The case for open science: rare diseases.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally

Common data elements for pediatric traumatic brain injury: Recommendations from the working group on demographics and clinical assessment

The Common Data Elements (CDEs) initiative is a National Institutes of Health (NIH) interagency effort to standardize naming, definitions, and data structure for clinical research variables. Comparisons of the results of clinical studies of neurological disorders have been hampered by variability in data coding, definitions, and procedures for sample collection. The CDE project objective is to enable comparison of future clinical trials results in major neurological disorders, including traumatic brain injury (TBI), stroke, multiple sclerosis, and epilepsy. As part of this effort, recommendations for CDEs for research on TBI were developed through a 2009 multi-agency initiative. Following the initial recommendations of the Working Group on Demographics and Clinical Assessment, a separate workgroup developed recommendations on the coding of clinical and demographic variables specific to pediatric TBI studies for subjects younger than 18 years. This article summarizes the selection of measures by the Pediatric TBI Demographics and Clinical Assessment Working Group. The variables are grouped into modules which are grouped into categories. For consistency with other CDE working groups, each variable was classified by priority (core, supplemental, and emerging). Templates were produced to summarize coding formats, guide selection of data points, and provide procedural recommendations. This proposed standardization, together with the products of the other pediatric TBI working groups in imaging, biomarkers, and outcome assessment, will facilitate multi-center studies, comparison of results across studies, and high-quality meta-analyses of individual patient data

Research-Practice-Policy Partnerships for Implementation of Evidence-Based Practices in Child Welfare and Child Mental Health

Partnerships between researchers, practitioners, and policymakers represent a promising avenue for improving outcomes for young people and families.In a new report, Lawrence Palinkas, Cherry Short, and Marleen Wong of the University of Southern California's School of Social Work suggest that research-practice-policy partnerships may help narrow the gap between the development of evidence-based services for young people in the child welfare and mental health systems and the routine delivery of these services.Describing the structure and operations of partnerships, and the potential challenges to making them work, Palinkas and colleagues present three models of successful partnerships in the child welfare and mental health systems. Case studies for each model provide rich examples of the common elements and central themes that characterize the value of partnerships as a strategy for delivering high quality services in high demand settings

Menstruation: science and society

© 2020 The Authors Women\u27s health concerns are generally underrepresented in basic and translational research, but reproductive health in particular has been hampered by a lack of understanding of basic uterine and menstrual physiology. Menstrual health is an integral part of overall health because between menarche and menopause, most women menstruate. Yet for tens of millions of women around the world, menstruation regularly and often catastrophically disrupts their physical, mental, and social well-being. Enhancing our understanding of the underlying phenomena involved in menstruation, abnormal uterine bleeding, and other menstruation-related disorders will move us closer to the goal of personalized care. Furthermore, a deeper mechanistic understanding of menstruation—a fast, scarless healing process in healthy individuals—will likely yield insights into a myriad of other diseases involving regulation of vascular function locally and systemically. We also recognize that many women now delay pregnancy and that there is an increasing desire for fertility and uterine preservation. In September 2018, the Gynecologic Health and Disease Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development convened a 2-day meeting, “Menstruation: Science and Society” with an aim to “identify gaps and opportunities in menstruation science and to raise awareness of the need for more research in this field.” Experts in fields ranging from the evolutionary role of menstruation to basic endometrial biology (including omic analysis of the endometrium, stem cells and tissue engineering of the endometrium, endometrial microbiome, and abnormal uterine bleeding and fibroids) and translational medicine (imaging and sampling modalities, patient-focused analysis of menstrual disorders including abnormal uterine bleeding, smart technologies or applications and mobile health platforms) to societal challenges in health literacy and dissemination frameworks across different economic and cultural landscapes shared current state-of-the-art and future vision, incorporating the patient voice at the launch of the meeting. Here, we provide an enhanced meeting report with extensive up-to-date (as of submission) context, capturing the spectrum from how the basic processes of menstruation commence in response to progesterone withdrawal, through the role of tissue-resident and circulating stem and progenitor cells in monthly regeneration—and current gaps in knowledge on how dysregulation leads to abnormal uterine bleeding and other menstruation-related disorders such as adenomyosis, endometriosis, and fibroids—to the clinical challenges in diagnostics, treatment, and patient and societal education. We conclude with an overview of how the global agenda concerning menstruation, and specifically menstrual health and hygiene, are gaining momentum, ranging from increasing investment in addressing menstruation-related barriers facing girls in schools in low- to middle-income countries to the more recent “menstrual equity” and “period poverty” movements spreading across high-income countries

Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM)

AbstractEfficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM’s initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements.Using a case study approach, we evaluated ODM’s ability to capture all necessary protocol elements during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health. ODM offers the advantage of a single format for institutions that deal with hundreds or thousands of concurrent clinical studies and maintain a data warehouse for these studies. For each study stage, we present a list of gaps in the ODM standard and identify necessary vendor or institutional extensions that can compensate for such gaps. The current version of ODM (1.3.2) has only partial support for study protocol and study registration data mainly because it is outside the original development goal. ODM provides comprehensive support for representation of case report forms (in both the design stage and with patient level data). Inclusion of requirements of observational, non-regulated or investigator-initiated studies (outside Food and Drug Administration (FDA) regulation) can further improve future revisions of the standard

Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022

Oncology like most medical specialties, is undergoing a data revolution at the center of which lie vast and growing amounts of clinical data in unstructured, semi-structured and structed formats. Artificial intelligence approaches are widely employed in research endeavors in an attempt to harness electronic medical records data to advance patient outcomes. The use of clinical oncologic data, although collected on large scale, particularly with the increased implementation of electronic medical records, remains limited due to missing, incorrect or manually entered data in registries and the lack of resource allocation to data curation in real world settings. Natural Language Processing (NLP) may provide an avenue to extract data from electronic medical records and as a result has grown considerably in medicine to be employed for documentation, outcome analysis, phenotyping and clinical trial eligibility. Barriers to NLP persist with inability to aggregate findings across studies due to use of different methods and significant heterogeneity at all levels with important parameters such as patient comorbidities and performance status lacking implementation in AI approaches. The goal of this review is to provide an updated overview of natural language processing (NLP) and the current state of its application in oncology for clinicians and researchers that wish to implement NLP to augment registries and/or advance research projects

Principles of precision medicine in stroke

The era of precision medicine has arrived and conveys tremendous potential, particularly for stroke neurology. The diagnosis of stroke, its underlying aetiology, theranostic strategies, recurrence risk and path to recovery are populated by a series of highly individualised questions. Moreover, the phenotypic complexity of a clinical diagnosis of stroke makes a simple genetic risk assessment only partially informative on an individual basis. The guiding principles of precision medicine in stroke underscore the need to identify, value, organise and analyse the multitude of variables obtained from each individual to generate a precise approach to optimise cerebrovascular health. Existing data may be leveraged with novel technologies, informatics and practical clinical paradigms to apply these principles in stroke and realise the promise of precision medicine. Importantly, precision medicine in stroke will only be realised once efforts to collect, value and synthesise the wealth of data collected in clinical trials and routine care starts. Stroke theranostics, the ultimate vision of synchronising tailored therapeutic strategies based on specific diagnostic data, demand cerebrovascular expertise on big data approaches to clinically relevant paradigms. This review considers such challenges and delineates the principles on a roadmap for rational application of precision medicine to stroke and cerebrovascular health

Nat Methods

Outbreak.info Research Library is a standardized, searchable interface of coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) publications, clinical trials, datasets, protocols and other resources, built with a reusable framework. We developed a rigorous schema to enforce consistency across different sources and resource types and linked related resources. Researchers can quickly search the latest research across data repositories, regardless of resource type or repository location, via a search interface, public application programming interface (API) and R package.75N91019D00024/CA/NCI NIH HHSUnited States/U24 TR002306/TR/NCATS NIH HHSUnited States/U19 AI135995/AI/NIAID NIH HHSUnited States/75D30120C09795/CC/CDC HHSUnited States/U24 LM013755/LM/NLM NIH HHSUnited States/U19 AI135995-03S2/AI/NIAID NIH HHSUnited States/R01 GM083924/GM/NIGMS NIH HHSUnited States/R01GM083924/GM/NIGMS NIH HHSUnited States

Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium.

Adult diffuse gliomas are a diverse group of brain neoplasms that inflict a high emotional toll on patients and their families. The Cancer Genome Atlas and similar projects have provided a comprehensive understanding of the somatic alterations and molecular subtypes of glioma at diagnosis. However, gliomas undergo significant cellular and molecular evolution during disease progression. We review the current knowledge on the genomic and epigenetic abnormalities in primary tumors and after disease recurrence, highlight the gaps in the literature, and elaborate on the need for a new multi-institutional effort to bridge these knowledge gaps and how the Glioma Longitudinal Analysis Consortium (GLASS) aims to systemically catalog the longitudinal changes in gliomas. The GLASS initiative will provide essential insights into the evolution of glioma toward a lethal phenotype, with the potential to reveal targetable vulnerabilities and, ultimately, improved outcomes for a patient population in need

Multimodal characterization of the late effects of traumatic brain injury: a methodological overview of the Late Effects of Traumatic Brain Injury Project

Epidemiological studies suggest that a single moderate-to-severe traumatic brain injury (TBI) is associated with an increased risk of neurodegenerative disease, including Alzheimer’s and Parkinson’s disease (AD and PD). Histopathological studies describe complex neurodegenerative pathologies in individuals exposed to single moderate-to-severe TBI or repetitive mild TBI, including chronic traumatic encephalopathy (CTE). However, the clinicopathological links between TBI and post-traumatic neurodegenerative diseases such as AD, PD, and CTE remain poorly understood. Here we describe the methodology of the Late Effects of TBI (LETBI) study, whose goals are to characterize chronic post-traumatic neuropathology and to identify in vivo biomarkers of post-traumatic neurodegeneration. LETBI participants undergo extensive clinical evaluation using National Institutes of Health TBI Common Data Elements, proteomic and genomic analysis, structural and functional MRI, and prospective consent for brain donation. Selected brain specimens undergo ultra-high resolution ex vivo MRI and histopathological evaluation including whole mount analysis. Co-registration of ex vivo and in vivo MRI data enables identification of ex vivo lesions that were present during life. In vivo signatures of postmortem pathology are then correlated with cognitive and behavioral data to characterize the clinical phenotype(s) associated with pathological brain lesions. We illustrate the study methods and demonstrate proof of concept for this approach by reporting results from the first LETBI participant, who despite the presence of multiple in vivo and ex vivo pathoanatomic lesions had normal cognition and was functionally independent until her mid-80s. The LETBI project represents a multidisciplinary effort to characterize post-traumatic neuropathology and identify in vivo signatures of postmortem pathology in a prospective study