Surging footprints of mathematical modeling for prediction of transdermal permeability

Differential Expression Profiles and Potential Intergenerational Functions of tRNA-Derived Small RNAs in Mice After Cadmium Exposure

Cadmium (Cd) is a toxic heavy metal and ubiquitous environmental endocrine disruptor. Previous studies on Cd-induced damage to male fertility mainly focus on the structure and function of testis, including cytoskeleton, blood-testis barrier, and steroidogenesis. Nevertheless, to date, no studies have investigated the effects of Cd exposure on sperm epigenetic inheritance and intergenerational inheritance. In our study, we systematically revealed the changes in sperm tRNA-derived small RNAs (tsRNA) profiles and found that 14 tsRNAs (9 up-regulated and 5 down-regulated) were significantly altered after Cd exposure. Bioinformatics of tsRNA-mRNA-pathway interactions revealed that the altered biological functions mainly were related to ion transmembrane transport, lipid metabolism and cell membrane system. In addition, we focused on two stages of early embryo development and selected two organs to study the impact of these changes on cell membrane system, especially mitochondrion and lysosome, two typical membrane-enclosed organelles. Surprisingly, we found that the content of mitochondrion was significantly decreased in 2-cell stage, whereas remarkably increased in the morula stage. The contents of mitochondrion and lysosome were increased in the testes of 6-day-old offspring and livers of adult offspring, whereas remarkably decreased in the testes of adult offspring. This provides a possible basis to further explore the effects of paternal Cd exposure on offspring health.

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.