This prospective 6-week study examined the differences in dosage and steady state plasma concentrations of sertraline in Chinese versus Caucasian depressed patients. Two groups of Chinese patients from different geographical sites and a group of Caucasian patients were evaluated with clinical measures during an initial dose of 50 mg/day, with subsequent doses adjusted clinically. The results of 17 Australian Chinese (ACHI), 13 Malaysian Chinese (MCHI) and 15 Australian Caucasians (AC) were analysed. Despite controlling for weight, the AC subjects received a significantly higher dose than both the ACHI (P = 0.002) and the MCHI groups (P = 0.012). However, the mean sertraline concentration to dose ratios at weeks 1 and 6 were not significantly different between the three groups. Sertraline was effective and well tolerated in both ethnic groups with few adverse events. Although there was a lack of difference between groups in the pharmacokinetic results, Chinese depressed patients appeared to require lower dosages with consequently lower plasma concentrations of sertraline compared to Caucasian patients to achieve clinical efficacy. Further studies of the dosages, kinetics and adverse effects of selective serotonin reuptake inhibitors linked with genotyping are necessary.
Influential nodes identification technology is one of the important topics which has been widely applied to logistics node location, social information dissemination, transportation network carrying, biological virus dissemination, power network anti-destruction, etc. At present, a large number of influential nodes identification methods have been studied, but the algorithms that are simple to execute, have high accuracy and can be better applied to real networks are still the focus of research. Therefore, due to the advantages of simple to execute in voting mechanism, a novel algorithm based on adaptive adjustment of voting ability (AAVA) to identify the influential nodes is presented by considering the local attributes of node and the voting contribution of its neighbor nodes, to solve the problem of low accuracy and discrimination of the existing algorithms. This proposed algorithm uses the similarity between the voting node and the voted node to dynamically adjust its voting ability without setting any parameters, so that a node can contribute different voting abilities to different neighbor nodes. To verify the performance of AAVA algorithm, the running results of 13 algorithms are analyzed and compared on 10 different networks with the SIR model as a reference. The experimental results show that the influential nodes identified by AAVA have high consistency with SIR model in Top-10 nodes and Kendall correlation, and have better infection effect of the network. Therefore, it is proved that AAV algorithm has high accuracy and effectiveness, and can be applied to real complex networks of different types and sizes.