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The Bliss Independence model was used to classify the effect of combining Sirolimus and Nimotuzumab as additive, synergistic or antagonistic [ 1].
Change in these features of ILD functions was not used to classify the effect of background noise, and it is reported only for completeness of description.
Experiments aiming at determining the structure activity relationship (SAR) or reverse medicinal chemistry approach to classify the effect of various structurally diverse compounds on the heartbeat or the spectral profile it generates can be easily adapted to this model.
Therefore our list is a bit longer, and we have preferred to classify the effect into two groups, where predictable haemolysis means that AHA can be expected in a G6PD-deficient patient, whereas possible haemolysis means that AHA may or may not take place, depending on dosage, other concomitant drugs, co-morbidity and other factors.
In the last years, several proteins, especially inflammatory mediators, growth factors and cytokines [ 3, 4], proteases [ 5, 6], and oxidative stress related proteins [ 7, 8], were identified in WF which can be used to characterize and classify the effect on different phases of the wound healing process and thereby can be used as predictive and diagnostic tools.
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We used the segmented regression analysis to classify the effects of policies.
One might classify the effects of melatonin as (i) antioxidant, including influences on mitochondrial metabolism, (ii) antifibrillogenic and (iii) cytoskeletal, including the suppression of protein hyperphosphorylation.
In order to detect and classify the effects of Sorafenib in combination with other cytostatics, a series of experiments were performed.
To prioritize the nsSNPs, all the nsSNPs were further assigned a SIFT score to classify the effects of each nsSNP on phenotypic functions, based on the amino acid conservation.
We used changes in maximum response strength, putative coding features, i.e. ILD50 and peak ILD, as well as in the shape of the ILD function to classify the effects of the standard level of background noise on the ILD sensitivity function at the standard ABL, and these effects of background noise on the 66 ILD functions available for analysis are summarized in Fig. 4F.
We considered effects to be significant when the P-values were <0.05 and classified the effect magnitudes as small (0.10 < f2 ≤ 0.25), medium (0.25 < f2 ≤ 0.40), or large (0.40 < f2) following previously published guidelines.
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