaningful specific genomic-derived biomarkers for sarcoidosis diagnosis and prognosis.

Background: There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome., Methods: This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality., Results: From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04)., Conclusions: An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization.

The four hyperpolarization-activated cylic-nucleotide gated (HCN) channel isoforms and their auxiliary subunit KCNE2 are important in the regulation of peripheral and central neuronal firing and the heartbeat. Disruption of their normal function has been implicated in cardiac arrhythmias, peripheral pain, and epilepsy. However, molecular details of the HCN-KCNE2 complexes are unknown. Using single-molecule subunit counting, we determined that the number of KCNE2 subunits in complex with the pore-forming subunits of human HCN channels differs with each HCN isoform and is dynamic with respect to concentration. These interactions can be altered by KCNE2 gene-variants with functional implications. The results provide an additional consideration necessary to understand heart rhythm, pain, and epileptic disorders.

Deficiency of the antidiuretic hormone arginine vasopressin (AVP) underlies diabetes insipidus, which is characterized by the excretion of abnormally large volumes of dilute urine and persistent thirst. In this issue of the JCI, Shi et al. report that Sel1L-Hrd1 ER-associated degradation (ERAD) is responsible for the clearance of misfolded pro-arginine vasopressin (proAVP) in the ER. Additionally, mice with Sel1L deficiency, either globally or specifically within AVP-expressing neurons, developed central diabetes insipidus. The results of this study demonstrate a role for ERAD in neuroendocrine cells and serve as a clinical example of the effect of misfolded ER proteins retrotranslocated through the membrane into the cytosol, where they are polyubiquitinated, extracted from the ER membrane, and degraded by the proteasome. Moreover, proAVP misfolding in hereditary central diabetes insipidus likely shares common physiopathological mechanisms with proinsulin misfolding in hereditary diabetes mellitus of youth.

Sarcoidosis is a granulomatous lung disorder of unknown cause. The majority of individuals with sarcoidosis spontaneously achieve full remission (uncomplicated sarcoidosis), however, ~20% of sarcoidosis-affected individuals experience progressive lung disease or cardiac and nervous system involvement (complicated sarcoidosis). We investigated peripheral blood mononuclear cell (PBMC) microRNA and protein-coding gene expression data from healthy controls and patients with uncomplicated or complicated sarcoidosis. We identified 46 microRNAs and 1,559 genes that were differentially expressed across a continuum of sarcoidosis severity (healthy control → uncomplicated sarcoidosis → complicated sarcoidosis). A total of 19 microRNA-mRNA regulatory pairs were identified within these deregulated microRNAs and mRNAs, which consisted of 17 unique protein-coding genes yielding a 17-gene signature. Pathway analysis of the 17-gene signature revealed Jak-STAT signaling pathway as the most significantly represented pathway. A severity score was assigned to each patient based on the expression of the 17-gene signature and a significant increasing trend in the severity score was observed from healthy control, to uncomplicated sarcoidosis, and finally to complicated sarcoidosis. In addition, this microRNA-regulated gene signature differentiates sarcoidosis patients from healthy controls in independent validation cohorts. Our study suggests that PBMC gene expression is useful in diagnosis of sarcoidosis.

Sickle cell disease (SCD) complications are associated with increased morbidity and risk of mortality. We sought to identify a circulating transcriptomic profile predictive of these poor outcomes in SCD. Training and testing cohorts consisting of adult patients with SCD were recruited and prospectively followed. A pathway-based signature derived from grouping peripheral blood mononuclear cell transcriptomes distinguished 2 patient clusters with differences in survival in the training cohort. These findings were validated in a testing cohort in which the association between cluster 1 molecular profiling and mortality remained significant in a fully adjusted model. In a third cohort of West African children with SCD, cluster 1 differentiated SCD severity using a published scoring index. Finally, a risk score composed of assigning weights to cluster 1 profiling, along with established clinical risk factors using tricuspid regurgitation velocity, white blood cell count, history of acute chest syndrome, and hemoglobin levels, demonstrated a higher hazard ratio for mortality in both the training and testing cohorts compared with clinical risk factors or cluster 1 data alone. Circulating transcriptomic profiles are a powerful method to risk-stratify severity of disease and poor outcomes in both children and adults, respectively, with SCD and highlight potential associated molecular pathways., (© 2017 by The American Society of Hematology.)

The renal proximal tubule reabsorbs 90% of the filtered glucose load through the Na + -coupled glucose transporter SGLT2, and specific inhibitors of SGLT2 are now available to patients with diabetes to increase urinary glucose excretion. Using expression cloning, we identified an accessory protein, 17 kDa membrane-associated protein (MAP17), that increased SGLT2 activity in RNA-injected Xenopus oocytes by two orders of magnitude. Significant stimulation of SGLT2 activity also occurred in opossum kidney cells cotransfected with SGLT2 and MAP17. Notably, transfection with MAP17 did not change the quantity of SGLT2 protein at the cell surface in either cell type. To confirm the physiologic relevance of the MAP17-SGLT2 interaction, we studied a cohort of 60 individuals with familial renal glucosuria. One patient without any identifiable mutation in the SGLT2 coding gene (SLC5A2) displayed homozygosity for a splicing mutation (c.176+1G>A) in the MAP17 coding gene (PDZK1IP1). In the proximal tubule and in other tissues, MAP17 is known to interact with PDZK1, a scaffolding protein linked to other transporters, including Na + /H + exchanger 3, and to signaling pathways, such as the A-kinase anchor protein 2/protein kinase A pathway. Thus, these results provide the basis for a more thorough characterization of SGLT2 which would include the possible effects of its inhibition on colocalized renal transporters., (Copyright © 2016 by the American Society of Nephrology.)

Aquaporin-2 (AQP2) is a homotetrameric water channel responsible for the final water reuptake in the kidney. Mutations in the protein induce nephrogenic diabetes insipidus (NDI), which challenges the water balance by producing large urinary volumes. Although recessive AQP2 mutations are believed to generate non-functional and monomeric proteins, the literature identifies several mild mutations which suggest the existence of mixed wt/mut tetramers likely to carry function in heterozygotes. Using Xenopus oocytes, we tested this hypothesis and found that mild mutants (V24A, D150E) can associate with wt-AQP2 in mixed heteromers, providing clear functional gain in the process (62 ± 17% and 63 ± 17% increases, respectively), conversely to the strong monomeric R187C mutant which fails to associate with wt-AQP2. In kidney cells, both V24A and D150E display restored targeting while R187C remains in intracellular stores. Using a collection of mutations to expand recovery analyses, we demonstrate that inter-unit contacts are central to this recovery process. These results not only present the ground data for the functional recovery of recessive AQP2 mutants through heteromerization, which prompt to revisit the accepted NDI model, but more importantly describe a general recovery process that could impact on all multimeric systems where recessive mutations are found.

Recent advances in the adoption and use of health information technology (HIT) have had a dramatic impact on the practice of medicine. In many environments, this has led to the ability to achieve new efficiencies and levels of safety. In others, the impact has been less positive, and is associated with both: 1) workflow and user experience dissatisfaction; and 2) perceptions of missed opportunities relative to the use of computational tools to enable data-driven and precise clinical decision making. Simultaneously, the "pipeline" through which new diagnostic tools and therapeutic agents are being developed and brought to the point-of-care or population health is challenged in terms of both cost and timeliness. Given the confluence of these trends, it can be argued that now is the time to consider new ways in which HIT can be used to deliver health and wellness interventions comparable to traditional approaches (e.g., drugs, devices, diagnostics, and behavioral modifications). Doing so could serve to fulfill the promise of what has been recently promoted as "precision medicine" in a rapid and cost-effective manner. However, it will also require the health and life sciences community to embrace new modes of using HIT, wherein the use of technology becomes a primary intervention as opposed to enabler of more conventional approaches, a model that we refer to in this commentary as "interventional informatics". Such a paradigm requires attention to critical issues, including: 1) the nature of the relationships between HIT vendors and healthcare innovators; 2) the formation and function of multidisciplinary teams consisting of technologists, informaticians, and clinical or scientific subject matter experts; and 3) the optimal design and execution of clinical studies that focus on HIT as the intervention of interest. Ultimately, the goal of an "interventional informatics" approach can and should be to substantially improve human health and wellness through the use of data-driven interventions at the point of care of broader population levels. Achieving a vision of "interventional informatics" will requires us to re-think how we study HIT tools in order to generate the necessary evidence-base that can support and justify their use as a primary means of improving the human condition.

Unlabelled: Collecting data from large studies on high-throughput platforms, such as microarray or next-generation sequencing, typically requires processing samples in batches. There are often systematic but unpredictable biases from batch-to-batch, so proper randomization of biologically relevant traits across batches is crucial for distinguishing true biological differences from experimental artifacts. When a large number of traits are biologically relevant, as is common for clinical studies of patients with varying sex, age, genotype and medical background, proper randomization can be extremely difficult to prepare by hand, especially because traits may affect biological inferences, such as differential expression, in a combinatorial manner. Here we present ARTS (automated randomization of multiple traits for study design), which aids researchers in study design by automatically optimizing batch assignment for any number of samples, any number of traits and any batch size., Availability and Implementation: ARTS is implemented in Perl and is available at github.com/mmaiensc/ARTS. ARTS is also available in the Galaxy Tool Shed, and can be used at the Galaxy installation hosted by the UIC Center for Research Informatics (CRI) at galaxy.cri.uic.edu., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Advances in health information technology and biomedical informatics have laid the groundwork for significant improvements in healthcare and biomedical research. For instance, Electronic Health Records can help improve the delivery of evidence-based care, enhance quality, and contribute to discoveries and evidence generation. Despite this promise, there are many challenges to achieving the vision and missions of our healthcare and research enterprises. Given the challenges inherent in doing so, institutions are increasingly moving to establish dedicated leadership and governance models charged with designing, deploying and leveraging various information resources to advance research and advanced care activities at AHCs. Some institutions have even created a new leadership position to oversee such activities, such as the Chief Research Information Officer. This panel will include research informatics leaders discussing their experiences from the proverbial trenches as they work to operationalize such cross-mission governance models. Panelists will start by providing an overview their respective positions and environments, discuss their experiences, and share lessons learned through their work at the intersection of clinical and translational research informatics and Health IT.

Aquaporin-2 (AQP2), located at the luminal side of the collecting duct principal cells, is a water channel responsible for the final concentration of urine. Lack of function, often occurring through mistargeting of mutated proteins, induces nephrogenic diabetes insipidus (NDI), a condition characterized by large urinary volumes. In the present study, two new mutations (K228E and V24A) identified in NDI-affected individuals from distinct families along with the already reported R187C were analysed in comparison to the wild-type protein (AQP2-wt) using Xenopus laevis oocytes and a mouse collecting duct cell-line (mIMCD-3). Initial data in oocytes showed that all mutations were adequately expressed at reduced levels when compared to AQP2-wt. K228E and V24A were found to be properly targeted at the plasma membrane and exhibited adequate functionality similar to AQP2-wt, as opposed to R187C which was retained in internal stores and was thus inactive. In coexpression studies using oocytes, R187C impeded the functionality of all other AQP2 variants while combinations with K228E, V24A and AQP2-wt only showed additive functionalities. When expressed in mIMCD-3 cells, forskolin treatment efficiently promoted the targeting of AQP2-wt at the plasma membrane (>90%) while K228E only weakly responded to the same treatment (approximately 20%) and both V24A and R187C remained completely insensitive to the treatment. We concluded that both V24A and K228E are intrinsically functional water channels that lack a proper response to vasopressin, which leads to NDI as found in both compound mutations studied (K228E + R187C and V24A + R187C). The discrepancies in plasma membrane targeting response found in both expression systems stress the need to evaluate such data using mammalian cell systems.

Aquaporin-2 (AQP2) is a water channel responsible for the final water reabsorption in renal collecting ducts. Alterations in AQP2 function induce nephrogenic diabetes insipidus (NDI), a condition characterized by severe polyuria and polydipsia. Three patients affected with severe NDI, who were compound heterozygous for the AQP2 mutations D150E and G196D, are presented here along with a mildly affected D150E homozygous patient from another family. Using Xenopus oocytes as an expression system, these two mutations (G196D and D150E) were compared with the wild-type protein (AQP2-wt) for functional activity (water flux analysis), protein maturation, and plasma membrane targeting. AQP2-wt induces a major increase in water permeability (P(f) = 47.4 +/- 12.2 x 10(-4) cm/s) whereas D150E displays intermediate P(f) values (P(f) = 12.5 +/- 3.0 x 10(-4) cm/s) and G196D presents no specific water flux, similar to controls (P(f) = 2.1 +/- 0.8 x 10(-4) cm/s and 2.2 +/- 0.7 x 10(-4) cm/s, respectively). Western blot and immunocytochemical evaluations show protein targeting that parallels activity levels with AQP2-wt adequately targeted to the plasma membrane, partial targeting for D150E, and complete sequestration of G196D within intracellular compartments. When coinjecting AQP2-wt with mutants, no (AQP2-wt + D150E) or partial (AQP2-wt + G196D) reduction of water flux were observed compared with AQP2-wt alone, whereas complete loss of function was found when both mutants were coinjected. These results essentially recapitulate the clinical profiles of the family members, showing a typical dominant negative effect when G196D is coinjected with either AQP2-wt or D150E but not between AQP2-wt and D150E mutant.

Oligonucleotide microarrays have been applied to microbial surveillance and discovery where highly multiplexed assays are required to address a wide range of genetic targets. Although printing density continues to increase, the design of comprehensive microbial probe sets remains a daunting challenge, particularly in virology where rapid sequence evolution and database expansion confound static solutions. Here, we present a strategy for probe design based on protein sequences that is responsive to the unique problems posed in virus detection and discovery. The method uses the Protein Families database (Pfam) and motif finding algorithms to identify oligonucleotide probes in conserved amino acid regions and untranslated sequences. In silico testing using an experimentally derived thermodynamic model indicated near complete coverage of the viral sequence database.

To facilitate rapid, unbiased, differential diagnosis of infectious diseases, we designed GreeneChipPm, a panmicrobial microarray comprising 29,455 sixty-mer oligonucleotide probes for vertebrate viruses, bacteria, fungi, and parasites. Methods for nucleic acid preparation, random primed PCR amplification, and labeling were optimized to allow the sensitivity required for application with nucleic acid extracted from clinical materials and cultured isolates. Analysis of nasopharyngeal aspirates, blood, urine, and tissue from persons with various infectious diseases confirmed the presence of viruses and bacteria identified by other methods, and implicated Plasmodium falciparum in an unexplained fatal case of hemorrhagic feverlike disease during the Marburg hemorrhagic fever outbreak in Angola in 2004-2005.

Natural language processing (NLP) is a high throughput technology because it can process vast quantities of text within a reasonable time period. It has the potential to substantially facilitate biomedical research by extracting, linking, and organizing massive amounts of information that occur in biomedical journal articles as well as in textual fields of biological databases. Until recently, much of the work in biological NLP and text mining has revolved around recognizing the occurrence of biomolecular entities in articles, and in extracting particular relationships among the entities. Now, researchers have recognized a need to link the extracted information to ontologies or knowledge bases, which is a more difficult task. One such knowledge base is Gene Ontology annotations (GOA), which significantly increases semantic computations over the function, cellular components and processes of genes. For multicellular organisms, these annotations can be refined with phenotypic context, such as the cell type, tissue, and organ because establishing phenotypic contexts in which a gene is expressed is a crucial step for understanding the development and the molecular underpinning of the pathophysiology of diseases. In this paper, we propose a system, PhenoGO, which automatically augments annotations in GOA with additional context. PhenoGO utilizes an existing NLP system, called BioMedLEE, an existing knowledge-based phenotype organizer system (PhenOS) in conjunction with MeSH indexing and established biomedical ontologies. More specifically, PhenoGO adds phenotypic contextual information to existing associations between gene products and GO terms as specified in GOA. The system also maps the context to identifiers that are associated with different biomedical ontologies, including the UMLS, Cell Ontology, Mouse Anatomy, NCBI taxonomy, GO, and Mammalian Phenotype Ontology. In addition, PhenoGO was evaluated for coding of anatomical and cellular information and assigning the coded phenotypes to the correct GOA; results obtained show that PhenoGO has a precision of 91% and recall of 92%, demonstrating that the PhenoGO NLP system can accurately encode a large number of anatomical and cellular ontologies to GO annotations. The PhenoGO Database may be accessed at the following URL: http://www.phenoGO.org

Molecular imaging represents the intersection between imaging and genomic sciences. There has been a surge in research literature and information in both sciences. Information contained within molecular imaging literature could be used to 1) link to genomic and imaging information resources and 2) to organize and index images. This research focuses on the adaptation, evaluation, and application of BioMedLEE, a natural language processing system (NLP), in the automated extraction of information from molecular imaging abstracts.

Comparative biological studies have led to remarkable biomedical discoveries. While genomic science and technologies are advancing rapidly, our ability to precisely specify a phenotype and compare it to related phenotypes of other organisms remains challenging. This study has examined the systematic use of terminology and knowledge based technologies to enable high-throughput comparative phenomics. More specifically, we measured the accuracy of a multi-strategy automated classification method to bridge the phenotype gap between a phenotypic terminology (MGD: Phenoslim) and a broad-coverage clinical terminology (SNOMED CT). Furthermore, we qualitatively evaluate the additional emerging properties of the combined terminological network for comparative biology and discovery science. According to the gold standard (n = 100), the accuracies (precision / recall) of the composite automated methods were 67% / 97% (mapping for identical concepts) and 85% / 98% (classification). Quantitatively, only 2% of the phenotypic concepts were missing from the clinical terminology, however, qualitatively the gap was larger: conceptual scope, granularity and subtle yet significant, homonymy problems were observed. These results suggest that, as observed in other domains, additional strategies are required for combining terminologies.

Integration of various informatics terminologies will be an essential activity towards supporting the advancement of both the biomedical and clinical sciences. The GO consortium has developed an impressive collection of biomedical terms specific to genes and proteins in a variety of organisms. The UMLS is a composite collection of various medical terminologies, pioneered by the National Library of Medicine. In the present study, we examine a variety of techniques for mapping terms from one terminology (GO) to another (UMLS), and describe their respective performances for a small, curated data set attained from the National Cancer Institute, which had precision values ranging from 30% (100% recall) to 95% (74% recall). Based on each technique's performance, we comment on how each can be used to enrich an existing terminology (UMLS) in future studies and how linking biological terminologies to UMLS differs from linking medical terminologies.

With the growing use of Natural Language Processing (NLP) techniques for information extraction and concept indexing in the biomedical domain, a method that quickly and efficiently assigns the correct sense of an ambiguous biomedical term in a given context is needed concurrently. The current status of word sense disambiguation (WSD) in the biomedical domain is that handcrafted rules are used based on contextual material. The disadvantages of this approach are (i) generating WSD rules manually is a time-consuming and tedious task, (ii) maintenance of rule sets becomes increasingly difficult over time, and (iii) handcrafted rules are often incomplete and perform poorly in new domains comprised of specialized vocabularies and different genres of text. This paper presents a two-phase unsupervised method to build a WSD classifier for an ambiguous biomedical term W. The first phase automatically creates a sense-tagged corpus for W, and the second phase derives a classifier for W using the derived sense-tagged corpus as a training set. A formative experiment was performed, which demonstrated that classifiers trained on the derived sense-tagged corpora achieved an overall accuracy of about 97%, with greater than 90% accuracy for each individual ambiguous term.

This paper evaluates qualitatively the use of the MedLEE natural language processing system to code medical narratives directly into the SNOMED nomenclature, while retaining the MedLEE information model data structure. A gold standard is produced from narrative text manually coded in SNOMED. An automated parsing and SNOMED-coding of the narrative text is then automatically generated by MedLEE. By comparing MedLEE s output to that of the Gold Standard, the capacities of SNOMED and MedLEE to represent the clinical information are subsequently evaluated leading to qualitative observations on their respective strengths and constraints. In this study, MedLEE did code to SNOMED and captures the codes in a sub-structure amenable to interoperability with the description logic of SNOMED RT, showing an approach that augments and formalizes SNOMED s compositional representation methods to accurately capture information from clinical narratives.

Abbreviations are widely used in medicine. The understanding of abbreviations is important for medical language processing and information retrieval systems. The Unified Medical Language System (UMLS) contains a large number of abbreviations. We hypothesized that extracting and studying the UMLS abbreviations can be helpful for understanding the characteristics of abbreviations in medicine. In this paper, we describe a method for extracting abbreviations from the UMLS. We evaluated the method and studied the ambiguous nature of the abbreviations. In addition, the coverage of the UMLS abbreviations in medical reports was studied. Using our method, we extracted 163,666 unique (abbreviation, full form) pairs from the UMLS with a precision of 97.5%, and a recall of 96%. The UMLS abbreviations were highly ambiguous: 33.1% of abbreviations with six characters or less had multiple meanings; the average number of different full forms for all abbreviations with six characters or less was 2.28. The coverage of the UMLS abbreviations in medical reports was over 66%.

This paper addresses a process in which we combined educational guidelines (EG) from heterogeneous sources in one set of coherent computable statements to support dynamically generated and precisely tailored patient education material. The Guideline Interchange Format (GLIF), predicate logic and decision tables were assessed. An extended formalism of GLIF was applied to break up composite sentences of the educational material in atomic sentences. The differentiation of atomic sentences and combinations of atomic sentences from heterogeneous sources lead to a simplified overall content and model, and a significant reduction of conditional sentences in the EG. The resulting streamlined and personalized guidelines are expected to provide an improved user experience.

We are examining ways in which a clinical information system can favorably influence the appropriateness and rapidity of decision-making in patients suffering from symptoms of acute myocardial infarction. In order to do so, we have developed a theoretically based cognitive model for patient decision making. Our model includes somatic and emotional awareness, perceived threat (vulnerability and susceptibility), expectations of symptoms, self-efficacy and response efficacy to explain the response of an individual their symptoms. Variables are explained within a framework that details how they are interrelated in the context of other moderating variables. With an understanding of the decision process, we are able to collect, maintain and access patient specific data to tailor technology-based interventions unique to the requirements of each individual at various phases of the decision process. Existing clinical information systems at Columbia-Presbyterian Medical Center already address issues related to patient relevant on-line data. Other patient specific information will be collected through on-line questionnaires. By basing our approach on the use of a cognitive model, we can assess the capacity of our interventions to modify variables important to the decision-making process, allowing us to pinpoint which interventions are effective and the reasons why they are ineffective.

While SNOMED International has been generally accepted by the international community of pathologists, its use for primary and secondary care remains limited. This can probably be attributed to the coding complexity of clinical concepts into this multiaxial postcoordinated nomenclature. The SNOMED editors propose the use of multiple codes (aggregates) for any nuanced clinical concept, thus allowing alternative rigorous representations of the concept with SNOMED codes. Some classification critics argue whether such redundant coding precludes precise retrieval of data. This research was initiated to compare the retrieval accuracies of a relational database using a simplified model of SNOMED against a classification-based model. SNOMED-based queries showed improvement over ICPC-based queries, regardless of the use of SNOMED cross-references. The addition of the latter significantly improved the queries sensitivity and false negative rate. In conclusion, the authors recommend using aggregates of SNOMED codes in relational database designs over classification-based designs in order to improve retrieval accuracy.

The application of the TEAM--Total Evaluation and Acceptance Methodology--to the development of Project FAMUS--Family Medicine, University of Sherbrooke--is described. Project FAMUS is concerned with the establishment of a pan-Canadian risk register, the data being provided from a network of 800 family physicians distributed across Canada. Emphasis is on the first phase of the project and the overall evaluation strategy.

This paper describes the data acquisition features of the PureMD Computerized Patient Record (CPR) software designed specifically for physicians. The physician uses a stylus to point, draw and handwrite on a Dynamic Dialog Interface that provides the same flexibility as the paper record and numerous other advantages. The clinical data thus entered is highly organized, easily legible and retrievable in many ways. The underlying Medical Knowledge Base (MKB) was optimized for rapid, intuitive and consistent data entry and automatic coding with minimum handwriting.

Infusion of ketone bodies to ammonium chloride-loaded acidotic dogs was found to induce significant reduction in urinary excretion of ammonia. This effect could not be attributed to urinary pH variations. Total ammonia production by the left kidney was measured in 25 animals infused during 90 min with the sodium salt of D,L-beta-hydroxybutyric acid adjusted to pH 6.0 or 4.2. Ketonemia averaged 4.5 mM/liter. In all experiments the ammonia content of both urine and renal venous blood fell markedly so that ammoniogenesis was depressed by 60% or more within 60 min after the onset of infusion. Administration of equimolar quantities of sodium acetoacetate adjusted to pH 6.0 resulted in a 50% decrease in renal ammonia production. Infusion of ketone bodies adjusted to pH 6.0 is usually accompanied by a small increase in extracellular bicarbonate (3.7 mM/liter). However infusion of D,L-sodium lactate or sodium bicarbonate in amounts sufficient to induce a similar rise in plasma bicarbonate resulted in only a slight decrement in ammonia production (15%). The continuous infusion of 5% mannitol alone during 90-150 min failed to influence renal ammoniogenesis. Infusion of pure sodium-free beta-hydroxybutyric acid prepared by ion exchange (pH 2.2) resulted in a 50% decrease in renal ammoniogenesis in spite of the fact that both urinary pH and plasma bicarbonate fell significantly. During all experiments where ketones were infused, the renal extraction of glutamine became negligible as the renal glutamine arteriovenous difference was abolished. Renal hemodynamics did not vary significantly. Infusion of beta-hydroxybutyrate into the left renal artery resulted in a rapid decrease in ammoniogenesis by the perfused kidney. The present study indicates that ketone bodies exert their inhibitory influence within the renal tubular cell. Since their effect is independent of urinary or systemic acid-base changes, it is suggested that they depress renal ammoniogenesis by preventing the transformation of glutamine and glutamate into alpha-ketoglutarate in the mitochondria of the renal tubular cell.