Structure-inhibition relationship of phenylethanoid glycosides on angiotensin-converting enzyme using ultra-performance liquid chromatography-tandem quadrupole mass spectrometry

Angiotensin-converting enzyme (ACE) plays a critical role in rennin-angiotensin system. Recently, natural products isolated from herbal medicines revealed inhibitory effects against ACE which suggested their potential activities in regulating blood pressure. In this study, ACE inhibition (ACEI) of 21 phenylethanoid glycosides and related phenolic compounds were investigated by measuring the production of HA a rapid, sensitive, accurate and specific ultra-performance liquid chromatography-tandem quadrupole mass spectrometry (UPLC-MS/MS) method. The test compounds showed different inhibitory potencies on ACE ranging from 5.29 to 95.01% at 50 mM, and the compounds with ACEI higher than 50% were selected for further IC50 determination. The IC50 values were from 0.53 ± 0.04 to 15.035 ± 0.036 mM. The structure-inhibition relationship were then explored and the result showed that cinnamoyl groups played an essential role in ACEI of phenylethanoid glycosides. Furthermore, the sub-structures of increasing ACEI for phenylethanoid glycosides is more hydroxyls and less steric hindrance to chelate the active site Zn2+ of ACE. In summary, our results suggested that phenylethanoid glycosides are a widely available source of anti-hypertensive natural products and the information provided from structure-inhibition relationship study could aid the design of structurally modified phenylethanoid glycosides as anti-hypertensive drugs.

Fractionating Human Intelligence

What makes one person more intellectually able than another? Can the entire distribution of human intelligence be accounted for by just one general factor? Is intelligence supported by a single neural system? Here, we provide a perspective on human intelligence that takes into account how general abilities or ‘‘factors’’ reflect the functional organiza- tion of the brain. By comparing factor models of individual differences in performance with factor models of brain functional organization, we demon- strate that different components of intelligence have their analogs in distinct brain networks. Using simulations based on neuroimaging data, we show that the higher-order factor ‘‘g’’ is accounted for by cognitive tasks corecruiting multiple networks. Finally, we confirm the independence of these com- ponents of intelligence by dissociating them using questionnaire variables. We propose that intelli- gence is an emergent property of anatomically distinct cognitive systems, each of which has its own capacity.