Analytic Model for DBF Under Multiple Particle Retention Mechanisms

Discrepancies between classical model (CM) predictions and experimental data for deep bed filtration (DBF) have been reported by various authors. In order to understand these discrepancies, an analytic continuum model for DBF is proposed. In this model, a filter coefficient is attributed to each distinct retention mechanism (straining, diffusion, gravity interception, etc.). It was shown that these coefficients generally cannot be merged into an effective filter coefficient, as considered in the CM. Furthermore, the derived analytic solutions for the proposed model (PM) were applied for fitting experimental data, and a very good agreement between experimental data and PM predictions were obtained. Comparison of the obtained results with empirical correlations allowed identifying the dominant retention mechanisms. In addition, it was shown that the larger the ratio of particle to pore sizes, the more intensive the straining mechanism and the larger the discrepancies between experimental data and CM predictions. Finally, the CM and PM were compared via statistical analysis. The obtained $p$ values allow concluding that the PM should be preferred especially when straining plays an important role.     

In Silico Identification of Tripeptides as Lead Compounds for the Design of KOR Ligands

The kappa opioid receptor (KOR) represents an attractive target for the development of drugs as potential antidepressants, anxiolytics and analgesics. A robust computational approach may guarantee a reduction in costs in the initial stages of drug discovery, novelty and accurate results. In this work, a virtual screening workflow of a library consisting of ~6 million molecules was set up, with the aim to find potential lead compounds that could manifest activity on the KOR. This in silico study provides a significant contribution in the identification of compounds capable of interacting with a specific molecular target. The main computational techniques adopted in this experimental work include: (i) virtual screening; (ii) drug design and leads optimization; (iii) molecular dynamics. The best hits are tripeptides prepared via solution phase peptide synthesis. These were tested in vivo, revealing a good antinociceptive effect after subcutaneous administration. However, further work is due to delineate their full pharmacological profile, in order to verify the features predicted by the in silico outcomes.     

Redox-active nickel in carbon nanotubes and its direct determination

The presence of residual metal-catalyst impurities in carbon nanotubes is responsible for their toxicity. It is important to differentiate between the total amount of impurities and the redox-active (bioavailable) amount of such impurities because only the bioavailable impurities exhibit toxic effects. Herein, we report a simple and specific method for quantifying the amount of redox-active Ni present in various commercial samples of CNTs. It is based on the electrochemical oxidation of Ni(OH)(2) that is formed in alkaline solutions when Ni impurities are opened to the surrounding environment. Metallic Ni impurities play an extremely active role in toxicological assays as well as in undesired catalytic processes, and thus a method to rapidly quantify the amount of redox-active Ni is of great importance.     

Resveratrol-derived stilbenoids and biological activity evaluation of seed extracts of Cenchrus echinatus L

A phytochemical study of the ethyl acetate fractions from the partition of seeds and roots methanol extracts of Cenchrus echinatus L. led to the isolation of three resveratrol-derived stilbenoids: pallidol (1), carasiphenol C (2) and nepalensinol B (3). The results of a topic anti-inflammatory evaluation, DPPH assay and antiproliferative activity against adenocarcinoma cells (Caco 2) are described.     

Mean-field cooperativity in chemical kinetics

We consider cooperative reactions and we study the effects of the interaction strength among the system components on the reaction rate, hence realizing a connection between microscopic and macroscopic observables. Our approach is based on statistical mechanics models and it is developed analytically via mean-field techniques. First of all, we show that, when the coupling strength is set positive, a cooperative behavior naturally emerges from the model; in particular, by means of various cooperative measures previously introduced, we highlight how the degree of cooperativity depends on the interaction strength among components. Furthermore, we introduce a criterion to discriminate between weak and strong cooperativity, based on a measure of “susceptibility.” We also properly extend the model in order to account for multiple attachments phenomena: this is realized by incorporating within the model p-body interactions, whose non-trivial cooperative capability is investigated too.     

Redox‐Active Nickel in Carbon Nanotubes and Its Direct Determination

The presence of residual metal‐catalyst impurities in carbon nanotubes is responsible for their toxicity. It is important to differentiate between the total amount of impurities and the redox‐active (bioavailable) amount of such impurities because only the bioavailable impurities exhibit toxic effects. Herein, we report a simple and specific method for quantifying the amount of redox‐active Ni present in various commercial samples of CNTs. It is based on the electrochemical oxidation of Ni(OH)₂ that is formed in alkaline solutions when Ni impurities are opened to the surrounding environment. Metallic Ni impurities play an extremely active role in toxicological assays as well as in undesired catalytic processes, and thus a method to rapidly quantify the amount of redox‐active Ni is of great importance.     

A statistical mechanics approach to Granovetter theory

In this paper we try to bridge breakthroughs in quantitative sociology/econometrics, pioneered during the last decades by Mac Fadden, Brock–Durlauf, Granovetter and Watts–Strogatz, by introducing a minimal model able to reproduce essentially all the features of social behavior highlighted by these authors.     

Implications of obesity for tendon structure, ultrastructure and biochemistry: a study on Zucker rats

The extracellular matrix consists of collagen, proteoglycans and non-collagen proteins. The incidence of obesity and associated diseases is currently increasing in developed countries. Obesity is considered to be a disease of modern times, and genes predisposing to the disease have been identified in humans and animals. The objective of the present study was to compare the morphological and biochemical aspects of the deep digital flexor tendon of lean (Fa/Fa or Fa/fa) and genetically obese (fa/fa) Zucker rats. Ultrastructural analysis showed the presence of lipid droplets in both groups, whereas disorganized collagen fibril bundles were observed in obese animals. Lean animals presented a larger amount of non-collagen proteins and glycosaminoglycans than obese rats. We propose that the overweight and lesser physical activity in obese animals may have provoked the alterations in the composition and organization of extracellular matrix components but a genetic mechanism cannot be excluded. These alterations might be related to organizational and structural modifications in the collagen bundles that influence the mechanical properties of tendons and the progression to a pathological state.