Antimicrobial activity of nutmeg against Escherichia coli O157

We examined the difference between Escherichia coli O157 and non-pathogenic E. coli in their tolerance to spices. Various spices (5 g each) were homogenized at 25 degrees C for 10 min with 5 ml of 70% ethyl alcohol, and the supernatant solutions obtained by centrifugation were used as spice extracts. When the E. coli strains were incubated with each spice extract at concentrations of 0.01% and 0.1%, a noteworthy difference was observed between the O157 and non-pathogenic strains in their tolerance to nutmeg. The populations of the non-pathogenic strains could not be reduced, but those of the O157 strains were remarkably reduced. Antibacterial activity by the nutmeg extract was also found against the enteropathogenic E. coli O111, but not against enterotoxigenic (O6 and O148) and enteroinvasive (O29 and O124) E. coli. When we examined the antibacterial effect of volatile oils in nutmeg on the O157 and non-pathogenic E. coli strains, all O157 strains tested were found to be more sensitive to beta-pinene than non-pathogenic E. coli strains.     

Effects of dietary α-lipoic acid enantiomers on hepatic fatty acid metabolism in rats

Effects of dietary α-lipoic acid enantiomers on hepatic and serum lipid concentrations and the activity and mRNA levels of enzymes involved in hepatic fatty acid metabolism were examined in rats. Rats were fed experimental diets containing 0 or 2.5 g/kg of α-lipoic acid enantiomers (a racemic mixture of R/S-, S- or R-α-lipoic acid) for 21 days. Various α-lipoic acid preparations decreased serum and liver concentrations of triacylglycerol, and also lowered serum concentrations of cholesterol and phospholipid and the concentration of cholesterol in the liver. α-Lipoic acid decreased the activity and mRNA levels of various hepatic lipogenic enzymes and also decreased the activities of carnitine acyltransferase and 3-ketoacyl-CoA thiolase and mRNA levels of carnitine palmitoyltransferase 1a involved in fatty acid oxidation in the liver; however, R- and S-enantiomers of α-lipoic acid as well as well as a racemic mixture of R/S-α-lipoic acid were indistinguishable in altering these indices for lipid metabolism.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.