UVA Assisted 4-Thiothymidine for Cancer Treatment

This article reviews the developments of 4-thiothymidine analogues, assisted with UVA light, as a novel cancer therapy. First, the key points on synthetic chemistry, photochemistry and cellular toxicity of 4-thiothymidine are summarized. As the chemical structure of 4-thiothymidine is very similar to that of its parent thymidine, thus 4-thiothymidine can be readily incorporated into cellular DNA, and with the help of thymidine kinase, much more preferably into cancerous DNA. Unlike thymidine, 4-thiothymdine can absorb strongly in UVA (longer wavelengths of UV) light. Thus UVA-assisted 4-thiothymidine offers an effective cancer treatment. Some underlying mechanisms of action by 4-thiothymidine/UVA and compares this cancer approach with the commonly used photodynamic therapy are discussed. The various interactions between 4-thiothymidine with human serum albumin are introduced. Finally, a short conclusion on the past efforts and a brief prospect for future work in this exciting research field are given

Mechanical and Gas Barrier Properties of Poly(Lactic Acid) Modified by Blending with Poly(Butylene 2,5-Furandicarboxylate): Based on Molecular Dynamics

Three blends of Poly(butylene 2,5-furandicarboxylate) (PBF) and Poly(lactic acid) (PLA) blends were modeled using molecular dynamics simulations, with PBF contents of 10%, 20%, and 30%, respectively. The study investigated the compatibilities of the blends, as well as the mechanical and gas barrier properties of the composite systems. The molecular dynamics simulation results show that: (1) PLA and PBF have good compatibility in the blend system; (2) the optimal toughness modification was achieved with a 20% PBF content, resulting in a 17.3% increase in toughness compared to pure PLA; (3) the barrier properties of the blend for O2, CO2, and N2 increased when increasing the PBF content. Compared to pure PLA, the diffusion coefficients of the O2, CO2, and N2 of the blends with 30% PBF decreased by 75%, 122%, and 188%, respectively. Our simulation results are in good agreement with the actual experimental results

Wavelet‑based Bayesian approximate kernel method for high‑dimensional data analysis

Kernel methods are often used for nonlinear regression and classification in statistics and machine learning because they are computationally cheap and accurate. The wavelet kernel functions based on wavelet analysis can efficiently approximate any nonlinear functions. In this article, we construct a novel wavelet kernel function in terms of random wavelet bases and define a linear vector space that captures nonlinear structures in reproducing kernel Hilbert spaces (RKHS). Based on the wavelet transform, the data are mapped into a low-dimensional randomized feature space and convert kernel function into operations of a linear machine. We then propose a new Bayesian approximate kernel model with the random wavelet expansion and use the Gibbs sampler to compute the model’s parameters. Finally, some simulation studies and two real datasets analyses are carried out to demonstrate that the proposed method displays good stability, prediction performance compared to some other existing methods

Synthesis of N,N-Diethyldithiocarbamate Nitrile Ethyl and the Chelating Behaviors with Metal Ions

The N,N-diethyldithiocarbamate nitrile ethyl (NND) was the nonionic polar collector, and it can synthesize the NND in dimethyl sulfoxide solvent. This method can effectively reduce the reaction intensity and the coefficient of the synthetic risk. The purity of NND which we synthesized is 94.23%, and the yield is 91.06%. UV analysis shows that the characteristic absorption peak wavelength of the NND is 276 nm, and its absorbance is 0.901. Based on the interaction of NND + Mn+ (Mn+ = Fe3+, Cu2+, Zn2+, Pb2+) and the quantum chemical calculation analysis of the NND and ethyl xanthate, we can conclude that the flotation performance of NND should be better than that of ethyl xanthate