A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services

OntoBrowser: a tool for curating ontologies and code lists by subject matter experts.

Lack of compliance of terminology and ontology usage leads to partial search results and poor inter-operability between databases. The source of the problem is often the difficulty of finding subject matter experts having time, skills and tools to facilitate curation. Existing mapping software are usually not intuitive and the process is time consuming. The primary objective of OntoBrowser is to provide an easy to use online collaborative solution for subject matter experts, to map reported terms to preferred ontologies or controlled terminologies. Additional features include: web service access to data, visualization of ontologies in hierarchical/graph format, advanced search capabilities, peer review/approval workflow

Generating modelling data from repeat-dose toxicity reports

Over the past decades, pharmaceutical companies have conducted a large number of high quality in vivo repeat-dose toxicity (RDT) studies for regulatory purposes. As part of the eTOX project, a high number of these studies have been compiled and integrated into a database. This valuable resource can be queried directly, but it can be further exploited to build predictive models. As the studies were originally conducted to investigate the properties of individual compounds, the experimental conditions across the studies are highly heterogeneous. Consequently, the original data required normalization/standardization, filtering, categorization and integration to make possible any data analysis (such as building predictive models). Additionally, the primary objectives of the RDT studies were to identify toxicological findings, most of which do not directly translate to in vivo endpoints. This article describes a method to extract datasets containing comparable toxicological properties for a series of compounds amenable for building predictive models. The proposed strategy starts with the normalization of the terms used within the original reports. Then, comparable datasets are extracted from the database by applying filters based on the experimental conditions. Finally, carefully selected profiles of toxicological findings are mapped to endpoints of interest, generating QSAR-like tables. In this work, we describe in detail the strategy and tools used for carrying out these transformations and illustrate its application in a data sample extracted from the eTOX database. The suitability of the resulting tables for developing hazard-predicting models was investigated by building proof-of-concept models for in vivo liver endpoints

Distribution of medium- to large-sized African mammals based on habitat suitability models

The knowledge of the areas inhabited by a species within its distribution range and the connections among patches are critical pieces of information for successful conservation actions. The internal structure of the extent of occurrence (EO) of a species is almost always unknown, even for "well-known" flagship species. We developed a methodology to infer the area of occupancy (AO) within the EO of a species using the limited available data. We present here the results of a three years project funded by European Union to develop high-resolution models of habitat suitability for 281 medium- to large-sized African mammals across the whole continent. The existing literature was reviewed and all data on the geographic distribution and environmental preferences of the selected species were collected. For each species, these data were then expressed in terms of key variables available as GIS layers at a resolution of 1 km(2) over the entire African continent. The AO of each species was obtained merging the information on the ecological needs of the species and the values of ecological variables over the region identified as EO. The habitat suitability models were evaluated through direct field work in four countries (Morocco, Cameroon, Uganda, Botswana) chosen as representatives of the environmental and species diversity of Africa. More than 81% of models had positive true skill statistics (TSS) values, indicating models performing better than random. Rigorous modeling procedures supported by ad-hoc field evaluation allowed the production of high-resolution habitat suitability models useful for conservation applications

Generating modeling data from repeat-dose toxicity reports

Over the past decades, pharmaceutical companies have conducted a large number of high-quality in vivo repeat-dose toxicity (RDT) studies for regulatory purposes. As part of the eTOX project, a high number of these studies have been compiled and integrated into a database. This valuable resource can be queried directly, but it can be further exploited to build predictive models. As the studies were originally conducted to investigate the properties of individual compounds, the experimental conditions across the studies are highly heterogeneous. Consequently, the original data required normalization/standardization, filtering, categorization and integration to make possible any data analysis (such as building predictive models). Additionally, the primary objectives of the RDT studies were to identify toxicological findings, most of which do not directly translate to in vivo endpoints. This article describes a method to extract datasets containing comparable toxicological properties for a series of compounds amenable for building predictive models. The proposed strategy starts with the normalization of the terms used within the original reports. Then, comparable datasets are extracted from the database by applying filters based on the experimental conditions. Finally, carefully selected profiles of toxicological findings are mapped to endpoints of interest, generating QSAR-like tables. In this work, we describe in detail the strategy and tools used for carrying out these transformations and illustrate its application in a data sample extracted from the eTOX database. The suitability of the resulting tables for developing hazard-predicting models was investigated by building proof-of-concept models for in vivo liver endpoints.Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115002 (eTOX), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in-kind contributions. Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 777365 (eTRANSAFE). This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA companies' in-kind contributions

Generating modeling data from repeat-dose toxicity reports

Over the past decades, pharmaceutical companies have conducted a large number of high-quality in vivo repeat-dose toxicity (RDT) studies for regulatory purposes. As part of the eTOX project, a high number of these studies have been compiled and integrated into a database. This valuable resource can be queried directly, but it can be further exploited to build predictive models. As the studies were originally conducted to investigate the properties of individual compounds, the experimental conditions across the studies are highly heterogeneous. Consequently, the original data required normalization/standardization, filtering, categorization and integration to make possible any data analysis (such as building predictive models). Additionally, the primary objectives of the RDT studies were to identify toxicological findings, most of which do not directly translate to in vivo endpoints. This article describes a method to extract datasets containing comparable toxicological properties for a series of compounds amenable for building predictive models. The proposed strategy starts with the normalization of the terms used within the original reports. Then, comparable datasets are extracted from the database by applying filters based on the experimental conditions. Finally, carefully selected profiles of toxicological findings are mapped to endpoints of interest, generating QSAR-like tables. In this work, we describe in detail the strategy and tools used for carrying out these transformations and illustrate its application in a data sample extracted from the eTOX database. The suitability of the resulting tables for developing hazard-predicting models was investigated by building proof-of-concept models for in vivo liver endpoints.Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115002 (eTOX), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in-kind contributions. Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 777365 (eTRANSAFE). This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA companies' in-kind contributions

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

BACKGROUND: Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services. METHODS/DESIGN: The Psychosis early Intervention and Assessment of Needs and Outcome (PIANO) trial is part of a larger research program (Genetics, Endophenotypes and Treatment: Understanding early Psychosis - GET UP) which aims to compare, at 9 months, the effectiveness of a multi-component psychosocial intervention versus treatment as usual (TAU) in a large epidemiologically based cohort of patients with FEP and their family members recruited from all public community mental health centers (CMHCs) located in two entire regions of Italy (Veneto and Emilia Romagna), and in the cities of Florence, Milan and Bolzano. The GET UP PIANO trial has a pragmatic cluster randomized controlled design. The randomized units (clusters) are the CMHCs, and the units of observation are the centers' patients and their family members. Patients in the experimental group will receive TAU plus: 1) cognitive behavioral therapy sessions, 2) psycho-educational sessions for family members, and 3) case management. Patient enrollment will take place over a 1-year period. Several psychopathological, psychological, functioning, and service use variables will be assessed at baseline and follow-up. The primary outcomes are: 1) change from baseline to follow-up in positive and negative symptoms' severity and subjective appraisal; 2) relapse occurrences between baseline and follow-up, that is, episodes resulting in admission and/or any case-note records of re-emergence of positive psychotic symptoms. The expected number of recruited patients is about 400, and that of relatives about 300. Owing to the implementation of the intervention at the CMHC level, the blinding of patients, clinicians, and raters is not possible, but every effort will be made to preserve the independency of the raters. We expect that this study will generate evidence on the best treatments for FEP, and will identify barriers that may hinder its feasibility in 'real-world' clinical settings, patient/family conditions that may render this intervention ineffective or inappropriate, and clinical, psychological, environmental, and service organization predictors of treatment effectiveness, compliance, and service satisfaction