It's difficult,but important,to make negative predictions

At the confluence of predictive and regulatory toxicologies, negative predictions may be the thin green line that prevents populations from being exposed to harm. Here, two novel approaches to making confident and robust negative in silico predictions for mutagenicity (as defined by the Ames test) have been evaluated. Analyses of 12 data sets containing >13,000 compounds, showed that negative predictivity is high (∼90%) for the best approach and features that either reduce the accuracy or certainty of negative predictions are identified as misclassified or unclassified respectively. However, negative predictivity remains high (and in excess of the prevalence of non-mutagens) even in the presence of these features, indicating that they are not flags for mutagenicity.     

La autonomía del paciente con enfermedades crónicas: De paciente pasivo a paciente activo

Due to social, economic and cultural changes, there has been a transformation of Health Services around the world. A new figure has emerged from this: the Active Patient, more responsible, with more information and willing to change his life as a chronic patient. In order to respond to this new situation, several countries have established initiatives such as self-reliance programmes for chronic patients. The aim of this article is to underline the Expert Patient Programme Catalonia^® and to explain its operation and the results obtained up until now.The purpose of this program is to improve the experience of chronic disease by patients, from meetings in which an expert patient provides his knowledge and experiences to a group of patients with the same disease, with the aim of promoting changes in habits and lifestyles that improve the quality of life and the coexistence of the person with his chronic process.     

Homeodomain‐interacting protein kinase (Hipk) phosphorylates the small SPOC family protein Spenito

The Drosophila homeodomain‐interacting protein kinase (Hipk) is a versatile regulator involved in a variety of pathways, such as Notch and Wingless signalling, thereby acting in processes including the promotion of eye development or control of cell numbers in the nervous system. In vertebrates, extensive studies have related its homologue HIPK2 to important roles in the control of p53‐mediated apoptosis and tumour suppression. Spenito (Nito) belongs to the group of small SPOC family proteins and has a role, amongst others, as a regulator of Wingless signalling downstream of Armadillo. In the present study, we show that both proteins have an enzyme–substrate relationship, adding a new interesting component to the broad range of Hipk interactions, and we map several phosphorylation sites of Nito. Furthermore, we were able to define a preliminary consensus motif for Hipk target sites, which will simplify the identification of new substrates of this kinase.     

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support

Objectives(1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available.MethodThe method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P.ResultsWhen employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence.ConclusionsThis development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.