Abdominal Wound Dehiscence in Adults: Development and Validation of a Risk Model

Background: Several studies have been performed to identify risk factors for abdominal wound dehiscence. No risk model had yet been developed for the general surgical population. The objective of the present study was to identify independent risk factors for abdominal wound dehiscence and to develop a risk model to recognize high-risk patients. Identification of high-risk patients offers opportunities for intervention strategies. Methods: Medical registers from January 1985 to December 2005 were searched. Patients who had primarily undergone appendectomies or nonsurgical (e.g., urological) operations were excluded. Each patient with abdominal wound dehiscence was matched with three controls by systematic random sampling. Putative relevant patient-related, operation-related, and postoperative variables were evaluated in univariate analysis and subsequently entered in multivariate stepwise logistic regression models to delineate major independent predictors of abdominal wound dehiscence. A risk model was developed, which was validated in a population of patients who had undergone operation between January and December 2006. Results: A total of 363 cases and 1,089 controls were analyzed. Major independent risk factors were age, gender, chronic pulmonary disease, ascites, jaundice, anemia, emergency surgery, type of surgery, postoperative coughing, and wound infection. In the validation population, risk scores were significantly higher (P < 0.001) for patients with abdominal wound dehiscence (n = 19) compared to those without (n = 677). Resulting scores ranged from 0 to 8.5, and the risk for abdominal wound dehiscence over this range increased exponentially from 0.02% to 70.1%. Conclusions: The validated risk model shows high predictive value for abdominal wound dehiscence and may help to identify patients at increased risk

The CRE1 carbon catabolite repressor of the fungus Trichoderma reesei: a master regulator of carbon assimilation

<p>Abstract</p> <p>Background</p> <p>The identification and characterization of the transcriptional regulatory networks governing the physiology and adaptation of microbial cells is a key step in understanding their behaviour. One such wide-domain regulatory circuit, essential to all cells, is carbon catabolite repression (CCR): it allows the cell to prefer some carbon sources, whose assimilation is of high nutritional value, over less profitable ones. In lower multicellular fungi, the C2H2 zinc finger CreA/CRE1 protein has been shown to act as the transcriptional repressor in this process. However, the complete list of its gene targets is not known.</p> <p>Results</p> <p>Here, we deciphered the CRE1 regulatory range in the model cellulose and hemicellulose-degrading fungus <it>Trichoderma reesei </it>(anamorph of <it>Hypocrea jecorina</it>) by profiling transcription in a wild-type and a delta-<it>cre1 </it>mutant strain on glucose at constant growth rates known to repress and de-repress CCR-affected genes. Analysis of genome-wide microarrays reveals 2.8% of transcripts whose expression was regulated in at least one of the four experimental conditions: 47.3% of which were repressed by CRE1, whereas 29.0% were actually induced by CRE1, and 17.2% only affected by the growth rate but CRE1 independent. Among CRE1 repressed transcripts, genes encoding unknown proteins and transport proteins were overrepresented. In addition, we found CRE1-repression of nitrogenous substances uptake, components of chromatin remodeling and the transcriptional mediator complex, as well as developmental processes.</p> <p>Conclusions</p> <p>Our study provides the first global insight into the molecular physiological response of a multicellular fungus to carbon catabolite regulation and identifies several not yet known targets in a growth-controlled environment.</p

Machine learning for regulatory analysis and transcription factor target prediction in yeast

High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps—the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data

A computational procedure for functional characterization of potential marker genes from molecular data: Alzheimer's as a case study

Abstract Background A molecular characterization of Alzheimer's Disease (AD) is the key to the identification of altered gene sets that lead to AD progression. We rely on the assumption that candidate marker genes for a given disease belong to specific pathogenic pathways, and we aim at unveiling those pathways stable across tissues, treatments and measurement systems. In this context, we analyzed three heterogeneous datasets, two microarray gene expression sets and one protein abundance set, applying a recently proposed feature selection method based on regularization. Results For each dataset we identified a signature that was successively evaluated both from the computational and functional characterization viewpoints, estimating the classification error and retrieving the most relevant biological knowledge from different repositories. Each signature includes genes already known to be related to AD and genes that are likely to be involved in the pathogenesis or in the disease progression. The integrated analysis revealed a meaningful overlap at the functional level. Conclusions The identification of three gene signatures showing a relevant overlap of pathways and ontologies, increases the likelihood of finding potential marker genes for AD.</p

Role of zinc and α2macroglobulin on thymic endocrine activity and on peripheral immune efficiency (natural killer activity and interleukin 2) in cervical carcinoma

Decreased natural killer (NK) activity as well as interleukin 2 (IL-2) are risk factors for the progression of cervical carcinoma. NK activity and IL-2 may be thymus controlled. Plasma levels of active thymulin, a zinc-dependent thymic hormone (ZnFTS), are reduced in cancer because of the low peripheral zinc bioavailability. Zinc and thymulin are relevant for normal immune functions. α2-Macroglobulin is an inhibitor of matrix metalloproteases (MMPs) against invasive tumour proliferation. Because α2-macroglobulin has a binding affinity (Kd) for zinc that is higher than does thymulin, it may play a key role in immune efficiency in cancer. Plasma samples of 22 patients (age range 35–60 years) with locally advanced squamous cervical carcinoma and with FIGO stage Ib2–IIb were examined. They showed reduced active thymulin, decreased NK activity and IL-2 production, increased soluble IL-2 receptor (sIL-2R) and augmented α2-macroglobulin in the circulation, whereas plasma zinc levels were within the normal range for age. Significant positive correlations were found between zinc or active thymulin and α2-macroglobulin (r = 0.75, P< 0.01, r = 0.78, P< 0.01, respectively) in cancer patients. In vitro zinc increases IL-2 production from peripheral blood mononuclear cells (PBMCs) of cancer patients. These data suggest that an increase in α2-macroglobulin, which competes with thymulin for zinc binding, may be involved in causing a thymulin deficit with a consequent decrease of IL-2 and NK cytotoxicity. Thus, physiological zinc treatment in cervical carcinoma maybe restores impaired central and peripheral immune efficiency. © 1999 Cancer Research Campaig

Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags

Next-generation sequencing technology provides novel opportunities for gathering genome-scale sequence data in natural populations, laying the empirical foundation for the evolving field of population genomics. Here we conducted a genome scan of nucleotide diversity and differentiation in natural populations of threespine stickleback (Gasterosteus aculeatus). We used Illumina-sequenced RAD tags to identify and type over 45,000 single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations. Overall estimates of genetic diversity and differentiation among populations confirm the biogeographic hypothesis that large panmictic oceanic populations have repeatedly given rise to phenotypically divergent freshwater populations. Genomic regions exhibiting signatures of both balancing and divergent selection were remarkably consistent across multiple, independently derived populations, indicating that replicate parallel phenotypic evolution in stickleback may be occurring through extensive, parallel genetic evolution at a genome-wide scale. Some of these genomic regions co-localize with previously identified QTL for stickleback phenotypic variation identified using laboratory mapping crosses. In addition, we have identified several novel regions showing parallel differentiation across independent populations. Annotation of these regions revealed numerous genes that are candidates for stickleback phenotypic evolution and will form the basis of future genetic analyses in this and other organisms. This study represents the first high-density SNP–based genome scan of genetic diversity and differentiation for populations of threespine stickleback in the wild. These data illustrate the complementary nature of laboratory crosses and population genomic scans by confirming the adaptive significance of previously identified genomic regions, elucidating the particular evolutionary and demographic history of such regions in natural populations, and identifying new genomic regions and candidate genes of evolutionary significance