Use of cholesterol-rich nanoparticles that bind to lipoprotein receptors as a vehicle to paclitaxel in the treatment of breast cancer: pharmacokinetics, tumor uptake and a pilot clinical study

Purpose In animal experiments paclitaxel oleate associated with a cholesterol-rich nanoemulsion concentrated in the neoplastic tissues and showed reduced toxicity and increased antitumor activity compared with paclitaxel-Cremophor EL. Here, a clinical study was performed in breast cancer patients to evaluate the tumoral uptake, pharmacokinetics and toxicity of paclitaxel associated to nanoemulsions. Methods Twenty-four hours before mastectomy [(3)H]paclitaxel oleate associated with [(14)C]-cholesteryl oleatenanoemulsion or [(3)H]- paclitaxel in Cremophor EL were injected into five patients for collection of blood samples and fragments of tumor and normal breast tissue. A pilot clinical study of paclitaxel-nanoemulsion administered at 3-week intervals was performed in four breast cancer patients with refractory advanced disease at 175 and 220 mg/m(2) dose levels. Results T(1/2) of paclitaxel oleate associated to the nanoemulsion was greater than that of paclitaxel (t(1/2) = 15.4 +/- 4.7 and 3.5 +/- 0.80 h). Uptake of the [(14)C]-cholesteryl ester nanoemulsion and [(3)H]- paclitaxel oleate by breast malignant tissue was threefold greater than the normal breast tissue and toxicity was minimal at the two dose levels. Conclusions Our results suggest that the paclitaxel-nanoemulsion preparation can be advantageous for use in the treatment of breast cancer because the pharmacokinetic parameters are improved, the drug is concentrated in the neoplastic tissue and the toxicity of paclitaxel is reduced.

Hematopoietic response of rats exposed to the impact of an acute psychophysiological stressor on responsiveness to an in vivo challenge with Listeria monocytogenes: Modulation by Chlorella vulgaris prophylactic treatment

In this study, we investigated the hematopoietic response of rats pretreated with CV and exposed to the impact of acute escapable, inescapable or psychogenical stress on responsiveness to an in vivo challenge with Listeria monocytogenes. No consistent changes were observed after exposure to escapable footshock. Conversely, the impact of uncontrollable stress (inescapable and psychogenical) was manifested by an early onset and increased severity and duration of myrelossuppression produced by the infection. Small size CFU-CM colonies and increased numbers of clusters were observed, concurrently to a greater expansion in the more mature population of bone marrow granulocytes. No differences were observed between the responses of both uncontrollable stress regimens. CV prevented the myelossuppression caused by stress/infection due to increased numbers of CFU-GM in the bone marrow. Colonies of cells tightly packed, with a very condensed nucleus; in association with a greater expansion in the more immature population of bone marrow granulocytes were observed. Investigation of the production of colony-stimulating factors revealed increased colony-stimulating activity (CSA) in the serum of normal and infected/stressed rats treated with the algae. CV treatment restored/enhanced the changes produced by stress/infection in total and differential bone marrow and peripheral cells counts. Further studies demonstrated that INF-gamma is significantly reduced, whereas IL-10 is significantly increased after exposure to Uncontrollable stress. Treatment with CV significantly increased INF-gamma levels and diminished the levels of IL-10. Uncontrollable stress reduced the protection afforded by CV to a lethal dose of L. monocytogenes, with survival rates being reduced from (50%) in infected rats to 20% in infected/stressed rats. All together, our results suggest Chlorella treatment as an effective tool for the prophylaxis of post-stress myelossupression, including the detrimental effect of stress on the course and outcome of infections. (C) 2008 Elsevier Inc. All rights reserved.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Modified particle swarm optimization for day-ahead distributed energy resources scheduling including vehicle-to-grid

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Assessment of chemiluminescence and PCR effectiveness in relation to conventional serological tests for the diagnosis of Chagas' disease

While testing 414 sera for the diagnosis of Chagas' disease, the conventional reactions of indirect hemagglutination, indirect immunofluorescence and the immunosorbent assay showed a sensitivity of 95.7%, 100% and 98.2% and a specificity of 98%, 98% and 96.4%, respectively, and an excellent association using Fisher's exact test. Chemiluminescence presented 100% sensitivity and 89.6% specificity, while PCR showed 100% specificity and 1.2% sensitivity. It is believed that the three conventional serological reactions are still adequate for diagnosing Chagas' disease.

Vehicle-to-anything application (v2anything app) for electric vehicles

This paper presents a mobile information system denominated as Vehicle-to-Anything Application (V2Anything App), and explains its conceptual aspects. This application is aimed at giving relevant information to Full Electric Vehicle (FEV) drivers, by supporting the integration of several sources of data in a mobile application, thus contributing to the deployment of the electric mobility process. The V2Anything App provides recommendations to the drivers about the FEV range autonomy, location of battery charging stations, information of the electricity market, and also a route planner taking into account public transportations and car or bike sharing systems. The main contributions of this application are related with the creation of an Information and Communication Technology (ICT) platform, recommender systems, data integration systems, driver profile, and personalized range prediction. Thus, it is possible to deliver relevant information to the FEV drivers related with the electric mobility process, electricity market, public transportation, and the FEV performance.

Bidirectional battery charger with grid-to-vehicle, vehicle-to-grid and vehicle-to-home technologies

This paper presents the development of na on-board bidirectional battery charger for Electric Vehicles (EVs) targeting Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and Vehicle-to-Home (V2H) technologies. During the G2V operation mode the batteries are charged from the power grid with sinusoidal current and unitary power factor. During the V2G operation mode the energy stored in the batteries can be delivered back to the power grid contributing to the power system stability. In the V2H operation mode the energy stored in the batteries can be used to supply home loads during power outages, or to supply loads in places without connection to the power grid. Along the paper the hardware topology of the bidirectional battery charger is presented and the control algorithms are explained. Some considerations about the sizing of the AC side passive filter are taken into account in order to improve the performance in the three operation modes. The adopted topology and control algorithms are accessed through computer simulations and validated by experimental results achieved with a developed laboratory prototype operating in the different scenarios.

Human Capital in Economic Development: From Labour Productivity to Macroeconomic Impact

Micro-econometric evidence reveals high private returns to education, most prominently in low-income countries. However, it is disputed to what extent this translates into a macro-economic impact. This paper projects the increase in human capital from higher education in Malawi and uses a dynamic applied general equilibrium model to estimate the resulting macroeconomics impact. This is contingent upon endogenous adjustments, in particular how labour productivity affects competitiveness and if this in turn stimulates exports. Choice among commonly applied labour market assumptions and trade elasticities results in widely different outcomes. Appraisal of such policies should consider not only the impact on human capital stocks, but also adjustments outside the labour market.