Reduction toxicity of Amphotericin B through loading into a novel nanoformulation of anionic linear globular dendrimer for improve treatment of leishmania major

Amphotericin B (A) as an antileishmanial drug has limited clinical application owing to severe side-effects and low-water solubility. This is the first study reported using Anionic Linear Globular Dendrimer (ALGD) as A carrier for the increase of A solubility rate, decrease its toxicity, and improve its therapeutic effects. ALGD was synthesized and A was loaded into nanoparticles for the first time with the drug-loading efficiency of 82%. Drug loading was confirmed using characterization methods. The drug solubility rate was increased by 478-folds. The results of the study showed that the A toxicity was significantly decreased by 95% in vitro and in vivo environments, which was confirmed by pathology findings and enzymatic evaluation. Furthermore, the nanodrug caused that mortality rate was reached to zero. Moreover, the nanodrug was as potent as the free drug and glucantime (GUL) in reducing the parasite burden and parasite number. These findings indicated the potency of ALGD to decrease the drug side-effects, increase the drug solubility rate, and improve the drug efficacy. Moreover, the nanoformulation was a non-toxic and cost-effective formulation. The conformity between in vitro and in vivo results suggested that the A-loaded ALGD could be considered as a promising candidate in reducing the side-effects of A in leishmaniasis treatment.

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Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model

This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic support vector regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees obtaining the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by three illustrative examples.

     

OSPM: A design methodology for open strategic planning

This study employs a design science perspective to propose a methodology for open strategic planning (OSP). Habermas’ discourse theory and Bryson’s strategy change cycle are used as informing kernel theories. A methodology is proposed to satisfy the requirements retrieved from the kernel theories. The proposed methodology contains modules for a planning system and a planning process. Design principles are explained through a blueprint of the system and process. The proposed methodology is applied and evaluated in two cases. Contributions to the literature involve extending the literature on OSP to an applicable methodology with guidelines on how to implement open strategy.     

Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot

Human mastication is a complex and rhythmic biomechanical process which is regulated by a brain stem central pattern generator (CPG). Masticatory patterns, frequency and amplitude of mastication are different from person to person and significantly depend on food properties. The central nervous system controls the activity of muscles to produce smooth transitions between different movements. Therefore, to rehab human mandibular system, there is a real need to use the concept of CPG for development of a new methodology in jaw exercises and to help jaw movements recovery. This paper proposes a novel method for real-time trajectory generation of a mastication rehab robot. The proposed method combines several methods and concepts including kinematics, dynamics, trajectory generation and CPG. The purpose of this article is to provide a methodology to enable physiotherapists to perform the human jaw rehabilitation. In this paper, the robotic setup includes two Gough–Stewart platforms. The first platform is used as the rehab robot, while the second one is used to model the human jaw system. Once the modeling is completed, the second robot will be replaced by an actual patient for the selected physiotherapy. Gibbs–Appell’s formulation is used to obtain the dynamics equations of the rehab robot. Then, a method based on the Fourier series is employed to tune parameters of the CPG. It is shown that changes in leg lengths, due to the online changes of the mastication parameters, occur in a smooth and continuous manner. The key feature of the proposed method, when applied to human mastication, is its ability to adapt to the environment and change the chewing pattern in real-time parameters, such as amplitudes as well as jaw movements velocity during mastication.     

Could-based vehicular networks: a taxonomy,survey, and conceptual hybrid architecture

Wireless Networks - In recent times, vehicular network research has attracted the attention of both researchers and the industry partly due to its potential applications in efficient traffic...     

Using the red-near infrared spectral to estimate ground cover based on vegetative indices

Rapid and accurate estimation of Ground Cover (GC) at regional and global scales for agricultural management application is only possible by using remote sensing (RS). In this study, two Vegetation Indices (VIs) including the Perpendicular Vegetation Index (PVI) and Normalized Difference Vegetation Index (NDVI) were used for estimating GC. Since the parameters of the bare soil line have an important role in calculating GC based on PVI, this line was extracted based on the red-NIRmin (minimum near infrared) method with different intervals (0.0001, 0.0005, and 0.0010). In addition to traditional statistics such as Root Mean Square Error (RMSE), the sensitivity analysis (S) was also used to sharpen the accuracy of the models' estimations. The results indicated that the PVI-based method, in contrast to the NDVI-based approach, had a better performance in estimating GC of wheat. The highest correlation between the observed GC and the estimated GC based on PVI method was achieved in interval length of 0.0005 (R2 = 0.91) with RMSE equal to 8.82. This regression line (GCEST = -3.47 + 0.96 GCOBS) was not significantly different from the 1:1 line. As expected, the best estimation was achieved when the sensitivity of estimated GC based on PVI (length of the interval: 0.0005) was almost constant and low compared to the other models.     

Application of a modified VIKOR method for decision-making problems in lean tool selection

Group decision making with multiple criteria is the most popular method for ranking a set of alternatives. In this regard, all alternatives are compared based on a common criteria set. Meanwhile, decision makers sometimes encounter special situations, for example, having to select from among a set of alternatives without a set of criteria or with a set of criteria that are grouped/related to various alternatives. Thus, it may be impossible to select from among a set of alternatives using typical methods. Hence, in this study, a new, modified VIKOR method is proposed to address the lean tool selection problems in manufacturing systems. In this study, a model was developed to help practitioners improve their ability to solve problems when the possible solutions have their own individual criteria. In fact the modified VIKOR method can be applied to rank alternatives in threefold: alternatives with common criteria, without common criteria, with integrated common, and uncommon criteria. This paper offers numerical examples of the model, using a case study to illustrate an application of the proposed model and properly assess the validity of this new method. The results demonstrate the usefulness and effectiveness of the modified new method. The model covers the lack existing in the current literature to assess effectiveness of applying lean tools.     

Dual-Function Semaphorin 4D Released by Platelets: Suppression of Osteoblastogenesis and Promotion of Osteoclastogenesis

Effects of the antiosteoblastogenesis factor Semaphorin 4D (Sema4D), expressed by thrombin-activated platelets (TPs), on osteoblastogenesis, as well as osteoclastogenesis, were investigated in vitro. Intact platelets released both Sema4D and IGF-1. However, in response to stimulation with thrombin, platelets upregulated the release of Sema4D, but not IGF-1. Anti-Sema4D-neutralizing monoclonal antibody (mAb) upregulated TP-mediated osteoblastogenesis in MC3T3-E1 osteoblast precursors. MC3T3-E1 cells exposed to TPs induced phosphorylation of Akt and ERK further upregulated by the addition of anti-sema4D-mAb, suggesting the suppressive effects of TP-expressing Sema4D on osteoblastogenesis. On the other hand, TPs promoted RANKL-mediated osteoclastogenesis in the primary culture of bone-marrow-derived mononuclear cells (BMMCs). Among the known three receptors of Sema4D, including Plexin B1, Plexin B2 and CD72, little Plexin B2 was detected, and no Plexin B1 was detected, but a high level of CD72 mRNA was detected in RANKL-stimulated BMMCs by qPCR. Both anti-Sema4D-mAb and anti-CD72-mAb suppressed RANKL-induced osteoclast formation and bone resorptive activity, suggesting that Sema4D released by TPs promotes osteoclastogenesis via ligation to a CD72 receptor. This study demonstrated that Sema4D released by TPs suppresses osteogenic activity and promotes osteoclastogenesis, suggesting the novel property of platelets in bone-remodeling processes.