Green tea catechin inhibits fatty acid synthase without stimulating carnitine Palmitoyltransferase-1 or inducing weight loss in experimental animals

Background: The enzyme fatty acid synthase (FASN) is highly expressed in many human carcinomas and its inhibition is cytotoxic to human cancer cells. The use of FASN inhibitors has been limited until now by anorexia and weight loss, which is associated with the stimulation of fatty acid oxidation. Materials and Methods: The in vitro effect of (-)-epigallocatechin-3-gallate (EGCG) on fatty acid metabolism enzymes, on apoptosis and on cell signalling was evaluated. In vivo, the effect of EGCG on animal body weight was addressed. Results: EGCG inhibited FASN activity, induced apoptosis and caused a marked decrease of human epidermal growth factor receptor 2 (HER2), phosphatidylinositol 3-kinase (PI3K)/AKT and extracellular (signal)-regulated kinase (ERK) 1/2 proteins, in breast cancer cells. EGCG did not induce a stimulatory effect on CPT-1 activity in vitro (84% of control), or on animal body weight in vivo (99% of control). Conclusion: EGCG is a FASN inhibitor with anticancer activity which does not exhibit cross-activation of fatty acid oxidation and does not induce weight loss, suggesting its potential use as an anticancer drug.

Assessing functional relations in single-case designs: Quantitative proposals in the context of the evidence-based movement

In the context of the evidence-based practices movement, the emphasis on computing effect sizes and combining them via meta-analysis does not preclude the demonstration of functional relations. For the latter aim, we propose to augment the visual analysis to add consistency to the decisions made on the existence of a functional relation without losing sight of the need for a methodological evaluation of what stimuli and reinforcement or punishment are used to control the behavior. Four options for quantification are reviewed, illustrated, and tested with simulated data. These quantifications include comparing the projected baseline with the actual treatment measurements, on the basis of either parametric or nonparametric statistics. The simulated data used to test the quantifications include nine data patterns in terms of the presence and type of effect and comprising ABAB and multiple baseline designs. Although none of the techniques is completely flawless in terms of detecting a functional relation only when it is present but not when it is absent, an option based on projecting split-middle trend and considering data variability as in exploratory data analysis proves to be the best performer for most data patterns. We suggest that the information on whether a functional relation has been demonstrated should be included in meta-analyses. It is also possible to use as a weight the inverse of the data variability measure used in the quantification for assessing the functional relation. We offer an easy to use code for open-source software for implementing some of the quantifications.