Proteomic Helicobacter pylori biomarkers discriminative of low-grade gastric MALT lymphoma and duodenal ulcer

To date no reliable diagnostic method exists to predict, among the very large and clinically heterogeneous group of Helicobacter pylori‐infected patients, the extremely small group at risk for developing low‐grade gastric MALT lymphoma (LG‐MALT). Search of proteomic biomarkers holds promise for the classification of the H. pylori strains with regard to this severe clinical outcome. In the present study 69 H. pylori strains isolated from patients with two different H. pylori‐associated diseases, duodenal ulcer (DU, n=29) and LG‐MALT (n=40) were used. Protein expression patterns of the strains were analyzed by using the high‐throughput methodology SELDI. Selected proteins were purified by means of chromatographic and electrophoretic methods in view of further sequencing by LC‐MS/MS. Univariate analysis (Mann–Whitney test) of the protein expression patterns generated nine significant biomarkers that can discriminate between H. pylori strains from patients with DU and LG‐MALT. These biomarkers are of low molecular weight, ranging from 6 to 26.6 kDa. Among them, two are overexpressed in LG‐MALT strains and seven – in DU strains. Two biomarker proteins, one overexpressed in LG‐MALT strains (13.2 kDa) and another one – overexpressed in DU strains (26.6 kDa), were purified to homogeneity and identified by using LC‐MS/MS as a 50S ribosomal protein L7/L12 and a urease subunit, respectively. These biomarkers can be included in novel protein arrays for the differential diagnosis of H. pylori‐associated clinical outcomes.     

Biodegradable poly(lactic acid)-based scaffolds: synthesis and biomedical applications

Biodegradable polymers are identified as substantial materials for biomedical applications. These polymers have the ability to deteriorate through an unpretentious hydrolysis and eliminated through kidneys’ functions or metabolic processes. Among widely used biodegradable polymers in biomedical applications, poly(lactic acid) (PLA) is becoming one of the most paramount polymers. Synthesizing PLA through melt/solution polycondensation polymerizations makes it relatively easy to tailor properties of final product. However, their synthesis reactions are affected by several parameters such as polymerization time, temperature, pressure, catalysts, and the polarity of the solvent. Moreover, equilibrium reactions are controlled through utilizing a hydrophilic monomer such as ethylene glycol (EG). These factors can strongly impact final properties of PLA. Thus, it is indispensable to comprehend the effect of operating parameters during the polymerization process. Optimizing synthesis conditions can be accomplished through reducing side reactions. Furthermore, this can be achieved through racemization by utilizing chain extenders to build high molecular weight and enhance thermal stability. In this review, the design and fabrication of porous PLA scaffolds and their physicomechanical behavior are reviewed. Different PLA scaffold parameters were investigated thoroughly, which include biocompatibility, biodegradability, and mechanical properties for different porosity and pore sizes to mimic the complex architecture of the natural tissue regeneration.

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Intelligent system to support judgmental business forecasting: thecase of estimating hotel room demand

Forecasting is an instrumental tool for strategic decision-making in any business activity. Good forecasts can reduce the uncertainty about the future and, hence, help managers make better decisions. Virtually all statistical forecasting techniques depend on the continuity of historical data and time series and may not predict a discontinuous change in the business environment. Often times, this discontinuity is known to managers who then must rely on their judgment to make forecast adjustments. We discuss the role of judgmental forecasting and take the problem of estimating future hotel room demand as a practical business application. Next, we propose IS-JFK: an intelligent system to support judgmental forecasting and knowledge of managers. To account for vagueness in the knowledge elicited from managers and the approximate nature of their reasoning, the system is built around fuzzy IF-THEN rules and uses fuzzy logic for decision inference. IS-JFK supports two methods for forecast adjustments: 1) a direct approach and 2) an approach based on fuzzy intervention analysis. Actual data from a hotel property are used in some case-scenario simulations to illustrate the merits of the intelligent support system     

Nanomedicine Strategies for Management of Drug Resistance in Lung Cancer

Lung cancer (LC) is one of the leading causes of cancer occurrence and mortality worldwide. Treatment of patients with advanced and metastatic LC presents a significant challenge, as malignant cells use different mechanisms to resist chemotherapy. Drug resistance (DR) is a complex process that occurs due to a variety of genetic and acquired factors. Identifying the mechanisms underlying DR in LC patients and possible therapeutic alternatives for more efficient therapy is a central goal of LC research. Advances in nanotechnology resulted in the development of targeted and multifunctional nanoscale drug constructs. The possible modulation of the components of nanomedicine, their surface functionalization, and the encapsulation of various active therapeutics provide promising tools to bypass crucial biological barriers. These attributes enhance the delivery of multiple therapeutic agents directly to the tumor microenvironment (TME), resulting in reversal of LC resistance to anticancer treatment. This review provides a broad framework for understanding the different molecular mechanisms of DR in lung cancer, presents novel nanomedicine therapeutics aimed at improving the efficacy of treatment of various forms of resistant LC; outlines current challenges in using nanotechnology for reversing DR; and discusses the future directions for the clinical application of nanomedicine in the management of LC resistance.     

Mechanical and thermal properties of calcium carbonate‐filled PP/LLDPE composite

Polymer blends typically are the most economical means to develop new resins for specific applications with the best cost/performance balance. In this paper, the mechanical properties, melting, glass transition, and crystallization behavoir of 80 phr polypropylene (PP) with varying weights of linear low density polyethylene (LLDPE) at 10, 20/ 20 wt % CaCO₃, 30, 40, and 50 phr were studied. A variety of physical properties such as tensile strength, impact strength, and flexural strength of these blends were evaluated. The compatibility of these composite was examined by differential scanning calorimetry (DSC) to estimate T_m and T_c, and by dynamic mechanical analysis (DMA) to estimate T_g. The fractographic analysis of these blends was examined by scanning electron microscopy (SEM). It has been confirmed that increasing the LLDPE content trends to decreases the tensile strength and flexural strength. However, increasing the LLDPE content led to increases in the impact strength of PP/LLDPE blends. It was also found that up to 40 phr the corresponding melting point (T_m) was not effected with increasing LLDPE content. Each compound has more than one T_g, which was informed that there is a brittle‐ductile transition in fracture nature of these blends, the amount of material plastically deformed on the failure surface seems to increase with the increasing the LLDPE content. And PP/LLDPE blends at temperature (23°C) showed a ductile fracture mode characterized by the co‐existence of a shear yielding process; whereas at lower temperature (−20°C) the fractured surfaces of specimens appear completely brittle. The specimens broke into two pieces with no evidence of stress whitening, permanent macroscopic deformation or yielding. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

A neural network model for the numerical prediction of the diameter of electro-spun polyethylene oxide nanofibers

This paper investigates the viability of neural network as a tool for predicting the diameter of fiber formed by an electrospinning process. Published experimental data for polyethylene oxide (PEO) aqueous solution is used to train and test the neural network model. Concentration, conductivity, flow rate, and electric field strength are used as the input variables to the neural network model. Network model selection, training and testing were conducted using the k-fold cross validation technique which is demonstrated to be the most suitable scheme for the size of dataset used in this study. A statistical study was conducted to establish 95% confidence intervals on the bias and on the limits of agreement between the experimental data and the predicted data. The computer simulation results show a very good agreement between the data, demonstrating the viability of neural network as a promising tool for predicting fiber diameter. While the proposed neural network approach is not intended to model the complete complex physics of the electrospinning process, it is demonstrated to provide an accurate nonlinear mapping between the four salient input variables and the diameter of the formed fiber. This study provides some potential insights into exploring neural network model-based feedback control techniques to regulate nanofiber diameter in an electrospinning process.     

Coumarin-Tagged Dinuclear Trithiolato-Bridged Ruthenium(II)⋅Arene Complexes: Photophysical Properties and Antiparasitic Activity

The synthesis, characterization, photophysical and biological properties of 13 new conjugate coumarin-diruthenium(II)⋅arene complexes against Toxoplasma gondii are presented. For all conjugate organometallic unit/coumarins, an almost complete loss of fluorescence efficacy was observed. However, the nature of the fluorophore, the type of bonding, the presence and length of a linker between the coumarin dye and the ruthenium(II) moiety, and the number of dye units influenced their biological properties. The in vitro activity against a transgenic T. gondii strain grown in human foreskin fibroblasts (HFF) leads to IC₅₀ values for T. gondii β-gal from 105 to 735 nM. Of note is that nine compounds displayed lower IC₅₀ than the standard drug pyrimethamine. One compound applied at its IC₅₀ did not affect B-cell proliferation but had an impact on T-cell proliferation in murine splenocyte cultures. Transmission electron microscopy of T. gondii β-gal-infected HFF showed that treatment predominantly affected the parasites’ mitochondrion.     

Cellular and Molecular Targets of Nucleotide-Tagged Trithiolato-Bridged Arene Ruthenium Complexes in the Protozoan Parasites Toxoplasma gondii and Trypanosoma brucei

Toxoplasma gondii is an apicomplexan parasite that infects and proliferates within many different types of host cells and infects virtually all warm-blooded animals and humans. Trypanosoma brucei is an extracellular kinetoplastid that causes human African trypanosomiasis and Nagana disease in cattle, primarily in rural sub-Saharan Africa. Current treatments against both parasites have limitations, e.g., suboptimal efficacy and adverse side effects. Here, we investigate the potential cellular and molecular targets of a trithiolato-bridged arene ruthenium complex conjugated to 9-(2-hydroxyethyl)-adenine (1), which inhibits both parasites with IC₅₀s below 10⁻⁷ M. Proteins that bind to 1 were identified using differential affinity chromatography (DAC) followed by shotgun-mass spectrometry. A trithiolato-bridged ruthenium complex decorated with hypoxanthine (2) and 2-hydroxyethyl-adenine (3) were included as controls. Transmission electron microscopy (TEM) revealed distinct ultrastructural modifications in the mitochondrion induced by (1) but not by (2) and (3) in both species. DAC revealed 128 proteins in T. gondii and 46 proteins in T. brucei specifically binding to 1 but not 2 or 3. In T. gondii, the most abundant was a protein with unknown function annotated as YOU2. This protein is a homolog to the human mitochondrial inner membrane translocase subunit Tim10. In T. brucei, the most abundant proteins binding specifically to 1 were mitochondrial ATP-synthase subunits. Exposure of T. brucei bloodstream forms to 1 resulted in rapid breakdown of the ATP-synthase complex. Moreover, both datasets contained proteins involved in key steps of metabolism and nucleic acid binding proteins.     

White color tracking adjustment in television receivers using neural networks

This paper discusses the application of neural networks to the white tracking adjustment of television receivers during production. High quality levels of tracking for the color temperature 8000 K were obtained with four-layer (7-10-10-6) network. The network input set consists of brightness level, high and low luminance levels, and “x” and “y” coordinates on the chromaticity diagram for both high and low luminance. The network output set consists of recommended adjustments for brightness, red, green, and blue cutoffs, and green and blue gains. The network was trained using the back-propagation algorithm. The experimental study has shown that the application of neural networks has reduced the testing time which has led to an increase in production rate.     

Molecular characterization of sweet potato (<i>Ipomoea batatas</i> [L.] Lam) germplasms for desirable traits by using simple sequence repeats markers

Every breeding program that aims to create new and improved cultivars with desired traits mostly relies on information related to genetic diversity. Therefore, molecular characterization of germplasms is important to obtain target cultivars with desirable traits. Sweet potato [Ipomoea batatas (L.) Lam] is widely considered the world’s most important crop, with great diversity in morphological and phenotypic traits. The genetic diversity of 20 sweet potato germplasms originating from Bangladesh, CIP, Philippines, Taiwan, and Malaysia were compared, which was accomplished by genetic diversity analysis by exploring 20 microsatellite DNA markers for germplasm characterization and utilization. This information was effective in differentiating or clustering the sweet potato genotypes. A total of 64 alleles were generated using the 20 primers throughout the 20 germplasm samples, with locus IBS97 having the highest number of alleles (5), whereas locus IbU33 had the fewest alleles (2). The alleles varied in size from 105 (IbU31) to 213 base pairs (IBS34). The Polymorphism Information Content (PIC) values for the loci IbL46 and IBS97 varied from 0.445 to 0.730. IBS97 has the highest number of effective alleles (3.704), compared to an average of 2.520. The average Shannon’s diversity index (H) was 1.003, ranging from 0.673 in IbU3 to 1.432 in IBS97. The value of gene flow (Nm) varied between 0.000 and 0.005, with an average of 0.003, whereas genetic differentiation (FST-values) ranged between 0.901 and 1.000. The sweet potato germplasm included in this study had a broad genetic base. SP1 vs. SP9 and SP12 vs. SP18 germplasm pairings had the greatest genetic distance (GD = 0.965), while SP1 vs. SP2 germplasm couples had the least genetic diversity (GD = 0.093). Twenty genotypes were classified into two groups in the UPGMA dendrogram, with 16 genotypes classified as group “A” and the remaining four genotypes, SP10, SP18, SP19, and SP20, classified as group “B.” According to cluster analysis, the anticipated heterozygosity (gene diversity) of Nei (1973) was 0.591 on average. In summary, SSR markers successfully evaluated the genetic relationships among the sweet potato accessions used and generated a high level of polymorphism. The results of the present study will be useful for the management of germplasm, improvement of the current breeding strategies, and the release of new cultivars as varieties.