Treatment of chronic venous disease with flavonoids: recommendations for treatment and further studies.

OBJECTIVES A variety of studies have suggested that flavonoids are effective for the treatment of CVD. However, many questions remain about their mechanism of action and when, how, and for what signs and symptoms they should be used. METHOD A panel of experts in CVD met in Budapest, Hungary in December 2011 to discuss the current state of knowledge of CVD and the role of flavonoids in its treatment. The discussion was based on a literature search in the current databases. The goals of this paper are recommendations for further studies on the use of flavonoids in the treatment of CVD. RESULTS There is good evidence to recommend the use of flavonoids in the treatment of CVD. However, because of the poor quality of some older clinical trials, inadequate reporting, and insufficient information, much work is still needed to firmly establish their clinical efficacy and to determine when and how they should be employed. In particular, long-term randomized, placebo-controlled, double-blind studies are needed to establish the efficacy and safety of flavonoids. Additional studies are also needed to establish their mechanism of action, pharmacokinetics, toxicity, and cost-effectiveness. CONCLUSIONS Aside from good evidence for the use of flavonoids in CVD further studies are indicated to establish long term treatment in this indication.

Implicit task sequence learning

Implicit task sequence learning (TSL) can be considered as an extension of implicit sequence learning which is typically tested with the classical serial reaction time task (SRTT). By design, in the SRTT there is a correlation between the sequence of stimuli to which participants must attend and the sequence of motor movements/key presses with which participants must respond. The TSL paradigm allows to disentangle this correlation and to separately manipulate the presences/absence of a sequence of tasks, a sequence of responses, and even other streams of information such as stimulus locations or stimulus-response mappings. Here I review the state of TSL research which seems to point at the critical role of the presence of correlated streams of information in implicit sequence learning. On a more general level, I propose that beyond correlated streams of information, a simple statistical learning mechanism may also be involved in implicit sequence learning, and that the relative contribution of these two explanations differ according to task requirements. With this differentiation, conflicting results can be integrated into a coherent framework.