Synthesis and characterization of paclitaxel-imprinted nanoparticles for recognition and controlled release of an anticancer drug

Imprinted nanoparticles as drug delivery carriers have been considered because owing to their cross-linked network, they act as the drug reservoir for controlled release. In this study, selective MIPs nanoparticles of paclitaxel (PTX) were successfully developed for application in the biological molecular recognition and in the design of new anticancer drug delivery systems. The MIPs nanoparticles prepared by miniemulsion polymerization technique using methacrylic acid (MAA) and methyl methacrylate as non-covalent functional monomer, ethylene glycol dimethacrylate and trimethylolpropane trimethacrylate (TRIM) as cross-linker agent, azobisisobutyronitrile as initiator, and hexadecane as hydrophobic agent. In order to prepare of MIP nanoparticles, the synthesis conditions and effective parameters, such as: cross-linker agent, different molar ratios of template–functional monomer–cross-linker agent, were investigated. In addition, the effect of different molar ratios of template and monomers on polymers binding and morphology were characterized. Structure and thermal properties of MIPs were confirmed by FT-IR spectroscopy and thermogravimetric analysis. Imprinted nanoparticles showed significant drug loading and encapsulation efficiency, 17.8 and 100 %, respectively. The particle size of MIP nanoparticles varies between 187 and 726 nm, according the SEM images and laser light scattering data. The imprinted nanoparticles showed satisfactory affinity (84 %) to PTX with a binding of 12 times higher than non-imprinted nanoparticles in biological samples when MAA and TRIM were used as functional and cross-linker monomer, respectively. Results from release experiments of MIPs showed a very slow and controlled release of PTX which would be helpful for sustained drug delivery.     

Estimating the rate constant in second-order kinetics using hard-soft-net analyte signal (HS-NAS) method

Rank deficiency is the major problem associated with the chemometrics modeling of the second-order chemical reactions of the form of A + B → C. In this article, the application of the hard-soft-net analyte signal (HS-NAS), as a newly proposed method in our research group, is described for modeling of second-order reactions. This combined hard-soft method is based on the net analyte signal (NAS) concept, which is defined as a part of total signal that is directly related to the concentration of the component of interest. Therefore, concentration changes versus time can be obtained by calculating NAS for any chemical species involved in the reaction without requiring any pure component spectra or extra runs. The power of the method was verified by applying it to the resolution of simulated data sets containing noises added at different levels. The resolution of the second-order reaction between amoxicillin and 1, 2-naphthoqoinone was also tested as a real chemical system.     

An efficient parallel algorithm for the longest path problem in meshes

The longest path problem is the problem of finding a simple path with the maximum number of vertices in a given graph, and so far it has been solved polynomially only for a few classes of graphs. This problem generalizes the well-known Hamiltonian path problem, hence it is NP-hard in general graphs. In this paper, first we give a sequential linear-time algorithm for the longest path problem in meshes. Then based on this algorithm, we present a constant-time parallel algorithm for the problem, which can be run on every parallel machine.     

Embedding linear arrays of the maximum length in O-shaped meshes

The Journal of Supercomputing - Embedding an interconnection network into another network is one of the important problems in parallel processing. In this paper, we study embedding of linear arrays...     

Enhancing spatial and temporal utilities in differentially private moving objects database release

International Journal of Information Security - The pervasive use of mobile technologies and GPS-equipped vehicles has resulted in a large number of moving objects databases. Privacy protection is...     

MC-MLAS: Multi-channel Minimum Latency Aggregation Scheduling in Wireless Sensor Networks

Data aggregation is a key, yet time-consuming functionality in wireless sensor networks (WSNs). Multi-channel design is a promising technique to alleviate interference as a primary reason for long latency of TDMA aggregation scheduling. Indeed, it provides more potential of parallel transmissions over different frequency channels, thus minimizing time latency. In this paper, we focus on designing a multi-channel minimum latency aggregation scheduling protocol, named MC-MLAS, using a new joint approach for tree construction, channel assignment, and transmission scheduling. To our best knowledge, this is the first work in the literature which combines orthogonal channels and partially overlapping channels to consider the total latency involved in data aggregation. Extensive simulations verify the superiority of MC-MLAS in WSNs.     

An extended compromise ratio method for fuzzy group multi-attribute decision making with SWOT analysis

The technique for order preference by similarity to ideal solution (TOPSIS) is a well-known multi-attribute decision making (MADM) method that is used to identify the most attractive alternative solution among a finite set of alternatives based on the simultaneous minimization of the distance from an ideal solution (IS) and the maximization of the distance from the nadir solution (NS). We propose an alternative compromise ratio method (CRM) using an efficient and powerful distance measure for solving the group MADM problems. In the proposed CRM, similar to TOPSIS, the chosen alternative should be simultaneously as close as possible to the IS and as far away as possible from the NS. The conventional MADM problems require well-defined and precise data; however, the values associated with the parameters in the real-world are often imprecise, vague, uncertain or incomplete. Fuzzy sets provide a powerful tool for dealing with the ambiguous data. We capture the decision makers’ (DMs’) judgments with linguistic variables and represent their importance weights with fuzzy sets. The fuzzy group MADM (FGMADM) method proposed in this study improves the usability of the CRM. We integrate the FGMADM method into a strengths, weaknesses, opportunities and threats (SWOT) analysis framework to show the applicability of the proposed method in a solar panel manufacturing firm in Canada.     

The approximate solution of charged particle motion equations in oscillating magnetic fields using the local multiquadrics collocation method

The charged particle motion for certain configurations of oscillating magnetic fields can be simulated by a Volterra integro-differential equation of the second order with time-periodic coefficients. This paper investigates a simple and accurate scheme for computationally solving these types of integro-differential equations. To start the method, we first reduce the integro-differential equations to equivalent Volterra integral equations of the second kind. Subsequently, the solution of the mentioned Volterra integral equations is estimated by the collocation method based on the local multiquadrics formulated on scattered points. We also expand the proposed method to solve fractional integro-differential equations including non-integer order derivatives. Since the offered method does not need any mesh generations on the solution domain, it can be recognized as a meshless method. To demonstrate the reliability and efficiency of the new technique, several illustrative examples are given. Moreover, the numerical results confirm that the method developed in the current paper in comparison with the method based on the globally supported multiquadrics has much lesser volume computing.