Combinations of Piperine with Hydroxypropyl-β-Cyclodextrin as a Multifunctional System

Piperine is an alkaloid that has extensive pharmacological activity and impacts other active substances bioavailability due to inhibition of CYP450 enzymes, stimulation of amino acid transporters and P-glycoprotein inhibition. Low solubility and the associated low bioavailability of piperine limit its potential. The combination of piperine with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) causes a significant increase in its solubility and, consequently, an increase in permeability through gastrointestinal tract membranes and the blood–brain barrier. X-ray powder diffraction (XRPD), differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR) were used to characterize interactions between piperine and HP-β-CD. The observed physicochemical changes should be combined with the process of piperine and CD system formation. Importantly, with an increase in solubility and permeability of piperine as a result of interaction with CD, it was proven to maintain its biological activity concerning the antioxidant potential (2,2-diphenyl-1-picryl-hydrazyl-hydrate assay), inhibition of enzymes essential for the inflammatory process and for neurodegenerative changes (hyaluronidase, acetylcholinesterase, butyrylcholinesterase).     

Metabolic Profiling of VOCs Emitted by Bacteria Isolated from Pressure Ulcers and Treated with Different Concentrations of Bio-AgNPs

Considering the advent of antibiotic resistance, the study of bacterial metabolic behavior stimulated by novel antimicrobial agents becomes a relevant tool to elucidate involved adaptive pathways. Profiling of volatile metabolites was performed to monitor alterations of bacterial metabolism induced by biosynthesized silver nanoparticles (bio-AgNPs). Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae and Proteus mirabilis were isolated from pressure ulcers, and their cultures were prepared in the presence/absence of bio-AgNPs at 12.5, 25 and 50 µg mL⁻¹. Headspace solid phase microextraction associated to gas chromatography–mass spectrometry was the employed analytical platform. At the lower concentration level, the agent promoted positive modulation of products of fermentation routes and bioactive volatiles, indicating an attempt of bacteria to adapt to an ongoing suppression of cellular respiration. Augmented response of aldehydes and other possible products of lipid oxidative cleavage was noticed for increasing levels of bio-AgNPs. The greatest concentration of agent caused a reduction of 44 to 80% in the variety of compounds found in the control samples. Pathway analysis indicated overall inhibition of amino acids and fatty acids routes. The present assessment may provide a deeper understanding of molecular mechanisms of bio-AgNPs and how the metabolic response of bacteria is untangled.     

Supramolecular Assembly of Aminoethylene‐Lipopeptide PMO Conjugates into RNA Splice‐Switching Nanomicelles

Phosphorodiamidate morpholino oligomers (PMOs) are oligonucleotide analogs that can be used for therapeutic modulation of pre‐mRNA splicing. Similar to other classes of nucleic acid‐based therapeutics, PMOs require delivery systems for efficient transport to the intracellular target sites. Here, artificial peptides based on the oligo(ethylenamino) acid succinyl‐tetraethylenpentamine (Stp), hydrophobic modifications, and an azide group are presented, which are used for strain‐promoted azide‐alkyne cycloaddition conjugation with splice‐switching PMOs. By systematically varying the lead structure and formulation, it is determined that the type of contained fatty acid and supramolecular assembly have a critical impact on the delivery efficacy. A compound containing linolenic acid with three cis double bonds exhibits the highest splice‐switching activity and significantly increases functional protein expression in pLuc/705 reporter cells in vitro and after local administration in vivo. Structural and mechanistic studies reveal that the lipopeptide PMO conjugates form nanoparticles, which accelerate cellular uptake and that the content of unsaturated fatty acids enhances endosomal escape. In an in vitro Duchenne muscular dystrophy exon skipping model using H2K‐mdx52 dystrophic skeletal myotubes, the highly potent PMO conjugates mediate significant splice‐switching at very low nanomolar concentrations. The presented aminoethylene‐lipopeptides are thus a promising platform for the generation of PMO‐therapeutics with a favorable activity/toxicity profile.     

Microscopic simulation of electronic noise in semiconductormaterials and devices

We present a microscopic interpretation of electronic noise in semiconductor materials and two-terminal devices. The theory is based on Monte Carlo simulations of the carrier motion self-consistently coupled with a Poisson solver. Current and voltage noise operations are applied and their respective representations discussed. As application we consider the cases of homogeneous materials, resistors, n⁺nn ⁺ structures, and Schottky-barrier diodes. Phenomena associated with coupling between fluctuations in carrier velocity and self-consistent electric field are quantitatively investigated for the first time. At increasing applied fields hot-carrier effects are found to be of relevant importance in all the cases considered here. As a general result, noise spectroscopy is found to be a source of valuable information to investigate and characterize transport properties of semiconductor materials and devices     

Elevated temperature performance of pseudomorphic AlGaAs/InGaAsMODFETs

Parametric DC measurements on pseudomorphic AlGaAs/InGaAs modulation-doped field-effect transistors (MODFETs) were carried out over the 300-405 K temperature range. A gradual channel device model was developed to simulate the temperature dependent behavior and assist in the interpretation of the characteristics. The simulations are shown to provide good predictive ability and confirm the physical reasons why the zero temperature coefficient point of a MODFET occurs only for gate bias voltages below the threshold voltage     

Fundamentals of modelling concurrency using discrete relational structures

We consider relational structures such that is a set and are two binary relations on . For a number of different classes of structures we show that any structure can be represented as the intersection of its maximal extensions. Such a property – called extension completeness – can be seen as a generalisation of Szpilrajn's theorem which states that each partial order is the intersection of its total order extensions. When can be interpreted as causality and as ‘weak’ causality we obtain a model of concurrent histories generalising that based on causal partial orders. Received 1 February 1994 / 6 May 1996     

Modeling of systems with overflow multi-rate traffic

The article proposes an analytical method for determining occupancy distribution and blocking probability in systems which are offered overflow traffic composed of multi-service traffic streams. The described analytical model enables determination of parameters of traffic overflowed from primary groups in hierarchically constructed telecommunication networks. The proposed method is based on an appropriate modification of the Kaufman-Roberts recursion for the full-availability group with multi-rate traffic and uses the modified Fredericks & Hayward’s approximation. Additionally, an approximate method for dimensioning systems with multi-service overflow traffic is also presented. The analytical results of the blocking probability and the results obtained in the dimensioning processes calculated using the presented methodology are compared with the data obtained from the system simulation process.     

Optimal predictor for a singular random process

An optimal predictor is developed for a singular random process generated by a known system driven by a white noise sequence. The properties of the predictor are compared with those of the optimal predictor for a nonsingular random process.     

The application of semantic classification trees to naturallanguage understanding

This article describes a new method for building a natural language understanding (NLU) system, in which the system's rules are learnt automatically from training data. The method has been applied to design of a speech understanding (SU) system. Designers of such systems rely increasingly on robust matchers to perform the task of extracting meaning from one or several word sequence hypotheses generated by a speech recognizer. We describe a new data structure, the semantic classification tree (SCT), that learns semantic rules from training data and can be a building block for robust matchers for NLU tasks. By reducing the need for handcoding and debugging a large number of rules, this approach facilitates rapid construction of an NLU system. In the case of an SU system, the rules learned by an SCT are highly resistant to errors by the speaker or by the speech recognizer because they depend on a small number of words in each utterance. Our work shows that semantic rules can be learned automatically from training data, yielding successful NLU for a realistic application