N-Substituted pyrimidinethione and acetophenone derivatives as a new therapeutic approach in diabetes

In this study, compounds with 4-hydroxybutyl, 4-phenyl, 5-carboxylate, and pyrimidine moieties were determined as α-glycosidase inhibitors. N-Substituted pyrimidinethione and acetophenone derivatives ( A1 – A5 , B1 – B11 , and C1 – C11 ) were good inhibitors of the α-glycosidase enzyme, with K_i values in the range of 104.27 ± 15.75 to 1,004.25 ± 100.43 nM. Among them, compound B7 was recorded as the best inhibitor, with a K_i of 104.27 ± 15.75 nM against α-glycosidase. In silico studies were carried out to clarify the binding affinity and interaction mode of the compounds with the best inhibition score against α-glycosidase from Saccharomyces cerevisiae. Compounds B7 (S) and B11 (R) exhibited a good binding affinity with docking scores of −8.608 and 8.582 kcal/mol, respectively. The docking results also showed that the 4-hydroxybutyl and pyrimidinethione moieties play a key role in S. cerevisiae and human α-glycosidase inhibition.     

Permissive role of cytosolic pH acidification in neurodegeneration: A closer look at its causes and consequences

The maintenance of cytosolic pH in its physiological range is required for normal neuronal activity, and even minor alterations can have serious consequences. This Review summarizes the current understanding of the conditions that are associated with cytosolic pH disruption and that lead to abnormal cytosolic acidification. Oxidative stress results in cytosolic acidification, and this plays a crucial role in the emergence of apoptosis in protein misfolding and excitotoxicity, ultimately leading to irreversible neuronal damage. Through the identification of mechanisms by which intraneuronal pH acidification promotes neurodegeneration, we may identify new approaches for preventing and treating neurodegenerative disorders. © 2016 Wiley Periodicals, Inc.     

Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete

Numerous tests are used to determine the performance of concrete, but compressive strength (CS) is usually regarded as the most important. The recycled aggregate concrete (RAC) exhibits lower CS compared to natural aggregate concrete. Several variables, such as the water-cement ratio, the strength of the parent concrete, recycled aggregate replacement ratio, density, and water absorption of recycled aggregate, all impact the RAC’s CS. Many studies have been carried out to ascertain the influence of each of these elements separately. However, it is difficult to investigate their combined effect on the CS of RAC experimentally. Experimental investigations entail casting, curing, and testing samples, which require considerable work, expense, and time. It is vital to adopt novel methods to the stated aim in order to conduct research quickly and efficiently. The CS of RAC was predicted in this research utilizing machine learning techniques like decision tree, gradient boosting, and bagging regressor. The data set included eight input variables, and their effect on the CS of RAC was evaluated. Coefficient correlation (R²), the variance between predicted and experimental outcomes, statistical checks, and k-fold evaluations, were carried out to validate and compare the models. With an R² of 0.92, the bagging regressor technique surpassed the decision tree and gradient boosting in predicting the strength of RAC. The statistical assessments also validated the superior accuracy of the bagging regressor model, yielding lower error values like mean absolute error (MAE) and root mean square error (RMSE). MAE and RMSE values for the bagging model were 4.258 and 5.693, respectively, which were lower than the other techniques employed, i.e., gradient boosting (MAE = 4.956 and RMSE = 7.046) and decision tree (MAE = 6.389 and RMSE = 8.952). Hence, the bagging regressor is the best suitable technique to predict the CS of RAC.     

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model

The depletion of natural resources of river sand and its availability issues as a construction material compelled the researchers to use manufactured sand. This study investigates the compressive strength of concrete made of manufactured sand as a partial replacement of normal sand. The prediction model, i.e., gene expression programming (GEP), was used to estimate the compressive strength of manufactured sand concrete (MSC). A database comprising 275 experimental results based on 11 input variables and 1 target variable was used to train and validate the developed models. For this purpose, the compressive strength of cement, tensile strength of cement, curing age, D_max of crushed stone, stone powder content, fineness modulus of the sand, water-to-binder ratio, water-to-cement ratio, water content, sand ratio, and slump were taken as input variables. The investigation of a varying number of genetic characteristics, such as chromosomal number, head size, and gene number, resulted in the creation of 11 alternative models (M1-M11). The M5 model outperformed other created models for the training and testing stages, with values of (4.538, 3.216, 0.919) and (4.953, 3.348, 0.906), respectively, according to the results of the accuracy evaluation parameters root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). The R² and error indices values revealed that the experimental and projected findings are in extremely close agreement. The best model has 200 chromosomes, 8 head sizes, and 3 genes. The mathematical expression achieved from the GEP model revealed that six parameters, namely the compressive and tensile strength of cement, curing period, water–binder ratio, water–cement ratio, and stone powder content contributed effectively among the 11 input variables. The sensitivity analysis showed that water–cement ratio (46.22%), curing period (25.43%), and stone powder content (13.55%) were revealed as the most influential variables, in descending order. The sensitivity of the remaining variables was recorded as w/b (11.37%) > f_ce (2.35%) > f_ct (1.35%).     

Polyesters bearing furan moieties, 2. A detailed investigation of the polytransesterification of difuranic diesters with different diols

A two-step transesterification procedure was applied to combinations of difuranic diesters and both aliphatic and furanic diols. The reaction parameters (including the nature of the catalyst) were varied in both phases of the process and the results compared with those published for similar systems based on aromatic diesters. The best results related to the first phase of the synthesis were obtained using Zn(AcO)₂, Pb(AcO)₂ or Ti(OBu)₄ at 200°C with a large excess of diol. The second phase, which led to the actual polymer at 200–240°C, called upon the catalytic action of SnC₂O₄, Sb₂O₃ and Ti(OBu)₄ and was prolonged until the viscosity of the media ceased to increase. Specific problems, related to some fragile moieties, limited the success of these polymerizations to a number of combinations which gave polyesters bearing regular structures and molecular weights in the tens of thousands.