RESUMO
OBJECTIVE： To establish a method for simultaneous determination of 7 kinds of anti-rheumatic active ingredients in Mongolian medicine Sendeng-4 decoction powder， such as catechin， jasminoidin， dihydromyricetin， texifolin， rutin， myricetin and quercetin. METHODS： HPLC-DAD method was adopted. The determination was performed on Sil Green C18 column with mobile phase consisted of acetonitrile-0.1％ phosphoric acid solution （gradient elution） at the flow rate of 1 mL/min. The detection wavelength was set at 238 nm， and column temperature was 30 ℃. The sample size was 10 μL. RESULTS： The linear range of catechin， jasminoidin， dihydromyricetin， texifolin， rutin， myricetin and quercetin were 8.590-2 290， 10.56-3 901， 13.00-3 958， 8.815-564.2， 4.030-257.8， 8.130-750.5， 7.075-454.2 μg/mL （r≥0.999 1）， respectively； the limits of detection were 0.429 5， 0.264 0， 0.325 0， 0.220 4， 0.201 5， 0.203 2， 0.176 9 μg/mL， respectively； the limits of quantification were 1.030， 1.321， 1.302， 1.397， 1.637， 0.813 0， 0.707 5 μg/mL， respectively. RSDs of precision， stability （48 h） and repetition tests were all lower than 2.0％ （n＝6）. The average recoveries were 96.24％-99.28％ （RSD＝1.03％-1.63％， n＝6）. CONCLUSIONS： The established method is simple， accurate and reproducible， and can be used for simultaneous determination of catechin， jasminoidin， dihydromyricetin， texifolin， rutin， myricetin and quercetin in Mongolian medicine Sendeng-4 decoction powder.
RESUMO
Network modularity is an important structural feature in metabolic networks. A previous study suggested that the variability in natural habitat promotes metabolic network modularity in bacteria. However, since many factors influence the structure of the metabolic network, this phenomenon might be limited and there may be other explanations for the change in metabolic network modularity. Therefore, we focus on archaea because they belong to another domain of prokaryotes and show variability in growth conditions (e.g., trophic requirement and optimal growth temperature), but not in habitats because of their specialized growth conditions (e.g., high growth temperature). The relationship between biological features and metabolic network modularity is examined in detail. We first show the absence of a relationship between network modularity and habitat variability in archaea, as archaeal habitats are more limited than bacterial habitats. Although this finding implies the need for further studies regarding the differences in network modularity, it does not contradict previous work. Further investigations reveal alternative explanations. Specifically, growth conditions, trophic requirement, and optimal growth temperature, in particular, affect metabolic network modularity. We have discussed the mechanisms for the growth condition-dependant changes in network modularity. Our findings suggest different explanations for the changes in network modularity and provide new insights into adaptation and evolution in metabolic networks, despite several limitations of data analysis.
