The combination therapy of targeting both paclitaxel and Dendrophthoe pentandra leaves extract nanoparticles for improvement breast cancer treatment efficacy by reducing TUBB3 and MAP4 expressions.

The aim of this study is to investigate the combination treatments of paclitaxel and chitosan-Dendrophthoe pentandra leaves extract nanoparticles (NPDP) on MCF-7 breast cancer cells. Chitosan-NPDP nanoparticles were characterized by Fourier-transform infrared (FTIR), scanning electron microscopy (SEM), and assessed by using immunofluorescence microscopy. MCF-7 cells are cultured and divided into six groups: group 1 was a negative control (without paclitaxel or NPDP); group 2 was treated with paclitaxel alone; groups 3-5 were treated with NPDP (2, 4, and 8 mg/mL, respectively) and group 6 was treated only by 8 mg/mL of chitosan-NPDP nanoparticles. The proliferation and cell cycle were analyzed by flow cytometry and the expression of TUBB3 and MAP4 were assessed by immunofluorescence microscopy. The combinations of paclitaxel-NPDP significantly inhibit proliferation of cells (P<0.001) and it is able to induce G2/M cell cycle arrest (P<0.001). The combination of paclitaxel-NPDP significantly decreases the expressions of TUBB3 (P<0.001) and MAP4 (P<0.001) in MCF-7 cells. These results indicate that the combination of NPDP nanoparticles could reduce the expressions of TUBB3 and MAP4. This research may provide possible sources of new therapy for NPDP.

East Asian Genome-wide association study derived loci in relation to type 2 diabetes in the Han Chinese population.

Meta-analysis of GWAS in East Asian populations had established 10 loci that were associated with type 2 diabetes. Eight of them were with genome-wide significance and two with a border line association. Since these data have not been studied in an independent Han Chinese population, we aimed to investigate the association of these susceptibility loci with type 2 diabetes in an independent Han Chinese population. We executed a case-control study in 2 000 Chinese by the SNPscan method. Firstly, the repetitive sequences of 10 loci were assessed. Next, we investigated the association of 8 SNPs out of 10 with type 2 diabetes and constructed the GRS of those 8 SNPs. Finally, the relationship of the 8 loci and diabetes-related traits was analyzed. Based on the fact, that highly repetitive sequences were detected in 2 SNPs, we investigated the remaining 8 SNPs. With the exception of four SNPs (CMIP rs16955379, PEPD rs3786897, PSMD6 rs831571, ZFAND3 rs9470794), the other SNPs had the same direction of effect (odds ratio [OR]>1.0) as in the original reports, especially GLIS3 rs7041847 and KCNK16 rs1535500 were significantly associated with type 2 diabetes (rs1535500: p=0.005, OR=1.224, 95% CI 1.062-1.409; rs7041847: p=0.035, OR=1.118, 95% CI 1.070-1.388). The GRS constructed from the 8 SNPs was significantly associated with type 2 diabetes in the Chinese population (p=0.004, OR=1.065, 95% CI: 1.021-1.111). Among the participants with 24≤BMI<28 kg/m2 the 8 SNPs were significantly associated with type 2 diabetes (p=0.040, OR=1.079, 95% CI: 1.003-1.160). In quantitative trait analyses, WWOX rs17797882 was associated with decreased HOMA-ß and increased level of TG and HDL-Ch, while PEPD rs3786897 and MAEA rs6815464 were associated with decreased fasting plasma glucose, and KCNK16 rs1535500 has shown a significant association with increased T-Ch and PSMD6 rs831571 had a significant association with decreased HDL-Ch. In Conclusion, with high probability the 8 loci identified in the East Asian GWAS meta-analysis are associated with type 2 diabetes in the Han Chinese population.

Analysis of Flavonoid in Medicinal Plant Extract Using Infrared Spectroscopy and Chemometrics.

Infrared (IR) spectroscopy combined with chemometrics has been developed for simple analysis of flavonoid in the medicinal plant extract. Flavonoid was extracted from medicinal plant leaves by ultrasonication and maceration. IR spectra of selected medicinal plant extract were correlated with flavonoid content using chemometrics. The chemometric method used for calibration analysis was Partial Last Square (PLS) and the methods used for classification analysis were Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogies (SIMCA), and Support Vector Machines (SVM). In this study, the calibration of NIR model that showed best calibration with R (2) and RMSEC value was 0.9916499 and 2.1521897, respectively, while the accuracy of all classification models (LDA, SIMCA, and SVM) was 100%. R (2) and RMSEC of calibration of FTIR model were 0.8653689 and 8.8958149, respectively, while the accuracy of LDA, SIMCA, and SVM was 86.0%, 91.2%, and 77.3%, respectively. PLS and LDA of NIR models were further used to predict unknown flavonoid content in commercial samples. Using these models, the significance of flavonoid content that has been measured by NIR and UV-Vis spectrophotometry was evaluated with paired samples t-test. The flavonoid content that has been measured with both methods gave no significant difference.