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In analogy, all possible biomarkers need to be carefully validated in cohort studies [77].
With these data we emphasize that experimental data from mouse models should be carefully validated in human cells to identify discrepancies, and to ensure that further therapeutic applications are efficient and safe.
As there are no universal reference genes for all experimental systems, the choice of reference genes has to be carefully validated in each individual experiment [ 25].
This could be an additional promising extension of this study, although it needs to be taken into account that the translations of value descriptions into different languages would have to be carefully validated in order to avoid shifts in meaning.
However, as our findings are the result of observations in a single-center cohort, no clinical extrapolation of our results can be made at this time, and our data need to be carefully validated in prospective external and independent cohort studies.
Similar(55)
Thus, traditional lessons must be recalled; experimental data should always be interpreted with caution with respect to their applicability to the human setting, and assays should be carefully validated and compared between different laboratories in order to obtain reliable and consistent data.
Biomarker assays need to be carefully validated and be robust, reliable and reproducible when applied in clinical contexts.
These tools need to be carefully validated before they can be used for planning purposes.
The capabilities of those systems need to be carefully validated against the control needs.
This indicates that a 3D velocity structure model should be carefully validated before performing a source inversion.
From this viewpoint, these identified biomarkers should be carefully validated for their biological functions.
More suggestions(16)
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be carefully developed in
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be carefully planned in
be carefully scrutinized in
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com