The nature of co-morbid psychopathology in adolescents with gender dysphoria

Gender-referred adolescents (GR) have been reported to present with considerable psychiatric symptomatology compared to their age-peers. There is, however, little research on how they compare to adolescents referred due to mental health problems (MHR). We set out to compare psychopathology in adolescents referred to our specialized gender identity unit (n = 84) and adolescents referred to a general adolescent psychiatric clinic (n = 293) in a university hospital setting in Finland. Of the GR adolescents, 40.9% had not received any psychiatric diagnosis during adolescence. Eating disorders were less common in the GR than in the MHR group, but otherwise the prevalences of disorders did not differ statistically significantly. At the symptom level, the GR adolescents displayed significantly more suicidal ideation and talk and less alcohol abuse and eating disorder symptoms than did the MHR adolescents, but otherwise their symptom profiles were comparable. Additionally, the GR adolescents had significantly fewer total externalizing symptoms than did the MHR adolescents. Adolescents seeking gender affirming treatments present with psychiatric symptoms and disorders comparable to those seen among adolescent psychiatric patients. Medical gender affirming care may not be a sufficient intervention for treating psychiatric comorbidities of adolescents with gender dysphoria.publishedVersionPeer reviewe

Time trends in the incidence of diagnosed depression among people aged 5-25 years living in Finland 1995-2012

Background: Knowledge of time trends for depression is important for disease prevention and healthcare planning. Only a few studies have addressed these questions regarding the incidence and cumulative incidence of diagnosed depression from childhood to early adulthood and findings have been inconclusive. Aim: The aim of this national register-based Finnish study was to report the time trends of the age-specific and gender-specific incidence and cumulative incidence of diagnosed depression. Methods: The study sample included all 1,245,502 singletons born in Finland between 1 January 1987 and 31 December 2007 and still living in Finland at the end of 2012. The participants were divided into three cohorts by birth year: 1987-1993, 1994-2000 and 2001-2007. Depression diagnoses (ICD-9: 2961; ICD-10: F32, F33) given in 1995-2012 were available and identified from the Care Register for Health Care. Results: Ten percent of the females and five percent of the males were diagnosed with depression in specialized services by age 25 years. The cumulative incidence of depression by age 15 years rose from 1.8% (95% CI 1.8-1.9) to 2.9% (95% CI 2.8-3.0) in females and from 1.0% (95% CI 1.1-1.2) to 1.6% (95% CI 1.6-1.7) in males when the cohorts born 1987-1993 and 1994-2000 were compared. Conclusions: A larger proportion of young people in Finland are diagnosed with depression in specialized services than before. This can be due to better identification, more positive attitudes to mental health problems and increased availability of the services.</div

Oral diagnosis and treatment planning: part 1. Introduction

P. Newsome, R. Smales and K. Yi

Genesis-DB: a database for autonomous laboratory systems

Acknowledgements: The authors gratefully acknowledge the members of the Ross King Group at Chalmers University for their thoughtful insights and discussions. They would also like to thank the members of the ThoughtWorks Engineering for Research Organization for their efforts and support.Funder: Chalmers AI Research CentreSummary: Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology

Genesis-DB: a database for autonomous laboratory systems

Acknowledgements: The authors gratefully acknowledge the members of the Ross King Group at Chalmers University for their thoughtful insights and discussions. They would also like to thank the members of the ThoughtWorks Engineering for Research Organization for their efforts and support.Funder: Chalmers AI Research CentreSummary: Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology

Genesis-DB: a database for autonomous laboratory systems

Acknowledgements: The authors gratefully acknowledge the members of the Ross King Group at Chalmers University for their thoughtful insights and discussions. They would also like to thank the members of the ThoughtWorks Engineering for Research Organization for their efforts and support.Funder: Chalmers AI Research CentreSummary: Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology

Genesis-DB: a database for autonomous laboratory systems

Acknowledgements: The authors gratefully acknowledge the members of the Ross King Group at Chalmers University for their thoughtful insights and discussions. They would also like to thank the members of the ThoughtWorks Engineering for Research Organization for their efforts and support.Funder: Chalmers AI Research CentreSummary: Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology

Genesis-DB: a database for autonomous laboratory systems

Acknowledgements: The authors gratefully acknowledge the members of the Ross King Group at Chalmers University for their thoughtful insights and discussions. They would also like to thank the members of the ThoughtWorks Engineering for Research Organization for their efforts and support.Funder: Chalmers AI Research CentreSummary: Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology

Genesis-DB: a database for autonomous laboratory systems

Acknowledgements: The authors gratefully acknowledge the members of the Ross King Group at Chalmers University for their thoughtful insights and discussions. They would also like to thank the members of the ThoughtWorks Engineering for Research Organization for their efforts and support.Funder: Chalmers AI Research CentreSummary: Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology