Medicine, Dentistry & Health Sciences Collected Works - Research Publications
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ItemMicrocytosis and possible early iron deficiency in paediatric inpatients: a retrospective audit.(Springer Science and Business Media LLC, 2009-05-29)BACKGROUND: Iron deficiency anaemia is a common paediatric problem worldwide, with significant neurodevelopmental morbidity if left untreated. A decrease in the mean corpuscular volume (MCV) can be used as a surrogate marker for detecting early iron deficiency prior to definitive investigation and treatment. An audit cycle was therefore undertaken to evaluate and improve the identification, follow-up and treatment of abnormally low MCV results amongst the paediatric inpatients in an English district general hospital. METHODS: The audit cycle was performed retrospectively over two three-month periods (February to April 2006; September to November 2006), amongst patients aged between one month and 16 years that had full blood counts performed whilst admitted on the paediatric ward. Patients with at least one abnormally low MCV result were identified, and their notes reviewed. We looked for any underlying explanation for the result, adequate documentation of the result as abnormal, and instigation of follow-up or treatment. In-between the two audit periods, the results of the first audit period were presented to the medical staff and suggestions were made for improvements in documentation and follow-up of abnormal results. The z-test was used to test for equality of proportions between the two audit samples. RESULTS: Out of 701 inpatients across both audit periods that had full blood counts, 61 (8.7%) had a low MCV result. Only 15% of patients in each audit period had an identifiable explanation for their low MCV values. Amongst the remaining 85% with either potentially explicable or inexplicable results, there was a significant increase in documentation of results as abnormal from 25% to 91% of cases between the first and second audit periods (p = 0.00 using z-test). However, there was no accompanying increase in the proportion of patients who received follow-up or treatment for their abnormal results. CONCLUSION: Abnormal red cell indices that may indicate iron deficiency are frequently missed amongst paediatric inpatients. Medical staff education and the use of appropriate protocols or pathways could further improve detection and treatment rates in this setting.
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ItemMucin 1 (MUC1) is a novel partner for MAL2 in breast carcinoma cells(BMC, 2009-01-28)BACKGROUND: The MAL2 gene, encoding a four-transmembrane protein of the MAL family, is amplified and overexpressed in breast and other cancers, yet the significance of this is unknown. MAL-like proteins have trafficking functions, but their molecular roles are largely obscure, partly due to a lack of known binding partners. METHODS: Yeast two-hybrid screening of a breast carcinoma cDNA expression library was performed using a full-length MAL2 bait, and subsequent deletion mapping experiments were performed. MAL2 interactions were confirmed by co-immunoprecipitation analyses and confocal microscopy was employed to compare protein sub-cellular distributions. Sucrose density gradient centrifugation of membranes extracted in cold Triton X-100 was employed to compare protein distributions between Triton X-100-soluble and -insoluble fractions. RESULTS: The tumor-associated protein mucin 1 (MUC1) was identified as a potential MAL2 partner, with MAL2/MUC1 interactions being confirmed in myc-tagged MAL2-expressing MCF-10A cells using co-immunoprecipitation assays. Deletion mapping experiments demonstrated a requirement for the first MAL2 transmembrane domain for MUC1 binding, whereas the MAL2 N-terminal domain was required to bind D52-like proteins. Confocal microscopy identified cytoplasmic co-localisation of MUC1 and MAL2 in breast cell lines, and centrifugation of cell lysates to equilibrium in sucrose density gradients demonstrated that MAL2 and MUC1 proteins were co-distributed between Triton X-100-soluble and -insoluble fractions. However co-immunoprecipitation analyses detected MAL2/MUC1 interactions in Triton X-100-soluble fractions only. Myc-MAL2 expression in MCF-10A cells was associated with both increased MUC1 detection within Triton X-100-soluble and -insoluble fractions, and increased MUC1 detection at the cell surface. CONCLUSION: These results identify MUC1 as a novel MAL2 partner, and suggest a role for MAL2 in regulating MUC1 expression and/or localisation.
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ItemNo Preview AvailableAnaesthesia in septic patients: good preparation and making the right choice?(BIOMED CENTRAL LTD, 2009)Septic patients may require anaesthesia for surgery or to facilitate endotracheal intubation for respiratory failure. These patients frequently start with a deranged haemodynamic state, including vasodilation with hypotension, and cardiomyopathy, making induction of anaesthesia a potentially hazardous task. Anaesthetic agents are well known to decrease contractility and to cause vasodilation - in part from direct effect of the drugs, and in part due to the state of anaesthesia, that causes reduced sympathetic tone. Before induction, the physician should understand the haemodynamic state (especially using echocardiography), should restore cardiovascular reserve with inotropes and vasopressors, and should induce anaesthesia with the smallest dose of the safest drug. In the previous issue of Critical Care, Zausig and colleagues show that propofol may not be the safest choice of induction agent in septic patients.
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ItemUsefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach.(Springer Science and Business Media LLC, 2009-09-03)BACKGROUND: Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. RESULTS: Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. CONCLUSION: Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.
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ItemProdepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only(PUBLIC LIBRARY SCIENCE, 2009-09-17)Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis.
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ItemDeveloping a manually annotated clinical document corpus to identify phenotypic information for inflammatory bowel disease(BIOMED CENTRAL LTD, 2009)BACKGROUND: Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers. METHODS: Using a set of clinical documents from the VA EMR for a particular use case of interest we identify specific challenges and present several opportunities for annotation tasks. We demonstrate specific methods using an open source annotation tool, a customized annotation schema, and a corpus of clinical documents for patients known to have a diagnosis of Inflammatory Bowel Disease (IBD). We report clinician annotator agreement at the document, concept, and concept attribute level. We estimate concept yield in terms of annotated concepts within specific note sections and document types. RESULTS: Annotator agreement at the document level for documents that contained concepts of interest for IBD using estimated Kappa statistic (95% CI) was very high at 0.87 (0.82, 0.93). At the concept level, F-measure ranged from 0.61 to 0.83. However, agreement varied greatly at the specific concept attribute level. For this particular use case (IBD), clinical documents producing the highest concept yield per document included GI clinic notes and primary care notes. Within the various types of notes, the highest concept yield was in sections representing patient assessment and history of presenting illness. Ancillary service documents and family history and plan note sections produced the lowest concept yield. CONCLUSION: Challenges include defining and building appropriate annotation schemas, adequately training clinician annotators, and determining the appropriate level of information to be annotated. Opportunities include narrowing the focus of information extraction to use case specific note types and sections, especially in cases where NLP systems will be used to extract information from large repositories of electronic clinical note documents.
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ItemDevelopment of a theory of implementation and integration: Normalization Process Theory(BMC, 2009-05-21)BACKGROUND: Theories are important tools in the social and natural sciences. The methods by which they are derived are rarely described and discussed. Normalization Process Theory explains how new technologies, ways of acting, and ways of working become routinely embedded in everyday practice, and has applications in the study of implementation processes. This paper describes the process by which it was built. METHODS: Between 1998 and 2008, we developed a theory. We derived a set of empirical generalizations from analysis of data collected in qualitative studies of healthcare work and organization. We developed an applied theoretical model through analysis of empirical generalizations. Finally, we built a formal theory through a process of extension and implication analysis of the applied theoretical model. RESULTS: Each phase of theory development showed that the constructs of the theory did not conflict with each other, had explanatory power, and possessed sufficient robustness for formal testing. As the theory developed, its scope expanded from a set of observed regularities in data with procedural explanations, to an applied theoretical model, to a formal middle-range theory. CONCLUSION: Normalization Process Theory has been developed through procedures that were properly sceptical and critical, and which were opened to review at each stage of development. The theory has been shown to merit formal testing.
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ItemOptimal Dosing and Dynamic Distribution of Vaccines in an Influenza Pandemic(OXFORD UNIV PRESS INC, 2009-06-15)Limited production capacity and delays inherent in vaccine development are major hurdles to the widespread use of vaccines to mitigate the effects of a new influenza pandemic. Antigen-sparing vaccines have the most potential to increase population coverage but may be less efficacious. The authors explored this trade-off by applying simple models of influenza transmission and dose response to recent clinical trial data. In this paper, these data are used to illustrate an approach to comparing vaccines on the basis of antigen supply and inferred efficacy. The effects of delays in matched vaccine availability and seroconversion on epidemic size during pandemic phase 6 were also studied. The authors infer from trial data that population benefits stem from the use of low-antigen vaccines. Delayed availability of a matched vaccine could be partially alleviated by using a 1-dose vaccination program with increased coverage and reduced time to full protection. Although less immunogenic, an overall attack rate of up to 6% lower than a 2-dose program could be achieved. However, if prevalence at vaccination is above 1%, effectiveness is much reduced, emphasizing the need for other control measures.
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ItemGenome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20(NATURE PUBLISHING GROUP, 2009-07)To identify multiple sclerosis (MS) susceptibility loci, we conducted a genome-wide association study (GWAS) in 1,618 cases and used shared data for 3,413 controls. We performed replication in an independent set of 2,256 cases and 2,310 controls, for a total of 3,874 cases and 5,723 controls. We identified risk-associated SNPs on chromosome 12q13-14 (rs703842, P = 5.4 x 10(-11); rs10876994, P = 2.7 x 10(-10); rs12368653, P = 1.0 x 10(-7)) and upstream of CD40 on chromosome 20q13 (rs6074022, P = 1.3 x 10(-7); rs1569723, P = 2.9 x 10(-7)). Both loci are also associated with other autoimmune diseases. We also replicated several known MS associations (HLA-DR15, P = 7.0 x 10(-184); CD58, P = 9.6 x 10(-8); EVI5-RPL5, P = 2.5 x 10(-6); IL2RA, P = 7.4 x 10(-6); CLEC16A, P = 1.1 x 10(-4); IL7R, P = 1.3 x 10(-3); TYK2, P = 3.5 x 10(-3)) and observed a statistical interaction between SNPs in EVI5-RPL5 and HLA-DR15 (P = 0.001).
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ItemTranscript length bias in RNA-seq data confounds systems biology(BMC, 2009-04-16)BACKGROUND: Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data. RESULTS: We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript. CONCLUSION: Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses. REVIEWERS: This article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit).