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As clearly stated now in Patients and Methods, our database included 310 patients.
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Table 7 shows the results of the detection methods using our database.
Patients and methods: From our database of sublingual microcirculatory assessment by IDF (Braedius Medical, The Netherlands), 50 images were randomly selected (July 2017 October 2017).
Four validation methods confirmed our database, which would help in further studies on the mechanism of lung tumorigenesis.
Methods: We reviewed our database for the detection of Dystrophin gene mutation by means of 31-exon multiplex PCR in Thai males, diagnosed clinically and biochemically with DMD or BMD from July 1994 to November 2006.
As will be shown in the results section, the background in these images is rather complex so that if our method works well with our database, it will certainly work with Triesch database.
Our database modelling method has five steps, as described below.
Based on our database modelling method, we implemented the DBLModeller command line tool to automate the 'Workload Extraction' and 'T2M Transformation' steps of the method.
As described next, our database modelling method and DBLModeller tool adopt a platform-independent approach that utilises a SQL schema dump and a SQL query log (if necessary).
Our database modelling method and DBLModeller tool only support the KDM data package, and therefore do not comply with any level.
In particular, our database modelling method and tool were applied to 15 systems (including Apache OFBiz, and MediaWiki) to obtain workload and structure models.
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