Adipose Tissue Secretion Pattern Influences β-Cell Wellness in the Transition from Obesity to Type 2 Diabetes

The dysregulation of the β-cell functional mass, which is a reduction in the number of β-cells and their ability to secure adequate insulin secretion, represents a key mechanistic factor leading to the onset of type 2 diabetes (T2D). Obesity is recognised as a leading cause of β-cell loss and dysfunction and a risk factor for T2D. The natural history of β-cell failure in obesity-induced T2D can be divided into three steps: (1) β-cell compensatory hyperplasia and insulin hypersecretion, (2) insulin secretory dysfunction, and (3) loss of β-cell mass. Adipose tissue (AT) secretes many hormones/cytokines (adipokines) and fatty acids that can directly influence β-cell function and viability. As this secretory pattern is altered in obese and diabetic patients, it is expected that the cross-talk between AT and pancreatic β-cells could drive the maintenance of the β-cell integrity under physiological conditions and contribute to the reduction in the β-cell functional mass in a dysmetabolic state. In the current review, we summarise the evidence of the ability of the AT secretome to influence each step of β-cell failure, and attempt to draw a timeline of the alterations in the adipokine secretion pattern in the transition from obesity to T2D that reflects the progressive deterioration of the β-cell functional mass.     

Oxidative coupling of methane over strontium-doped neodymium oxide: Parametric evaluations

Since its discovery in 1982, oxidative coupling of methane (OCM) has been considered one of the most promising approaches for the on-purpose synthesis of ethylene. The development of more selective catalysts is essential to improve process economics. In this work, undoped neodymium oxide as well as neodymium oxide doped with high (20%) and low (2.5%) levels of strontium were tested in a high-throughput fashion covering a wide range of operating conditions. The catalysts were shown to be able to achieve greater than 18% C₂+ yield. Space velocity was shown to play a significant role in C₂+ selectivity. For a methane to oxygen feed ratio of 3.5, selectivity increased with increasing space velocity, reaching a maximum of 62% at a methane conversion of 30% at an optimal space velocity of ~250,000 ml/h/g. The difference in activity between the three samples was linked to the contribution of different oxygen centers.     

An accurate approach to real-time machine-readable zone detection with mobile devices

International Journal on Document Analysis and Recognition (IJDAR) - In this article we consider a problem of machine-readable zone (MRZ) detection in document images on mobile devices. MRZ...     

Hydrophobized Hydrogels: Construction Strategies,Properties, and Biomedical Applications

Hydrogels, as 3D networks containing huge amount of water, display similarity to soft tissues, and thus they are of wide interest in tissue engineering. Hydrogels, due to biocompatibility and porous structure, are valuable therapeutic platforms for hydrophilic drugs. Over the last decade, there has been a strong emphasis on the development of hydrogel platforms with the ability to increase the solubility of hydrophobic drugs. However, the pronounced discrepancy between the hydrophilic character of hydrogels and the hydrophobic nature of numerous pharmacologically active compounds is problematic. In recent years, different strategies are applied using special polymer constructs or composite materials exploiting the advanced scientific knowledge in the area of polymer and lipid-based nano- and microcarriers hydrophobization of the hydrogel turns out to be not only valuable in terms of achieving the ability to dissolve poorly soluble drugs in water, but also proves to be crucial in obtaining bioadhesion in wet conditions, but also, unexpected abnormal water swelling behavior, as well as in mechanical properties such as the dissipation mechanism and self-healable hydrogel properties. This review is mainly focused on recent advances in the usage of hydrophobized hydrogels in biomedical applications.     

Acridine/Acridone–Carborane Conjugates as Strong DNA-Binding Agents with Anticancer Potential

Synthesis of acridine derivatives that act as DNA-targeting anticancer agents is an evolving field and has resulted in the introduction of several drugs into clinical trials. Carboranes can be of importance in designing biologically active compounds due to their specific properties. Therefore, a series of novel acridine analogs modified with carborane clusters were synthesized. The DNA-binding ability of these analogs was evaluated on calf thymus DNA (ct-DNA). Results of these analyses showed that 9-[(1,7-dicarba-closo-dodecaborane-1-yl)propylamino]acridine ( 30 ) interacted strongly with ct-DNA, indicating its ability to intercalate into DNA, whereas 9-[(1,7-dicarba-closo-dodecaborane-1-yl)propanamido]acridine ( 29 ) changed the B-form of ct-DNA to the Z form. Compound 30 demonstrated cytotoxicity, was able to inhibit cell proliferation, arrest the cell cycle in the S phase in the HeLa cancer cell line, and induced the production of reactive oxygen species (ROS). In addition, it was specifically localized in lysosomes and was a weak inhibitor of Topo IIα.     

Artificial neural network to predict the power number of agitated tanks fed by CFD simulations

The power consumption of the agitator is a critical variable to consider in the design of a mixing system. It is generally evaluated through a dimensionless number known as the power number $N_p$. Multiple empirical equations exist to calculate the power number based on the Reynolds number $Re$ and dimensionless geometrical variables that characterize the tank, the impeller, and the height of the fluid. However, correlations perform poorly outside of the conditions in which they were established. We create a rich database of 100 k computational fluid dynamics (CFD) simulations. We simulate paddle and pitched blade turbines in unbaffled tanks from $Re$ 1 to 100 and use an artificial neural network (ANN) to create a robust and accurate predictor of the power number. We perform a mesh sensitivity analysis to verify the precision of the $N_p$ values given by the CFD simulations. To sample the 100 k mixers by their geometrical and physical properties, we use the Latin hypercube sampling (LHS) method. We then normalize the data with a MinMax transformation to put all features in the same scale and thus avoid bias during the ANN's training. Using a grid search cross-validation, we find the best architecture of the ANN that prevents overfitting and underfitting. Finally, we quantify the performance of the ANN by extracting 30% of the database, predicting the $N_p$ using the ANN, and evaluating the mean absolute percentage error. The mean absolute error in the ANN prediction is 0.5%, and its accuracy surpasses correlations even for untrained geometries.