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81.
Stephen F?n Hughes Samantha Jayne Thomas-Wright Joseph Banwell Rachel Williams Alyson Jayne Moyes Sohail Mushtaq Mohamed Abdulmajed Iqbal Shergill 《PloS one》2015,10(5)
PurposeThe number of patients undergoing shock wave lithotripsy (SWL) in the UK for solitary unilateral kidney stones is increasing annually. The development of postoperative complications such as haematuria and sepsis following SWL is likely to increase. Comparing a range of biological markers with the aim of monitoring or predicting postoperative complications following SWL has not been extensively researched. The main purpose of this pilot-study was to test the hypothesis that SWL results in changes to haemostatic function. Subsequently, this pilot-study would form a sound basis to undertake future investigations involving larger cohorts.MethodsTwelve patients undergoing SWL for solitary unilateral kidney stones were recruited. From patients (8 male and 4 females) aged between 31–72 years (median—43 years), venous blood samples were collected pre-operatively (baseline), at 30, 120 and 240 minutes postoperatively. Specific haemostatic biomarkers [platelet counts, prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen, D-dimer, von Willebrand Factor (vWF), sE-selectin and plasma viscosity (PV)] were measured.ResultsPlatelet counts and fibrinogen concentration were significantly decreased following SWL (p = 0.027 and p = 0.014 respectively), while D-dimer and vWF levels significantly increased following SWL (p = 0.019 and p = 0.001 respectively). PT, APTT, sE-selectin and PV parameters were not significantly changed following SWL (p>0.05).ConclusionsChanges to specific biomarkers such as plasma fibrinogen and vWF suggest that these represent a more clinically relevant assessment of the extent of haemostatic involvement following SWL. Analysis of such markers, in the future, may potentially provide valuable data on “normal” response after lithotripsy, and could be expanded to identify or predict those patients at risk of coagulopathy following SWL. The validation and reliability will be assessed through the assessment of larger cohorts. 相似文献
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Zachary D. Stephens Skylar Y. Lee Faraz Faghri Roy H. Campbell Chengxiang Zhai Miles J. Efron Ravishankar Iyer Michael C. Schatz Saurabh Sinha Gene E. Robinson 《PLoS biology》2015,13(7)
Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.We compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Astronomy has faced the challenges of Big Data for over 20 years and continues with ever-more ambitious studies of the universe. YouTube burst on the scene in 2005 and has sparked extraordinary worldwide interest in creating and sharing huge numbers of videos. Twitter, created in 2006, has become the poster child of the burgeoning movement in computational social science [6], with unprecedented opportunities for new insights by mining the enormous and ever-growing amount of textual data [7]. Particle physics also produces massive quantities of raw data, although the footprint is surprisingly limited since the vast majority of data are discarded soon after acquisition using the processing power that is coupled to the sensors [8]. Consequently, we do not include the domain in full detail here, although that model of rapid filtering and analysis will surely play an increasingly important role in genomics as the field matures.To compare these four disparate domains, we considered the four components that comprise the “life cycle” of a dataset: acquisition, storage, distribution, and analysis (
Data Phase
Astronomy
Twitter
YouTube
Genomics
Acquisition
25 zetta-bytes/year 0.5–15 billion tweets/year 500–900 million hours/year 1 zetta-bases/year
Storage
1 EB/year 1–17 PB/year 1–2 EB/year 2–40 EB/year
Analysis
In situ data reduction Topic and sentiment mining Limited requirements Heterogeneous data and analysis Real-time processing Metadata analysis Variant calling, ~2 trillion central processing unit (CPU) hours Massive volumes All-pairs genome alignments, ~10,000 trillion CPU hours
Distribution
Dedicated lines from antennae to server (600 TB/s) Small units of distribution Major component of modern user’s bandwidth (10 MB/s) Many small (10 MB/s) and fewer massive (10 TB/s) data movement