Fibrinolytic Parameters In Children With Non‐Catheter Related Deep Venous Thrombosis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106129/1/jth00267.pd

Clinical and laboratory variability in a cohort of patients diagnosed with type 1 VWD in the United States

Von Willebrand disease (VWD) is the most common inherited bleeding disorder, and type 1 VWD is the most common VWD variant. Despite its frequency, diagnosis of type 1 VWD remains the subject of much debate. In order to study the spectrum of type 1 VWD in the United States, the Zimmerman Program enrolled 482 subjects with a previous diagnosis of type 1 VWD without stringent laboratory diagnostic criteria. VWF laboratory testing and full length VWF gene sequencing were performed for all index cases and healthy control subjects in a central laboratory. Bleeding phenotype was characterized using the ISTH Bleeding Assessment Tool. At study entry, 64% of subjects had VWF:Ag or VWF:RCo below the lower limit of normal, while 36% had normal VWF levels. VWF sequence variations were most frequent in subjects with VWF:Ag < 30 IU/dL (82%) while subjects with type 1 VWD and VWF:Ag ≥ 30 IU/dL had an intermediate frequency of variants (44%). Subjects whose VWF testing was normal at study entry had a similar rate of sequence variations as the healthy controls at 14% of subjects. All subjects with severe type 1 VWD and VWF:Ag ≤ 5 IU/dL had an abnormal bleeding score, but otherwise bleeding score did not correlate with VWF:Ag level. Subjects with a historical diagnosis of type 1 VWD had similar rates of abnormal bleeding scores compared to subjects with low VWF levels at study entry. Type 1 VWD in the United States is highly variable, and bleeding symptoms are frequent in this population

The FAIR Guiding Principles for scientific data management and stewardship

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community