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β Ketodithioesters (KDEs) are versatile building blocks for the rapid construction of various heterocyclic compounds. Quite a good number of successful reactions based on KDEs have been developed in the past decade for the construction of heterocyclic skeletons under mild conditions. This review covers the new C-C/X bond formation and annulation reactions of KDEs with dielectrophilic or dinucleophilic reagents. Multicomponent reactions using KDEs to construct various heterocycles are also the major contents in this review. Objective The aim of this review is to bring a fresh perspective on the application of KDEs in organic synthesis covering from 2013 to 2020. Conclusion From this review, it is clear that KDEs have been the object of numerous studies on its use in heterocyclic synthesis. The presence of different functional groups on this synthon permits the incorporation of C-C/X sources into the final targets, which is the beauty of KDEs.Severe pressure from energy consumption and serious pollution from non-renewable resources have urged human beings to develop green and energy-efficient materials. Transparent wood, consisting of original wood channel structure filled with resins, has favorable environmental friendliness and high transparency and haze, which holds huge potential in various important fields. Herein, a brief review of the current research activities centering on the development of transparent wood is provided. This review begins with an introduction to the background of transparent wood. Next, the cellular wall structure of wood and the synthetic strategy of transparent wood (including decolorization and impregnation) are summarized. Furthermore, the functionalization of transparent wood through doping nanomaterials or modifying resins is highlighted, and the relationship between the physicochemical properties and the potential uses (like optoelectronics, building materials, and furniture decoration) of transparent wood are clarified. Finally, a brief overview of the prospects and challenges for transparent wood is provided.<br /><br /> Evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital at unraveling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome.<br /><br /> Eight bio-fluids ( 8 urine and 8 serum samples) obtained from consenting healthy controls (HC), twenty five (25) biofluids from first episode treatment naïve MDD (TNMDD) patients and twenty (22) biofluids from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients' samples was carried out using Partial least squares discriminant analysis (PLSDA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLSDA) models.<br /><br /> In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or major depressive disorder ptabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.Recent evidence has demonstrated that Sinomenine (SIN) exerts antitumor activity in vitro. However, the clinical utility of SIN remains limited mainly because of its poor bioavailability. Exosomes are nanoscale vesicles that play crucial roles in intracellular communications through functionally active substances such as DNA and RNA. Exosomes have been utilized as nanocarriers for targeted drug delivery of different anticancer drugs. The present study aimed to evaluate the effectiveness of combined Exosomes-SIN for treatment of hepatocellular carcinoma (HCC) in a rat model. To do so, we prepared a mixture of SIN and exosomes (Exo-SIN) to improve the bioavailability of SIN to treat liver cancer. The in vitro release profile of the Exo-SIN was examined. We observed a continuous, slow release of SIN from Exo-SIN in simulated body fluid as well as tumor microenvironment. In the cytotoxicity test, Exo-SIN exhibited a significantly stronger inhibition in HepG2 cells, compared to free SIN. The flow cytometry assessments showed that Exo-SIN could suppress HepG2 cell migration in a Transwell assay and induce cell cycle arrest and cellular apoptosis. Western blotting showed that survivin, a crucial protein for survival of living cells, was significantly downregulated after treatment with Exo-SIN. In conclusion, our data suggest that Exo-SIN could serve as a potential, effective delivery platform for hepatic carcinoma therapy.Cancer is a kind of disease that has scared many people for many years. [https://www.selleckchem.com/products/pf-07220060.html buy PF-07220060] Cancer is due to the excessive growth of cells in every particular part of the body. Oxadiazole 1,3,4 is a magical organic moiety that has anticancer potential. Various works on the 1,3,4-oxadiazoles moiety showing anticancer activity have been reported. The present analysis summarizes general synthetic methods for 1,3,4 oxadiazole. Different receptors on which these drug acts are discussed. Pharmacophore models are also presented in this review for topoisomerase-I, histone deacetylase, epidermal growth factor enzymes.Cancer is one of the major causes of death among human beings. Traditional treatments of cancer kill cancerous cells and negatively affect normal cells. The side effects and high medical costs prevent effective treatment. Recently, anticancer peptides have become potential therapeutic agents that are expected to assist the traditional treatments. Compared with the conventional wet-lab experiments, computation-based methods provide a promising sight for high-throughput identification of peptides that has an anticancer activity. We summarize the current available database of anticancer peptides/proteins. We survey 21 recently published in-silico methods that aim to accurately predict anticancer peptides. More specifically, we focus on the benchmark datasets, feature construction and feature selection, machine learning algorithms, assessment criteria, comparison of different methods, and publically available predictors. Finally, we propose several recommendations concerning the future development of database of anticancer peptides, and the methods that are used to predict anticancer peptides.