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Background: Thiazoles are an important class of heterocyclic compounds with many biological effects, including anticholinesterase activity. Objective: The purpose of this work was to synthesize new thiazole derivatives and evaluate as acetylcholinesterase inhibitors (AChEIs) for Alzheimer’s disease. Methods: A series of new N-acyl-4-(4-aminoalkoxy-phenyl)-thiazole-2-amine derivatives was designed and synthesized. Ellman assay protocol was used for the AchE and BuChE inhibitory activity. To correlate better the drug-like property, the theoretical prediction was calculated using Mol inspiration software 2015 online. The potential binding mode of compounds with AChE and BuChE was investigated by the molecular docking simulation. Results: All synthesized compounds exhibited a certain inhibitory activity on AChE and 5p had the most effective selective inhibitory effect on AChE. The inhibitory form of 5p on AChE was shown to be a combination of competitive and noncompetitive inhibition, according to enzyme kinetic tests. Docking simulation studies revealed that the binding energy of 5p with AChE was lower than that of it with BuChE, which also explained the selective inhibitory activity of 5p on AChE. Conclusion: These results provided valuable information for the design of potent AChEIs, and it was believed that 5p could be a promising lead structure for its further development for the treatment of AD.

Abstract:: β-Nicotinamide mononucleotide (β-NMN), a key precursor in the biosynthesis of nicotinamide adenine dinucleotide (NAD+) in mammals, has significant effects in replenishing NAD+ levels in the body, so it has obvious ameliorative effects on metabolic and age-related degenerative diseases. β-NMN is widely used in healthcare products, food, and cosmetics. It has considerable commercial worth and promising medical application prospects. Hence, the development of methods for preparing β-NMN is of great research significance. This review summarized and analyzed recent developments in the chemical synthesis of β-NMN from various starting materials, which could provide helpful references for the investigation of new synthetic techniques for β-NMN and encourage its further development and large-scale application.

Background: Intensive care unit (ICU) resources are inadequate for the large population in China, so it is essential for physicians to evaluate the condition of patients at admission. In this study, our objective was to construct a machine-learning risk prediction model for mortality in respiratory intensive care units (RICUs). Methods: This study involved 817 patients who made 1,063 visits and who were admitted to the RICU from 2012 to 2017. Potential predictors such as demographic information, laboratory results, vital signs and clinical characteristics were considered. We constructed eXtreme Gradient Boosting (XGBoost) models and compared performances with random forest models, logistic regression models and clinical scores such as Acute Physiology and Chronic Health Evaluation II (APACHE II) and the sequential organ failure assessment (SOFA) system. The model was externally validated using data from Medical Information Mart for Intensive Care (MIMIC-III) database. A web-based calculator was developed for practical use. Results: Among the 1,063 visits, the RICU mortality rate was 13.5%. The XGBoost model achieved the best performance with the area under the receiver operating characteristics curve (AUROC) of 0.860 (95% confidence interval (CI): 0.808 - 0.909) in the test set, which was significantly greater than APACHE II (0.749, 95% CI: 0.674 - 0.820; P = 0.015) and SOFA (0.751, 95% CI: 0.669 - 0.818; P = 0.018). The Hosmer-Lemeshow test indicated a good calibration of our predictive model in the test set with a P-value of 0.176. In the external validation dataset, the AUROC of XGBoost model was 0.779 (95% CI: 0.714 - 0.813). The final model contained variables that were previously known to be associated with mortality, but it also included some features absent from the clinical scores. The mean N-terminal pro-B-type natriuretic peptide (NTproBNP) of survivors was significantly lower than that of the non-survival group (2066.43 pg/mL vs. 8232.81 pg/mL; P < 0.001). Conclusions: Our results showed that the XGBoost model could be a suitable model for predicting RICU mortality with easy-to-collect variables at admission and help intensivists improve clinical decision-making for RICU patients. We found that higher NT-proBNP can be a good indicator of poor prognosis.

Background: Due to its effective osteogenic ability, BMP9 is a promising candidate for bone regeneration medicine. Whereas, BMP9 can also induce adipogenesis simultaneously. LCN2 is a cytokine associated with osteogenesis and adipogenesis. Reducing the adipogenic potential may be a feasible measure to enhance the osteogenic capability of BMP9. Objective: The objective of the study was to explore the role of LCN2 in regulating the BMP9-initialized osteogenic and adipogenic differentiation in mouse embryonic fibroblasts (MEFs), and clarify the possible underlying mechanism. Methods: Histochemical stain, western blot, real-time PCR, laser confocal, immunoprecipitation, cranial defect repair, and fetal limb culture assays were used to evaluate the effects of LCN2 on BMP9-induced osteogenic and adipogenic differentiation, as well as Wnt/β-catenin signaling. Results: LCN2 was down-regulated by BMP9. The BMP9-induced osteogenic markers were inhibited by LCN2 overexpression, but the adipogenic markers were increased; LCN2 knockdown exhibited opposite effects. Similar results were found in bone defect repair and fetal limb culture tests. The level of β-catenin nucleus translocation was found to be reduced by LCN2 overexpression, but increased by LCN2 knockdown. The inhibitory effect of LCN2 overexpression on the osteogenic capability of BMP9 was reversed by β-catenin overexpression; whereas, the effect of LCN2 knockdown on promoting BMP9 osteogenic potential was almost eliminated by β-catenin knockdown. LCN2 could bind with LRP6 specifically, and the inhibitory effect of LCN2 on the osteogenic potential of BMP9 could not be enhanced by LRP6 knockdown. Conclusion: LCN2 inhibits the BMP9-induced osteogenic differentiation but promotes its adipogenic potential in MEFs, which may be partially mediated by reducing Wnt/β-catenin signaling via binding with LRP6.

Background: The treatment of diabetes with plant ingredients such as in Traditional Chinese Medicine (TCM) is an alternative to classical chemotherapy. Objective: This study aims to explore the hypoglycemic effect of Trichosanthes pericarpium powder (TP) and intestinal bacteria transplantation in type 2 diabetic mice. The relationship between intestinal bacteria transplantation and improvement in insulin resistance was also investigated. Methods: The polyphenols and terpenoids in the TP were identified by LC-MS/MS. Streptozotocin was used to induce a mouse model of type 2 diabetes. Diabetic mice were treated with different doses of TP and the intestinal bacteria obtained from the high-dose TP group for four weeks. Results: As a result, FBG levels were found to be significantly reduced in diabetic mice, weight gain and organ enlargement were alleviated, and insulin resistance was significantly improved. TP administration also improved the disorder in intestinal bacteria in diabetic mice. Besides, TP can increase the liver AMPK, SIRT1, GLUT1, and GLUT4 gene expression, while down-regulated PEPCK and G6Pase gene expression suggest a potential mechanism for hypoglycemia in diabetic mice. Conclusion: Oral administration of Trichosanthes pericarpium powder in the treatment of diabetes may be achieved by restoring hepatic function, improving insulin resistance, and the dynamic balance of intestinal bacteria.