Metal-Free ALS Variants of Dimeric Human Cu,Zn-Superoxide Dismutase Have Enhanced Populations of Monomeric Species

Amino acid replacements at dozens of positions in the dimeric protein human, Cu,Zn superoxide dismutase (SOD1) can cause amyotrophic lateral sclerosis (ALS). Although it has long been hypothesized that these mutations might enhance the populations of marginally-stable aggregation-prone species responsible for cellular toxicity, there has been little quantitative evidence to support this notion. Perturbations of the folding free energy landscapes of metal-free versions of five ALS-inducing variants, A4V, L38V, G93A, L106V and S134N SOD1, were determined with a global analysis of kinetic and thermodynamic folding data for dimeric and stable monomeric versions of these variants. Utilizing this global analysis approach, the perturbations on the global stability in response to mutation can be partitioned between the monomer folding and association steps, and the effects of mutation on the populations of the folded and unfolded monomeric states can be determined. The 2- to 10-fold increase in the population of the folded monomeric state for A4V, L38V and L106V and the 80- to 480-fold increase in the population of the unfolded monomeric states for all but S134N would dramatically increase their propensity for aggregation through high-order nucleation reactions. The wild-type-like populations of these states for the metal-binding region S134N variant suggest that even wild-type SOD1 may also be prone to aggregation in the absence of metals

Hepatic P450 Enzyme Activity, Tissue Morphology and Histology of Mink (Mustela vison) Exposed to Polychlorinated Dibenzofurans

Dose- and time-dependent effects of environmentally relevant concentrations of 2,3,7,8-tetrachlorodibenzo-p-dioxin equivalents (TEQ) of 2,3,7,8-tetrachlorodibenzofuran (TCDF), 2,3,4,7,8-pentachlorodibenzofuran (PeCDF), or a mixture of these two congeners on hepatic P450 enzyme activity and tissue morphology, including jaw histology, of adult ranch mink were determined under controlled conditions. Adult female ranch mink were fed either TCDF (0.98, 3.8, or 20 ng TEQTCDF/kg bw/day) or PeCDF (0.62, 2.2, or 9.5 ng TEQPeCDF/kg bw/day), or a mixture of TCDF and PeCDF (4.1 ng TEQTCDF/kg bw/day and 2.8 ng TEQPeCDF/kg bw/day, respectively) for 180 days. Doses used in this study were approximately eight times greater than those reported in a parallel field study. Activities of the cytochrome P450 1A enzymes, ethoxyresorufin O-deethylase (EROD) and methoxyresorufin O-deethylase (MROD) were significantly greater in livers of mink exposed to TCDF, PeCDF, and a mixture of the two congeners; however, there were no significant histological or morphological effects observed. It was determined that EROD and MROD activity can be used as sensitive biomarkers of exposure to PeCDF and TCDF in adult female mink; however, under the conditions of this study, the response of EROD/MROD induction occurred at doses that were less than those required to cause histological or morphological changes

Systematic screening for unsafe driving due to medical conditions: Still debatable

<p>Abstract</p> <p>Background</p> <p>Assessing people's ability to drive has become a public health concern in most industrialized countries. Although age itself is not a predictive factor of an increased risk for dangerous driving, the prevalence of medical conditions that may impair driving increases with age. Because the implementation of a screening for unsafe driving due to medical conditions is a public health issue, its usefulness should be judged using standardised criteria already proposed for screening for chronic disease. The aim of this paper is to propose standardised criteria suitable to assess the scientific validity of screening for unsafe driving due to medical conditions, and identify potential issues to be clarified before screening can be implemented and effective.</p> <p>Discussion</p> <p>Using criteria developed for screening for chronic diseases and published studies on driving with medical conditions, we specify six criteria to judge the opportunity of screening for unsafe driving due to medical conditions. This adaptation was needed because of the complexity of the natural history of medical conditions and their potential consequences on driving and road safety. We then illustrate that published studies pleading for or against screening for unsafe driving due to medical conditions fail to provide the needed documentation. Individual criteria were mentioned in 3 to 72% of 36 papers pleading for or against screening. Quantitative estimates of relevant indicators were provided in at most 42% of papers, and some data, such as the definition of an appropriate unsafe driving period were never provided.</p> <p>Summary</p> <p>The standardised framework described in this paper provides a template for assessing the effectiveness (or lack of effectiveness) of proposed measures for screening for unsafe driving due to medical conditions. Even if most criteria were mentioned in the published literature pleading for or against such a screening, the failure to find quantitative and evidence-based estimates of relevant indicators provides useful insight for further research.</p

An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

<p>Abstract</p> <p>Background</p> <p>Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.</p> <p>Method</p> <p>To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.</p> <p>Result</p> <p>We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.</p> <p>Conclusions</p> <p>We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.</p

A finer grained approach to psychological capital and work performance

Purpose Psychological capital is a set of personal resources comprised by hope, efficacy, optimism, and resilience, which previous research has supported as being valuable for general work performance. However, in today’s organizations, a multidimensional approach is required to understanding work performance, thus, we aimed to determine whether psychological capital improves proficiency, adaptivity, and proactivity, and also whether hope, efficiency, resilience, and optimism have a differential contribution to the same outcomes. Analyzing the temporal meaning of each psychological capital dimension, this paper theorizes the relative weights of psychological capital dimensions on proficiency, adaptivity, and proactivity, proposing also that higher relative weight dimensions are helpful to cope with job demands and perform well. Methodology Two survey studies, the first based on cross-sectional data and the second on two waves of data, were conducted with employees from diverse organizations, who provided measures of their psychological capital, work performance, and job demands. Data was modeled with regression analysis together with relative weights analysis. Findings Relative weights for dimensions of psychological capital were supported as having remarkable unique contributions for proficient, adaptive, and proactive behavior, particularly when job demands were high. Originality/Value We concluded that organizations facing high job demands should implement actions to enhance psychological capital dimensions; however, those actions should focus on the specific criterion of performance of interest

Identification of patients at risk for early death after conventional chemotherapy in solid tumours and lymphomas

1–5% of cancer patients treated with cytotoxic chemotherapy die within a month after the administration of chemotherapy. Risk factors for these early deaths (ED) are not well known. The purpose of this study was to establish a risk model for ED after chemotherapy applicable to all tumour types. The model was delineated in a series of 1051 cancer patients receiving a first course of chemotherapy in the Department of Medicine of the Centre Léon Bérard (CLB) in 1996 (CLB-1996 cohort), and then validated in a series of patients treated in the same department in 1997 (CLB-1997), in a prospective cohort of patients with aggressive non-Hodgkin's lymphoma (NHL) (CLB-NHL), and in a prospective cohort of patients with metastatic breast cancer (MBC series) receiving first-line chemotherapy. In the CLB-1996 series, 43 patients (4.1%) experienced early. In univariate analysis, age > 60, PS > 1, lymphocyte (ly) count ≤ 700 μl−1 immediately prior to chemotherapy (d1), d1-platelet count ≤ 150 Gl−1, and the type of chemotherapy were significantly correlated to the risk of early death (P ≤ 0.01). Using logistic regression, PS > 1 (hazard ratio 3.9 (95% Cl 2.0–7.5)) and d1-ly count ≤ 700 μl−1 (3.1 (95% Cl 1.6–5.8)) were identified as independent risk factors for ED. The calculated probability of ED was 20% (95% Cl 10–31) in patients with both risk factors, 6% (95% Cl 4–9) for patients with only 1 risk factor, and 1.7% (95% Cl 0.9–3) for patients with none of these 2 risk factors. In the CLB-97, CLB-NHL and MBC validation series, the observed incidences of early death in patients with both risk factors were 19%, 25% and 40% respectively and did not differ significantly from those calculated in the model. In conclusion, poor performance status and lymphopenia identify a subgroup of patients at high risk for early death after chemotherapy. © 2001 Cancer Research Campaignhttp://www.bjcancer.co

Translating microarray data for diagnostic testing in childhood leukaemia

BACKGROUND: Recent findings from microarray studies have raised the prospect of a standardized diagnostic gene expression platform to enhance accurate diagnosis and risk stratification in paediatric acute lymphoblastic leukaemia (ALL). However, the robustness as well as the format for such a diagnostic test remains to be determined. As a step towards clinical application of these findings, we have systematically analyzed a published ALL microarray data set using Robust Multi-array Analysis (RMA) and Random Forest (RF). METHODS: We examined published microarray data from 104 ALL patients specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent patient cohort. RESULTS: We achieved very high overall ALL subgroup prediction accuracies of about 98%, and were able to verify the robustness of these genes in an independent panel of 68 specimens obtained from a different institution and processed in a different laboratory. Our study established that the selection of discriminating genes is strongly dependent on the analysis method. This may have profound implications for clinical use, particularly when the classifier is reduced to a small set of genes. We have demonstrated that as few as 26 genes yield accurate class prediction and importantly, almost 70% of these genes have not been previously identified as essential for class distinction of the six ALL subgroups. CONCLUSION: Our finding supports the feasibility of qRT-PCR technology for standardized diagnostic testing in paediatric ALL and should, in conjunction with conventional cytogenetics lead to a more accurate classification of the disease. In addition, we have demonstrated that microarray findings from one study can be confirmed in an independent study, using an entirely independent patient cohort and with microarray experiments being performed by a different research team