Development of the first dual inhibitors for steroid sulfatase (STS) and 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1) : a novel treatment approach for endometriosis

Endometriosis is an estrogen dependent disease (EDD) that has no satisfying treatment option, as the existing ones mainly comprise endocrine treatments that lead to severe systemic hypo-estrogenic side effects. Steroid sulfatase (STS) and 17β hydroxysteroid dehydrogenase type 1 (17β HSD1) are attractive new targets for the treatment of EDDs. Their inhibition leads to blockage of the local biosynthesis of estrogen without significantly affecting the circulating estrogen. The simultaneous inhibition of both enzymes appears to be more promising than the blockage of only one protein. The main aim of this study is the development of dual inhibitors of STS and 17β-HSD1 (DSHIs) that offers a novel treatment option for endometriosis without severe side effects. Using a designed multiple ligand (DML) approach, the first DSHIs were identified. Upon structural optimizations, highly potent inhibitors in cell-free and cellular assays were achieved that are characterized by high selectivity over 17β-HSD2 which plays a protective role in endometriosis. The DSHIs were able to efficiently reverse the E1-S and E1- induced T47D cell proliferation. The most interesting inhibitor described in this work is characterized by high metabolic stability in human and mouse hepatic S9 fraction, along with good physicochemical properties and high safety margins in cytotoxicity assays. Furthermore, this DSHI is considered a suitable candidate for in vivo proof of principle studies based on its pharmacokinetic profile.Endometriose ist eine Estrogen-abhängige Erkrankung für die bislang keine zufriedenstellende Therapieoption existiert. Zum Einsatz kommen hauptsächlich endokrine Behandlungen, die systemisch zu stark hypoestrogenen Zuständen und damit zusammenhängenden, ernsthaften Nebenwirkungen führen. Die Enzyme Steroid Sulfatase (STS) and 17β-Hydroxysteroid Dehydrogenase Typ 1 (17β-HSD1) sind attraktive, neuartige Targets zur Behandlung Estrogen-abhängiger Erkrankung. Ihre Inhibierung führt zur Hemmung lokaler Estrogen-Biosynthese, ohne starke Beeinflussung systemischer Estrogen-Konzentrationen. Die gleichzeitige Hemmung beider Enzyme erscheint vielversprechender als die Blockade eines einzelnen Proteins. Ein Gegenstand der vorliegenden Arbeit ist die Entwicklung dualer Inhibitoren von STS und 17β-HSD1 (DSHIs). Solche Wirkstoffe sind eine neuartige Therapieoption für Endometriose, die nicht zu den erwähnten Nebenwirkungen führt. Unter Anwendung eines Ansatzes zum gezielten Design von Liganden mehrerer biologischer Targets wurden die ersten DSHIs identifiziert. Anschließende Strukturoptimierungen führten zu Wirkstoffen, die in Zell-freien und zellbasierten Assays beide Targetenzyme hochpotent hemmten. Darüberhinaus waren die DSHIs in der Lage, die Estronsulfat- und Estron-induzierte Proliferation von T47D Zellen vollständig aufzuheben. Die vielversprechendste Verbindung zeigt hohe metabolische Stabilität in den S9-Lebermikrosomenfraktionen von Mensch und Maus, vorteilhafte physiko-chemische Eigenschaften und geringe Cytotoxizität. Darüberhinaus zeigte sie günstige pharmakokinetische Eigenschaften in der Maus, was sie zu einem geeigneten Kandidaten für eine proof-of-principle Studie macht

Data on fuzzy logic based-modelling and optimization of recovered lipid from microalgae

This article presents the data of recovered lipid from microalgae using fuzzy logic based-modelling and particle swarm optimization (PSO) algorithm. The details of fuzzy model and optimization process were discussed in our work entitled “Application of Fuzzy Modelling and Particle Swarm Optimization to Enhance Lipid Extraction from Microalgae” (Nassef et al., 2019) [1]. The presented data are divided into two main parts. The first part represents the percentage of recovered lipid using fuzzy logic model and ANOVA. However, the second part shows the variation of the cost function (recovered lipid) for the 100 runs of PSO algorithm during optimization process. These data sets can be used as references to analyze the data obtained by any other optimization technique. The data sets are provided in the supplementary materials in Tables 1–2

Hybrid photovoltaic-thermoelectric generator powered synchronous reluctance motor for pumping applications

The interest in photovoltaic (PV) pumping systems has increased, particularly in rural areas where there is no grid supply available. However, both the performance and the cost of the whole system are still an obstacle for a wide spread of this technology. In this article, a hybrid photovoltaic (PV)-thermoelectric generator (TEG) is investigated for pumping applications. The electric drivetrain comprises a synchronous reluctance motor and an inverter. A control strategy for the drivetrain is employed to execute two main tasks: 1) driving the motor properly to achieve a maximum torque per Ampere condition and 2) maximizing the output power of the PV system at different weather conditions. This means that the conventional DC-DC converter is not used in the proposed system. Moreover, batteries, which are characterized by short life expectancy and high replacement cost, are also not used. It is found that the motor output power and the pump flow rate are increased by about 9.5% and 12% respectively when the hybrid PV-TEG array is used compared to only using PV array. Accordingly, the performance, cost and complexity of the system are improved. Measurements on an experimental laboratory setup are constructed to validate the theoretical results of this work

Parameter estimation of electric power transformers using Coyote Optimization Algorithm with experimental verification

In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms

Solar array fed synchronous reluctance motor driven water pump : an improved performance under partial shading conditions

An improved performance of a photovoltaic (PV) pumping system employing a synchronous reluctance motor (SynRM) under partial shading conditions is proposed. The system does not include the dc-dc converter that is predominantly being utilized for maximizing the output power of the PV array. In addition, storage batteries are also not contained. A conventional inverter connected directly to the PV array is used to drive the SynRM. Further, a control strategy is proposed to drive the inverter so that the maximum output power of the PV array is achieved while the SynRM is working at the maximum torque per Ampere condition. Consequently, this results in an improved system efficiency and cost. Moreover, two maximum power point tracking (MPPT) techniques are compared under uniform and partial shadow irradiation conditions. The first MPPT algorithm is based on the conventional perturbation and observation (P&O) method and the second one uses a differential evolution (DE) optimization technique. It is found that the DE optimization method leads to a higher PV output power than using the P&O method under the partial shadow condition. Hence, the pump flow rate is much higher. However, under a uniform irradiation level, the PV system provides the available maximum power using both MPPT techniques. The experimental measurements are obtained to validate the theoretical work

The effect of Resveratrol on the Upregulation of Ngb

Apoptosis involves a series of biochemical events that leads to the eventual death of the cell. One pathway – intrinsic pathway, involves the fragmentation of mitochondria and the release of pro-apoptotic proteins, such as cytochrome c. A certain globin protein has been shown to be able to protect cells from apoptosis, called Neuroglobin (Ngb). Ngb is a globin haem protein that has been shown to reduce the ferric form of cytochrome c to inhibit apoptosis. In addition, Ngb has been shown to translocate into the mitochondria under stress, where it reacts with cytochrome c. Estradiol (E2) has been shown to greatly upregulate the levels of Ngb and also stimulates the translocation of Ngb into the mitochondria. The upregulation of Ngb has been shown to be mediated via the ER subtype, ERβ. Even though literature covers the effects of E2 and the ERβ agonist DPN (Diartylpropiolnitrile), there is a lack of evidence on the ER agonist, Resveratrol (RES); RES is a phytoestrogen that has been shown to induce mitochondrial biogenesis and abrogate mitochondrial fragmentation, ameliorating apoptosis. The hypothesis of this study is that RES will upregulate Ngb levels as E2 does, and will translocate Ngb into the mitochondria as E2 does. The results of this study showed that Ngb bands could not be detected via western blots, and the mRNA transcript levels in MCF-7 and DLD-1 could not be quantified. The Ngb-GFP fusion protein did not fluoresce and Ngb’s translocation into the mitochondria could not be determined. Ngb overexpression did not inhibit mitochondrial fragmentation and did not induce mitochondrial fusion

Finite element based overall optimization of switched reluctance motor using multi-objective genetic algorithm (NSGA-II)

The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM

Multi-objective optimization of switched reluctance machine design using Jaya algorithm (MO-Jaya)

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions

Optimal Parameter Estimation of Solar PV Panel Based on Hybrid Particle Swarm and Grey Wolf Optimization Algorithms

The performance of a solar photovoltaic (PV) panel is examined through determining its internal parameters based on single and double diode models. The environmental conditions such as temperature and the level of radiation also influence the output characteristics of solar panel. In this research work, the parameters of solar PV panel are identified for the first time, as far as the authors know, using hybrid particle swarm optimization (PSO) and grey wolf optimizer (WGO) based on experimental datasets of I-V curves. The main advantage of hybrid PSOGWO is combining the exploitation ability of the PSO with the exploration ability of the GWO. During the optimization process, the main target is minimizing the root mean square error (RMSE) between the original experimental data and the estimated data. Three different solar PV modules are considered to prove the superiority of the proposed strategy. Three different solar PV panels are used during the evaluation of the proposed strategy. A comparison of PSOGWO with other state-of-the-art methods is made. The obtained results confirmed that the least RMSE values are achieved using PSOGWO for all case studies compared with PSO and GWO optimizers. Almost a perfect agreement between the estimated data and experimental data set is achieved by PSOGWO