31,750 research outputs found

    Development and preliminary testing of the psychosocial adjustment to hereditary diseases scale

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    Background: The presence of Lynch syndrome (LS) can bring a lifetime of uncertainty to an entire family as members adjust to living with a high lifetime cancer risk. The research base on how individuals and families adjust to genetic-linked diseases following predictive genetic testing has increased our understanding of short-term impacts but gaps continue to exist in knowledge of important factors that facilitate or impede long-term adjustment. The failure of existing scales to detect psychosocial adjustment challenges in this population has led researchers to question the adequate sensitivity of these instruments. Furthermore, we have limited insight into the role of the family in promoting adjustment. Methods: The purpose of this study was to develop and initially validate the Psychosocial Adjustment to Hereditary Diseases (PAHD) scale. This scale consists of two subscales, the Burden of Knowing (BK) and Family Connectedness (FC). Items for the two subscales were generated from a qualitative data base and tested in a sample of 243 participants from families with LS. Results: The Multitrait/Multi-Item Analysis Program-Revised (MAP-R) was used to evaluate the psychometric properties of the PAHD. The findings support the convergent and discriminant validity of the subscales. Construct validity was confirmed by factor analysis and Cronbach’s alpha supported a strong internal consistency for BK (0.83) and FC (0.84). Conclusion: Preliminary testing suggests that the PAHD is a psychometrically sound scale capable of assessing psychosocial adjustment. We conclude that the PAHD may be a valuable monitoring tool to identify individuals and families who may require therapeutic interventions

    Identification of delivery models for the provision of predictive genetic testing in Europe: protocol for a multicentre qualitative study and a systematic review of the literature

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    Introduction: The appropriate application of genomic technologies in healthcare is surrounded by many concerns. In particular, there is a lack of evidence on what constitutes an optimal genetic service delivery model, which depends on the type of genetic test and healthcare context considered. The present project aims to identify, classify, and evaluate delivery models for the provision of predictive genetic testing in Europe and in selected Anglophone extra-European countries (the USA, Canada, Australia, and New Zealand). It also sets out to survey the European public health community’s readiness to incorporate public health genomics into their practice. Materials and equipment: The project consists of (i) a systematic review of published literature and selected country websites, (ii) structured interviews with health experts on the genetic service delivery models in their respective countries, and (iii) a survey of European Public Health Association (EUPHA) members’ knowledge and attitudes toward genomics applications in clinical practice. The inclusion criteria for the systematic review are that articles be published in the period 2000–2015; be in English or Italian; and be from European countries or from Canada, the USA, Australia, or New Zealand. Additional policy documents will be retrieved from represented countries’ government-affiliated websites. The results of the research will be disseminated through the EUPHA network, the Italian Network for Genomics in Public Health (GENISAP), and seminars and workshops. Expected impact of the study on public health: The transfer of genomic technologies from research to clinical application is influenced not only by several factors inherent to research goals and delivery of healthcare but also by external and commercial interests that may cause the premature introduction of genetic tests in the public and private sectors. Furthermore, current genetic services are delivered without a standardized set of process and outcome measures, which makes the evaluation of healthcare services difficult. The present study will identify and classify delivery models and, subsequently, establish which are appropriate for the provision of predictive genetic testing in Europe by comparing sets of process and outcome measures. In this way, the study will provide a basis for future recommendations to decision makers involved in the financing, delivery, and consumption of genetic services

    Contextual Predictive Mutation Testing

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    Mutation testing is a powerful technique for assessing and improving test suite quality that artificially introduces bugs and checks whether the test suites catch them. However, it is also computationally expensive and thus does not scale to large systems and projects. One promising recent approach to tackling this scalability problem uses machine learning to predict whether the tests will detect the synthetic bugs, without actually running those tests. However, existing predictive mutation testing approaches still misclassify 33% of detection outcomes on a randomly sampled set of mutant-test suite pairs. We introduce MutationBERT, an approach for predictive mutation testing that simultaneously encodes the source method mutation and test method, capturing key context in the input representation. Thanks to its higher precision, MutationBERT saves 33% of the time spent by a prior approach on checking/verifying live mutants. MutationBERT, also outperforms the state-of-the-art in both same project and cross project settings, with meaningful improvements in precision, recall, and F1 score. We validate our input representation, and aggregation approaches for lifting predictions from the test matrix level to the test suite level, finding similar improvements in performance. MutationBERT not only enhances the state-of-the-art in predictive mutation testing, but also presents practical benefits for real-world applications, both in saving developer time and finding hard to detect mutants

    Genomic and Transcriptomic Alterations Associated with STAT3 Activation in Head and Neck Cancer.

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    BackgroundHyperactivation of STAT3 via constitutive phosphorylation of tyrosine 705 (Y705) is common in most human cancers, including head and neck squamous carcinoma (HNSCC). STAT3 is rarely mutated in cancer and the (epi)genetic alterations that lead to STAT3 activation are incompletely understood. Here we used an unbiased approach to identify genomic and epigenomic changes associated with pSTAT3(Y705) expression using data generated by The Cancer Genome Atlas (TCGA).Methods and findingsMutation, mRNA expression, promoter methylation, and copy number alteration data were extracted from TCGA and examined in the context of pSTAT3(Y705) protein expression. mRNA expression levels of 1279 genes were found to be associated with pSTAT3(705) expression. Association of pSTAT3(Y705) expression with caspase-8 mRNA expression was validated by immunoblot analysis in HNSCC cells. Mutation, promoter hypermethylation, and copy number alteration of any gene were not significantly associated with increased pSTAT3(Y705) protein expression.ConclusionsThese cumulative results suggest that unbiased approaches may be useful in identifying the molecular underpinnings of oncogenic signaling, including STAT3 activation, in HNSCC. Larger datasets will likely be necessary to elucidate signaling consequences of infrequent alterations

    DNA methylation profiling to assess pathogenicity of BRCA1 unclassified variants in breast cancer

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    Germline pathogenic mutations in BRCA1 increase risk of developing breast cancer. Screening for mutations in BRCA1 frequently identifies sequence variants of unknown pathogenicity and recent work has aimed to develop methods for determining pathogenicity. We previously observed that tumor DNA methylation can differentiate BRCA1-mutated from BRCA1-wild type tumors. We hypothesized that we could predict pathogenicity of variants based on DNA methylation profiles of tumors that had arisen in carriers of unclassified variants. We selected 150 FFPE breast tumor DNA samples [47 BRCA1 pathogenic mutation carriers, 65 BRCAx (BRCA1-wild type), 38 BRCA1 test variants] and analyzed a subset (n=54) using the Illumina 450K methylation platform, using the remaining samples for bisulphite pyrosequencing validation. Three validated markers (BACH2, C8orf31, and LOC654342) were combined with sequence bioinformatics in a model to predict pathogenicity of 27 variants (independent test set). Predictions were compared with standard multifactorial likelihood analysis. Prediction was consistent for c.5194-12G>A (IVS 19-12 G>A) (P>0.99); 13 variants were considered not pathogenic or likely not pathogenic using both approaches. We conclude that tumor DNA methylation data alone has potential to be used in prediction of BRCA1 variant pathogenicity but is not independent of estrogen receptor status and grade, which are used in current multifactorial models to predict pathogenicity

    Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

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    An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via BĂŠzier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included
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