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All biochemical serum evaluations, which are used to investigate main metabolic pathways and organ functions, were performed at the same time to minimize analytical variability, and determined by dry chemistry on a Vitros 250 Analyzer (Ortho Clinical Diagnostics, Johnson & Johnson Co, Rochester, NY).
These samples were then analyzed in one run in order to minimize analytical variability.
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All assays were performed on a single analyzer using the same reagents and were conducted by skilled technicians and, therefore, minimizing analytical variability.
All assays were carried out in duplicate to minimize analytical errors.
We also used fresh tumor samples to minimize analytical errors and our statistical analyses were adjusted for known risk factors.
The run order of the samples was randomized to minimize the analytical variability such as day to day instrumental drift, SPME fiber degradation, or ambient background differences and to ensure that comparisons between four groups were unbiased.
We wanted to simultaneously analyze pre- and post-dosing PBMC samples to minimize within-run analytical variability while allowing detailed analysis for each major immune cell subset.
To minimize the effect of analytical variability all samples from the same patient were tested in the same analytical series.
Various sources of technical and analytical variability are associated with miRNA profiling and it has been suggested that much more work is required to identify and minimize these technical variables and increase reliability and credibility of miRNA profiles (reviewed in [ 17]).
Analytical variability is an important consideration.
For reference, average analytical variability was 3.4% from five sweeps during analysis.
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