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We suggest a framework for classification of homeostatic signals that is based on different classes of homeostatic variables they report on.
Trials were eligible for inclusion in this prospective collaborative meta-analysis if they met one of the following criteria: patients were randomised to a blood pressure lowering drug/regimen or a control group (placebo or less intensive blood pressure lowering regimen) or patients were randomised between regimens based on different classes of drugs to lower blood pressure.
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We did separate overviews of trials comparing active treatment regimens with placebo, trials comparing more intensive and less intensive blood-pressure-lowering strategies, and trials comparing treatment regimens based on different drug classes.
We aimed to estimate effects of strategies based on different drug classes (angiotensin-converting-enzyme [ACE] inhibitors, calcium antagonists, angiotensin-receptor blockers [ARBs], and diuretics or β blockers) or those targeting different blood pressure goals, on the risks of major cardiovascular events and death.
This indicates that after these phyla split different regulatory strategies have developed based on different regulator classes.
In the trials comparing blood pressure lowering regimens based on different drug classes, there were no clear differential effects on major cardiovascular events (fig 4) between groups with different eGFR for any comparison (all P>0.6 for homogeneity).
The mitigation measures proposed based on different predominating class is overlapped in each and every cell units.
The results obtained from the mathematical model and the applied SA algorithm are summarized in Table 6, based on different test problem classes.
In order to investigate this issue, we used clustering techniques: (1) to investigate and better understand the distribution and the variance of unit test case metrics based on different categories of classes, and (2) to determine what are the less volatile unit test case metrics, which are the less influenced by the style adopted by the developers while writing the code of unit test cases.
The reconstructed maps could be further classified into two classes based on different features of the TibA density.
So far, more than 16 alleles have been detected and grouped into four classes based on different expression levels of αS1-CN in the milk.
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