Effect of Bakumondo-to on cytochrome P450 activities in rat liver microsomes

AbstractBakumondo-to is a traditional herbal medicine. It has been widely used for the treatment of chronic airway diseases. Recently, it was reported that several herbal medicines affected cytochrome P450 (CYP). However, there is little information about the effects of Bakumondo-to on CYP activities. In this study, we evaluated the effects of Bakumondo-to on CYP activities in rat liver microsomes. Rats were orally treated twice a day with Bakumondo-to at doses of 2.0g/kg body weight/day for 4days. CYP activities were determined in liver microsomes isolated from treated rats. CYP1A2, CYP2C, and CYP3A activities were measured using their specific substrates [7-methoxyresorufin, 7-methoxy-4-(trifluoromethyl)-coumarin, and 7-benzyloxyquinoline, respectively]. Bakumondo-to decreased CYP1A2 activity by 42.5±7.8%, increased CYP2C activity by 158.0±29.6%, and decreased CYP3A activity to 81.5±7.8% of the control level. Activities were expressed as percentages of the control.Bakumondo-to induced CYP2C activity and decreased CYP1A2 activity; it may cause drug-herbal interactions. It is suggested that combinations of Bakumondo-to and drugs that are metabolized by CYP1A2 and CYP2C should be carefully used in clinical settings

Validating Variational Bayes Linear Regression Method With Multi-Central Datasets.

PurposeTo validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset.MethodThe training dataset consisted of 7268 eyes of 4278 subjects from the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2-4) to 2nd to 10th VF (VF2-10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic.ResultsOLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2-4 to VF2-10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2-2 to VF2-10) (P > 0.05).ConclusionsVBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings

Identification of neochlorogenic acid as the predominant antioxidant in Polygonum cuspidatum leaves

To identify the predominant antioxidant compound in Polygonum cuspidatum leaves, the methanol extract of fresh samples were separated by liquid–liquid partitioning, octadecylsilyl Sep-pak®cartridge and high-performance liquid chromatography. The main active compound was identified as (1R,3R,4S,5R)-3-{[(2E)-3-(3,4-dihydroxyphenyl)-2-propenoyl]oxy}-1,4,5-trihydroxycyclohexanecarboxylic acid (neochlorogenic acid) by nuclear magnetic resonance and liquid chromatographymassspectroscopic analysis. Its content was found to be 2.31 mg/g of fresh leaves. As shown by 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical and superoxide anion scavenging assays, the contributionsof neochlorogenic acid as an antioxidant were 16.5% and 36.5%, respectively, suggesting that neochlorogenic acid is the predominant antioxidant in P. cuspidatum leaves

Validating Variational Bayes Linear Regression Method With Multi-Central Datasets.

PurposeTo validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset.MethodThe training dataset consisted of 7268 eyes of 4278 subjects from the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2-4) to 2nd to 10th VF (VF2-10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic.ResultsOLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2-4 to VF2-10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2-2 to VF2-10) (P > 0.05).ConclusionsVBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings