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The strong inhibition of tumour cells by arginase indicates that it should be a very effective antitumour agent for treating animals, provided this effect can be translated into an effective in vivo protocol, as would seem possible from the work of Storr and Burton (1974), who obtained considerable tumour reduction after a remarkably short treatment time (3 – 4 days).
However, there is insufficient evidence that such a beneficial effect can be translated to more clinically relevant outcomes such as mortality or duration of MV.
However, there is insufficient evidence that such beneficial effect can be translated to more clinically relevant outcomes such as mortality, LOS in the ICU or duration of MV.
The challenge, however, is to determine the clinical significance of this effect size and whether such an effect can be translated into reducing patients' or caregivers' burden.
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The effects have been interpreted as labour supply effects even though higher labour force participation can be translated into higher employment only if wages fall such that an increased labour demand is forthcoming.
In this paper, a hierarchically structured formalism, the Hierarchical Cause-Effect Charts (HCEC), is introduced to iteratively refine and formalize complex control requirements of industrial systems, resulting in a hierarchical tree structure of small and transparent cause-effect matrices that can be translated into Boolean formulae algorithmically.
Second, its enzymatic activity must be linked to an effect or signal that can be translated into a cellular response.
Future studies are needed to evaluate its efficacy and side effect profiles before it can be translated into clinical applications for the treatment of ovarian cancer patients.
In this way, knowledge can be obtained about the contributions and variability of individual processes on the causal path between drug dosing and CNS effect in animals that can be translated to the human situation.
It is important to mention that if a given therapeutic agent has an influence that is not represented by any of our model's parameters but corresponds with the in- or outflux of a model variable, the effect of this agent can be translated in our model by a change in the respective rate constant γi.
Emphasis on behavioral domains rather than clinical diagnosis, as proposed in the Research Domain Criteria framework, facilitates the use of mouse models that recapitulate human polymorphisms because effects on equivalent phenotypes can be translated across species and integrated across levels of analysis.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com