The use of coercive measures in forensic psychiatric care: legal, ethical and practical challenges

The use of coercive measures, namely restraint, seclusion and involuntary medication, remain controversial methods of practice within forensic psychiatry. Ethical and moral debates surrounding the use of coercive measures are compounded by the need to balance care, safety, and security. Despite such tensions, limited research has been conducted in this area. This paper examines the prevalence of coercive measures and factors associated with their use specifically within forensic psychiatry. A systematic review was conducted and fifteen empirical studies were identified, each examining the use of coercive measures in forensic inpatient psychiatry, reported in papers published between January 1980 and January 2012. Findings suggest that patients who are younger or newly admitted tend to be secluded most often. Findings relating to gender, ethnicity and patient diagnoses, however, are equivocal. Patients tend to perceive experiences of coercive measures negatively. Staff perceptions however, appear to be determined by their role in governing or practicing coercive interventions. Findings are discussed in light of variations in hospital settings, policies and sociocultural traditions. While the uses of coercive measures appear to be influenced by a combination of all patient, staff, and environmental factors, further research is required to explore each of these aspects in greater detail

Precision medicine in the age of big data: The present and future role of large-scale unbiased sequencing in drug discovery and development

High throughput molecular and functional profiling of patients is a key driver of precision medicine. DNA and RNA characterization has been enabled at unprecedented cost and scale through rapid, disruptive progress in sequencing technology, but challenges persist in data management and interpretation. We analyze the state-of-the-art of large-scale unbiased sequencing in drug discovery and development, including technology, application, ethical, regulatory, policy and commercial considerations, and discuss issues of LUS implementation in clinical and regulatory practice

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies. Database URL: BioMuta: http://hive.biochemistry.gwu.edu/tools/biomuta/index.php; CSR: http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr; HIVE: http://hive.biochemistry.gwu.ed