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Epidemiological and animal models studies generate hypotheses for innovative strategies in OSA management by interfering intermediates mechanisms associated with cardiovascular complications.
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They have been used to build workforce flexibility, productivity, and efficiency [ 22]; a Canadian workflow modelling study generated outcomes that were used to support nursing role restructure across 17 acute health sites in British Columbia [ 23].
For example, a recent study generated models based on historical data on tularaemia and plague from the USA that were consistent in relation to known climate changes over the period.
The models in this study generated results that were fairly similar to the current situation.
Measures of agreement, time to event models, and proportional odds models were considered exploratory, as the study generated estimates required to calculate study power and/or precision for these analyses in subsequent studies.
Simulation studies showed that these models could generate a decreasing function of < Knn k)>, yet the value of ν (0.18) in < Knn k)> ~ k- ν is much smaller than the actual value (0.47; see Tables 1 and 2).
Future studies using animal models will generate data that could have implications for the development of new targets for potential therapeutic intervention of this devastating pathology.
The first "wave" of disease modeling studies focused on generating patient-specific human neurons and confirming previously described pathologies (Dimos et al, 2008; Ebert et al, 2009; Marchetto et al, 2010; Brennand et al, 2011; Seibler et al, 2011; Bilican et al, 2012; Israel et al, 2012).
The data in our simulation studies were generated from model (1) Y = B L + E, where variables y i corresponding to b i = 0 are, by definition, the 'null variables' not associated with L (Supplementary Fig. S1).
In the United Kingdom, previous studies have used such models to generate cancer mortality projections up to the year 2025 (Olsen et al, 2008), and cancer incidence projections up to the year 2020 for England (Møller et al, 2007).
This approach combines toxicologic dose response data (usually from animal studies) and conventional mathematical models to generate dose response curves for the chemical in question, even in the subexperimental region, and does not assume a linear relationship in this region.
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