Methimazole and α-lipoic acid as metallo-ß-lactamases inhibitors.

The emergence of bacterial resistance poses a serious threat to public health. One of the most important resistance mechanisms against ß-lactam antibiotics is the production of metallo-ß-lactamases (MBLs). In this study, α-lipoic acid (LA) and methimazole (MMI), which have been used in clinical practice as non-antibacterial drugs and as a supplement, were chosen to explore their potential to be metallo-ß-lactamases inhibitors (MBLIs). Enzyme inhibition assays showed that LA and MMI had moderate inhibitory activity against NDM-1 but no activity against VIM-2 and IMP-7. Antibacterial assays to determine synergy, demonstrated that the combination of LA or MMI with meropenem (MER) reduced the MIC value of MER against NDM-1 producing E. coli 16 times and 4 times, respectively, lower than that of MER alone. The fractional inhibitory concentration index (FICI) values were calculated to be less than 0.5, indicating that both LA and MMI had synergistic antibacterial effects with MER against all three MBLs expressing E. coli strains. The time-kill studies also suggested that LA and MMI were effective in restoring the antibacterial effect of MER. These findings revealed that LA and MMI are potential carbapenem enhancers, and provide a starting point for the development of potent MBLIs.

Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis.

The rapid and nondestructive determination of active compositions in Chrysanthemum morifolium (Hangbaiju) is of great value for producers and consumers. Hyperspectral imaging as a rapid and nondestructive technique was used to determine total polysaccharides and total flavonoids content in Chrysanthemum morifolium. Hyperspectral images of different sizes of Chrysanthemum morifolium flowers were acquired. Pixel-wise spectra within all samples were preprocessed by wavelet transform (WT) followed by standard normal variate (SNV). Partial least squares (PLS) and least squares-support vector machine (LS-SVM) were used to build prediction models using sample average spectra calculated by preprocessed pixel-wise spectra. The LS-SVM model performed better than the PLS models, with the determination of the coefficient of calibration (R²c) and prediction (R²p) being over 0.90 and the residual predictive deviation (RPD) being over 3 for total polysaccharides and total flavonoids content prediction. Prediction maps of total polysaccharides and total flavonoids content in Chrysanthemum morifolium flowers were successfully obtained by LS-SVM models, which exhibited the best performances. The overall results showed that hyperspectral imaging was a promising technique for the rapid and accurate determination of active ingredients in Chrysanthemum morifolium, indicating the great potential to develop an online system for the quality determination of Chrysanthemum morifolium.