A Novel Artificial Hemoglobin Carrier Based on Heulandite-Calcium Mesoporous Aluminosilicate Particles

Tetraethyl-orthosilicate (TEOS)-based nanoparticles are most extensively used as a silica-based hemoglobin carrier system. However, TEOS-based nanoparticles induce adverse effects on the hemoglobin structure. Therefore, a heulandite-calcium-based carrier was investigated as a novel silica-based hemoglobin carrier system. The heulandite-calcium mesoporous aluminosilicate particles (MSPs) were fabricated by a patented tribo-mechanical activation process, according to the manufacturer, and its structure was assessed by X-ray diffraction analysis. Upon hemoglobin encapsulation, alternation in the secondary and tertiary structure was observed. The hemoglobin-particle interactions do not cause heme degradation or decreased activity. Once encapsulated inside the particle pores, the hemoglobin shows increased thermal stability, and higher loading capacity per gram of particles (by a factor of >1.4) when compared to TEOS-based nanoparticles. Futhermore, we introduced a PEGlyted lipid bilayer which significantly decreases the premature hemoglobin release and increases the colloidal stability. The newly developed hemoglobin carrier shows no cytotoxicity to human umbilical vein endothelial cells (HUVEC).     

Multidimensional networks: foundations of structural analysis

Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks.     

Environmental impact assessment of an automotive component using eco-indicator and CML methodologies

The environmental conscious manufacturing and climate change made automotive manufacturing organisations to recognise sustainability as a vital concept for survival in the global competitive scenario. Many components of automobiles are manufactured using plastics which play imperative role in ensuring sustainability. This research article deals with the application of Life Cycle Assessment on instrument panel of an electric car which is made of ABS (Acrylonitrile–Butadiene–Styrene). The case study has been conducted in an Indian instrument panel manufacturing plant. First, a life cycle model of ABS sheet which is particularly used in instrument panel manufacturing was built, and the environmental impact assessment of the instrument panel was done using Eco-indicator and CML methodologies. In life cycle perspective, the environmental hot spots have been found out. Later the results have been interpreted based on environmental impact assessment factors. The relative comparison of impact factors at different stages in the life cycle enables the generation of improvement options. The sustainability index of instrument panel before and after implementing Environmental Impact Assessment (EIA) has been computed. The results are statistically validated, and the managerial implications have been derived.     

Gene Mutation Analysis in EGFR Wild Type NSCLC Responsive to Erlotinib: Are There Features to Guide Patient Selection?

Tyrosine kinase inhibitors (TKIs) are very efficacious in non-small-cell lung cancer (NSCLC) patients harboring activating Epidermal Growth Factor Receptor (EGFR) mutations. However, about 10% of EGFR wild type (wt) patients respond to TKI, with unknown molecular mechanisms of sensitivity. We considered a case series of 34 EGFR wt NSCLC patients responsive to erlotinib after at least one line of therapy. Responsive patients were matched with an equal number of non-responsive EGFR wt patients. A panel of 26 genes, for a total of 214 somatic mutations, was analyzed by MassARRAY^® System (Sequenom, San Diego, CA, USA). A 15% KRAS mutation was observed in both groups, with a prevalence of G12C in non-responders (80% vs. 40% in responders). NOTCH1, p53 and EGFR-resistance-related mutations were found more frequently in non-responders, whereas EGFR-sensitizing mutations and alterations in genes involved in proliferation pathways were more frequent in responders. In conclusion, our findings indicate that p53, NOTCH1 and exon 20 EGFR mutations seem to be related to TKI resistance. KRAS mutations do not appear to influence the TKI response, although G12C mutation is more frequent in non-responders. Finally, the use of highly sensitive methodologies could lead to the identification of under-represented EGFR mutations potentially associated with TKI sensitivity.     

LODE: A distance-based classifier built on ensembles of positive and negative observations

Current work on assembling a set of local patterns such as rules and class association rules into a global model for the prediction of a target usually focuses on the identification of the minimal set of patterns that cover the training data. In this paper we present a different point of view: the model of a class has been built with the purpose to emphasize the typical features of the examples of the class. Typical features are modeled by frequent itemsets extracted from the examples and constitute a new representation space of the examples of the class. Prediction of the target class of test examples occurs by computation of the distance between the vector representing the example in the space of the itemsets of each class and the vectors representing the classes.It is interesting to observe that in the distance computation the critical contribution to the discrimination between classes is given not only by the itemsets of the class model that match the example but also by itemsets that do not match the example. These absent features constitute some pieces of information on the examples that can be considered for the prediction and should not be disregarded. Second, absent features are more abundant in the wrong classes than in the correct ones and their number increases the distance between the example vector and the negative class vectors. Furthermore, since absent features are frequent features in their respective classes, they make the prediction more robust against over-fitting and noise. The usage of features absent in the test example is a novel issue in classification: existing learners usually tend to select the best local pattern that matches the example and do not consider the abundance of other patterns that do not match it. We demonstrate the validity of our observations and the effectiveness of LODE, our learner, by means of extensive empirical experiments in which we compare the prediction accuracy of LODE with a consistent set of classifiers of the state of the art. In this paper we also report the methodology that we adopted in order to determine automatically the setting of the learner and of its parameters.     

Optical, electrical and structural properties of Cu2Te thin films deposited by magnetron sputtering

Thin films of Cu₂Te were deposited, at room temperature, on glass substrates by magnetron sputtering from independent Cu and Te sources. This work presents the effect of annealing temperature on the optical, structural, and electrical properties of sputtered Cu₂Te films. Annealing above 300 °C resulted in stoichiometric and near stoichiometric Cu₂Te phases, whereas temperatures above 400 °C yielded films with single Cu₂Te phase. In contrast, annealing at temperatures of 250 °C and below resulted in mixed phases of CuTe, Cu₇Te₅, Cu_1.8Te, and Cu₂Te. Analyses of transmittance and reflectance measurements for Cu₂Te indicate that photon absorption occurs via indirect band transitions for incident photons with energy above the band gap energy and free carrier absorption below the band gap energy. The determined indirect band gap was 0.90 eV and its associated phonon energy was 0.065 eV. Optical phonon scattering was identified as the mechanism through which the momentum is conserved during absorption by free carriers. Electrical measurements show p-type conductivity and highly degenerate semiconducting behavior with a hole carrier concentration p = 5.18 × 10²¹ cm^− 3.     

Crystallization of poly(ethylene terephthalate) (PET) from the oriented mesomorphic form

Yarns of different inherent viscosities, in the range 0.6–1.1 dL/g, spun in industrial plants, and drawn at room temperature to obtain mesomorphic samples, have been characterized. The evolution from the mesomorphic form toward the triclinic crystalline form has also been studied by combined differential scanning calorimetry (DSC), dynamic mechanical analyses (DMA), and accurate wide angle X-ray diffraction experiments. The DMA analysis of the mesomorphic samples allows better resolution of the glass transition and crystallization phenomena, which are superimposed in the DSC scans. The degree of molecular orientation in the mesomorphic samples, and the temperature of crystallization from the mesomorphic form (60–80°C), are essentially independent of the polymer molar mass. © 1994 John Wiley & Sons, Inc.     

Interaction between Maillard reaction products and lipid oxidation in starch‐based model systems

The effect of Maillard reaction products (MRPs) on the kinetics of lipid oxidation in intermediate‐moisture model systems containing pregelatinised starch, glucose, lysine and soybean oil has been studied. The samples, either containing all components or excluding one or more of them, were heated at 100 °C for different times. Lipid oxidation and browning indices were measured and the results confirmed the ability of MRPs to retard peroxide formation. Under the conditions adopted, the rate of the Maillard reaction was increased by the presence of the oil and its oxidation products. The antioxidant action of MRPs was also evaluated using a peroxide‐scavenging test based on crocin bleaching. The results demonstrated that antioxidant activity developed with increased browning of the samples. © 2000 Society of Chemical Industry