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This validation is not a complete clinical validation which is expensive and difficult to implement but a "sensitivity" validation which ensures that the developed microarray and protocol is likely to perform well for clinical use.
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However, the aforementioned confirmatory experiments with full-length viral RNA genomic preparations have shown that this novel strategy is a suitable alternative for sensitivity validation purposes.
These sensitivity validation experiments differed from real world testing situations in a number of ways: they used DNA instead of RNA (majority of viral agents detected by RPM-TEI are RNA viruses) and the test templates contained isolated target sequences outside of the whole genome context.
Due to the high risks associated with handling these particular target pathogens, the sensitivity validation of the RPM-TEI has been performed using an innovative approach, in which synthetic DNA fragments are used as templates for testing the assay's limit of detection (LOD).
Below, advantages and disadvantages of these approaches are explored, as well as how lower sensitivity validation can result in misinterpretation.
Subsequent analyses for case control and family datasets were performed using the set of parameters that produced the best combination of sensitivity, validation and average CNV quality score.
For specificity and sensitivity validation, fluorescence stereomicroscopy of imaging ex vivo data is compared directly to histology results by accurate registration of pathology sections (<1 mm resolution) and evaluation by a gastrointestinal pathologist.
Our previous experience with respiratory organisms has indicated that a LOD ∼104 copies for the sensitivity validations provides the required detection sensitivity in real world clinical samples ∼10 pfu [19].
The results of this work indicate that LOD at ∼104 copies using synthetic templates correlates with 10 pfu or less of the full length viral genomic preparations which we believe is the required target LOD of sensitivity validations.
The spearman rank correlation coefficient method and the full factorial designs method were used for a sensitivity analysis and validation.
High-scoring predictions not already verified in published screens could become interesting candidates for higher-sensitivity validation experiments, as suggested in our comparison with systematic RNAi screening data.
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