Mental health and quality of life after genetic testing for Huntington disease: a long-term effect study in Germany

Predictive genetic testing for Huntington disease (HD) might cause severe short-term psychological reactions in patients with poor mental health. Very few studies exist on the long-term effects of genetic HD testing. The aim of this study was to assess mental health and quality of life in persons who were tested for HD mutation, to compare mental health depending on the result of the genetic test (non-carriers, gene carriers, and patients with HD) and to identify predictors of mental health and quality of life via linear regression. The data were collected by self-report questionnaires. In total, 121 individuals participated in this study: 52 were non-carriers, 54 were gene carriers, and 15 were gene carriers suffering from HD. Non-carriers and gene carriers showed better mental health and quality of life than HD-patients but did not differ from each other. In non-carriers four variables predicted increased depression and low mental quality of life: low perceived social support, no intimate relationship, female sex and younger age. For gene carriers three predictors were found: low perceived social support, the expectation of an unfavorable genetic test result before the testing procedure and being childless. To prevent detrimental effects of HD testing on mental health and mental quality of life, specific attention should be paid to persons with limited social networks during genetic counseling. Assessment of expectations related to the test result and mental health prior to a genetic testing procedure may help to identify gene carriers at risk of poor coping after an unfavorable test result.

Predictive genetic test decisions for Huntington's disease: context, appraisal and new moral imperatives

Predictive testing is one of the new genetic technologies which, in conjunction with developing fields such as pharmacogenomics, promises many benefits for preventive and population health. Understanding how individuals appraise and make genetic test decisions is increasingly relevant as the technology expands. Lay understandings of genetic risk and test decision-making, located within holistic life frameworks including family or kin relationships, may vary considerably from clinical representations of these phenomena. The predictive test for Huntington's disease (HD), whilst specific to a single-gene, serious, mature-onset but currently untreatable disorder, is regarded as a model in this context. This paper reports upon a qualitative Australian study which investigated predictive test decision-making by individuals at risk for HD, the contexts of their decisions and the appraisals which underpinned them. In-depth interviews were conducted in Australia with 16 individuals at 50% risk for HD, with variation across testing decisions, gender, age and selected characteristics. Findings suggested predictive testing was regarded as a significant life decision with important implications for self and others, while the right not to know genetic status was staunchly and unanimously defended. Multiple contexts of reference were identified within which test decisions were located, including intra- and inter-personal frameworks, family history and experience of HID, and temporality. Participants used two main criteria in appraising test options: perceived value of, or need for the test information, for self and/or significant others, and degree to which such information could be tolerated and managed, short and long-term, by self and/or others. Selected moral and ethical considerations involved in decision-making are examined, as well as the clinical and socio-political contexts in which predictive testing is located. The paper argues that psychosocial vulnerabilities generated by the availability of testing technologies and exacerbated by policy imperatives towards individual responsibility and self-governance should be addressed at broader societal levels. (C) 2003 Elsevier Science Ltd. All rights reserved.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.

Predictive genetic test decisions for Huntington's disease: Elucidating the test/no-test dichotomy

Predictive genetic testing for serious, mature-onset genetic illness represents a unique context in health decision making. This article presents findings from an exploratory qualitative Australian-based study into the decision making of individuals at risk for Huntington's disease (HD) with regard to predictive genetic testing. Sixteen in-depth interviews were conducted with a range of at-risk individuals. Data analysis revealed four discrete decision-making positions rather than a 'to test' or not to test' dichotomy. A conceptual dimension of (non-)openness and (non-)engagement characterized the various decisions. Processes of decision making and a concept of 'test readiness' were identified. Findings from this research, while not generalizable, are discussed in relation to theoretical frameworks and stage models of health decision making, as well as possible clinical implications.

Desenhos de estruturas químicas correlacionam-se com propriedades biológicas: MIA-QSAR

Descriptors in multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) are pixels of bidimensional images of chemical structures (drawings), which were used to model the trichomonicidal activities of a series of benzimidazole derivatives. The MIA-QSAR model showed good predictive ability, with r², q² and r val. ext.² of 0.853, 0.519 and 0.778, respectively, which are comparable to the best values obtained by CoMFA e CoMSIA for the same series. A MIA-based analysis was also performed by using images of alphabetic letters with the corresponding numeric ordering as dependent variables, but no correlation was found, supporting that MIA-QSAR is not arbitrary.

Qualidade de vida e avaliação fisica em intoxicados por mercurio : estudo observacional transversal descritivo

Universidade Estadual de Campinas . Faculdade de Educação Física

Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by 'orange rust' ( Puccinia kuehnii ) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660 nm) yielded increased separability of rust-affected areas. The newly formulated 'Disease-Water Stress Indices' (DWSI-1=R800/R1660; DSWI-2=R1660/R550; DWSI-5=(R800+R550)/(R1660+R680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.

Discontinuation of continuous renal replacement therapy: A post hoc analysis of a prospective multicenter observational study

Objectives: To describe current practice for the discontinuation of continuous renal replacement therapy in a multinational setting and to identify variables associated with successful discontinuation. The approach to discontinue continuous renal replacement therapy may affect patient outcomes. However, there is lack of information on how and under what conditions continuous renal replacement therapy is discontinued. Design: Post hoc analysis of a prospective observational study. Setting. Fifty-four intensive care units in 23 countries. Patients: Five hundred twenty-nine patients (52.6%) who survived initial therapy among 1006 patients treated with continuous renal replacement therapy. Interventions: None. Measurements and Main Results., Three hundred thirteen patients were removed successfully from continuous renal replacement therapy and did not require any renal replacement therapy for at least 7 days and were classified as the ""success"" group and the rest (216 patients) were classified as the ""repeat-RRT"" (renal replacement therapy) group. Patients in the ""success"" group had lower hospital mortality (28.5% vs. 42.7%, p < .0001) compared with patients in the ""repeat-RRT"" group. They also had lower creatinine and urea concentrations and a higher urine output at the time of stopping continuous renal replacement therapy. Multivariate logistic regression analysis for successful discontinuation of continuous renal replacement therapy identified urine output (during the 24 hrs before stopping continuous renal replacement therapy: odds ratio, 1.078 per 100 mL/day increase) and creatinine (odds ratio, 0.996 per mu mol/L increase) as significant predictors of successful cessation. The area under the receiver operating characteristic curve to predict successful discontinuation of continuous renal replacement therapy was 0.808 for urine output and 0.635 for creatinine. The predictive ability of urine output was negatively affected by the use of diuretics (area under the receiver operating characteristic curve, 0.671 with diuretics and 0.845 without diuretics). Conclusions. We report on the current practice of discontinuing continuous renal replacement therapy in a multinational setting. Urine output at the time of initial cessation (if continuous renal replacement therapy was the most important predictor of successful discontinuation, especially if occurring without the administration of diuretics. (Crit Care Med 2009; 37:2576-2582)

A Palliative Prognostic Score for Terminally Ill Children and Adolescents With Cancer

Background. The loss of a child is considered the hardest moment in a parent`s life. Studies addressing length of survival under pediatric palliative care are rare. The aim of this study was to improve a survival prediction model for children in palliative care, as accurate information positively impacts parent and child preparation for palliative care. Procedure. Sixty-five children referred to a pediatric palliative care team were followed from August 2003 until December 2006. Variables investigated (also included in previous studies) were: diagnosis, home care provider, presence of anemia, and performance status score given by the home care provider. Clinical variables such as symptom number were also used to test the score`s ability to pre-validated using the above variables. The number of symptoms at transition to palliative care does not improve the score`s predictive ability. The sum of the single scores gives an overall score for each patient, dividing the population into three groups by probability of 60-day survival: Group A 80.0%, Group B 38.0%, and Group C 28.5% (P < 0.001). Conclusion. A pediatric palliative care score based on easily accessible variables is statistically significant in multivariate analysis. Factors that increase accuracy of life expectancy prediction enable adequate information to be given to patients and families, contributing to therapeutic decision-making issues. Pediatr Blood Cancer. 2010;55:1167-1171. (C) 2010 Wiley-Liss, Inc.

Simplified criteria for the diagnosis of autoimmune hepatitis

Diagnosis of autoimmune hepatitis (AIH) may be challenging. However, early diagnosis is important because immunosuppression is life-saving. Diagnostic criteria of the International Autoimmune Hepatitis Group (IAIHG) were complex and purely meant for scientific purposes. This study of the IAIHG aims to define simplified diagnostic criteria for routine clinical practice. Candidate criteria included sex, age, autoantibodies, immunoglobutins, absence of viral hepatitis, and histology. The training set included 250 AIH patients and 193 controls from 11 centers worldwide. Scores were built from variables showing predictive ability in univariate analysis. Diagnostic value of each score was assessed by the area under the receiver operating characteristic (ROC) curve. The best score was validated using data of an additional 109 AIH patients and 284 controls. This score included autoantibodies, immunoglobulin G, histology, and exclusion of viral hepatitis. The area under the curve for prediction of AIH was 0.946 in the training set and 0.91 in the validation set. Based on the ROC curves, two cutoff points were chosen. The score was found to have 88% sensitivity and 97% specificity (cutoff >= 6) and 81% sensitivity and 99% specificity (cutoff 2:7) in the validation set. Conclusion: A reliable diagnosis of AIH can be made using a very simple diagnostic score. We propose the diagnosis of probable AIH at a cutoff point greater than 6 points and definite AIH 7 points or higher.

Modified logit life table system: Principles, empirical validation, and application

Despite its widespread use, the Coale-Demeny model life table system does not capture the extensive variation in age-specific mortality patterns observed in contemporary populations, particularly those of the countries of Eastern Europe and populations affected by HIV/AIDS. Although relational mortality models, such as the Brass logit system, can identify these variations, these models show systematic bias in their predictive ability as mortality levels depart from the standard. We propose a modification of the two-parameter Brass relational model. The modified model incorporates two additional age-specific correction factors (gamma(x), and theta(x)) based on mortality levels among children and adults, relative to the standard. Tests of predictive validity show deviations in age-specific mortality rates predicted by the proposed system to be 30-50 per cent lower than those predicted by the Coale-Demeny system and 15-40 per cent lower than those predicted using the original Brass system. The modified logit system is a two-parameter system, parameterized using values of l(5) and l(60).

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Enthalpies of Solution of 1-Butyl-3-methylimidazolium Tetrafluoroborate in 15 Solvents at 298.15 K

Enthalpies of solution of 1-butyl-3-methylimidazolium tetra fluoroborate, [BMIm]BF4, are reported at 298.15 K in a set of 15 hydrogen bond donor and hydrogen bond acceptor solvents, chosen by their diversity, namely, water, methanol, ethanol, 1,2-ethanediol, 2-choroethanol, 2-methoxyethanol, formamide, propylene carbonate, nitromethane, acetonitrile, dimethyl sulfoxide, acetone, N,N-dimethylformamide, N,N-dimethylacetamide, and aniline. These values are shown to be largely independent of [BMIm]BF4 concentration. The obtained enthalpies of solution vary from very endothermic to quite exothermic, thus showing a very high sensitivity of the enthalpies of solution of [BMIm]BF4 to solvent properties. Solvent effects on the solution process of this IL are analyzed by a quantitative structure-property relationship methodology, using the TAKA equation and a modified equation, which significantly improves the model's predictive ability. The observed differences in the enthalpies of solution are rationalized in terms of the solvent properties found to be relevant, that is, pi* and E-T(N).

Design, synthesis and biologival evaluation of novel isoniazid derivatives with potente antitubercular activity

The disturbing emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb) has been driving the scientific community to urgently search for new and efficient antitubercular drugs. Despite the various drugs currently under evaluation, isoniazid is still the key and most effective component in all multi-therapeutic regimens recommended by the WHO. This paper describes the QSAR-oriented design, synthesis and in vitro antitubercular activity of several potent isoniazid derivatives (isonicotinoyl hydrazones and isonicotinoyl hydrazides) against H37Rv and two resistant Mtb strains. QSAR studies entailed RFs and ASNNs classification models, as well as MLR models. Strict validation procedures were used to guarantee the models' robustness and predictive ability. Lipophilicity was shown not to be relevant to explain the activity of these derivatives, whereas shorter N-N distances and lengthy substituents lead to more active compounds. Compounds I, 2, 4, 5 and 6, showed measured activities against H37Rv higher than INH (i.e., MIC <= 0.28 mu M), while compound 9 exhibited a six fold decrease in MIC against the katG (S315T) mutated strain, by comparison with INH (Le., 6.9 vs. 43.8 mu M). All compounds were ineffective against H37Rv(INH) (Delta katG), a strain with a full deletion of the katG gene, thus corroborating the importance of KatG in the activation of INH-based compounds. The most potent compounds were also shown not to be cytotoxic up to a concentration 500 times higher than MIC. (C) 2014 Elsevier Masson SAS. All rights reserved.