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Implementing the 3R initiative to reduce animal experiments in brain penetration prediction for CNS-targeting drugs requires more predictive in vitro and in silico models. However, animal studies are still indispensable to obtaining brain concentration and determining the prediction performance of in vitro models. To reveal species differences and provide reliable data for IVIVE, in vitro models are required. Systems overexpressing MDR1 and BCRP are widely used to predict BBB penetration, highlighting the impact of the in vitro system on predictive performance. In this study, endogenous Abcb1 knock-out MDCKII cells overexpressing MDR1 of human, mouse, rat or cynomolgus monkey origin were used. Good correlations between ERs of 83 drugs determined in each cell line suggest limited species specificities. All cell lines differentiated CNS-penetrating compounds based on ERs with high efficiency and sensitivity. The correlation between in vivo and predicted Kp,uu,brain was the highest using total ER of human MDR1 and BCRP and optimized scaling factors. MDR1 interactors were tested on all MDR1 orthologs using digoxin and quinidine as substrates. We found several examples of inhibition dependent on either substrate or transporter abundance. In summary, this assay system has the potential for early-stage brain penetration screening. IC50 comparison between orthologs is complex; correlation with transporter abundance data is not necessarily proportional and requires the understanding of modes of transporter inhibition.

Abstract Background Altered glutamatergic neurotransmission and neuropeptide levels play a central role in migraine pathomechanism. Previously, we confirmed that kynurenic acid, an endogenous glutamatergic antagonist, was able to decrease the expression of pituitary adenylate cyclase-activating polypeptide 1–38, a neuropeptide with known migraine-inducing properties. Hence, our aim was to reveal the role of the peripheral kynurenine pathway (KP) in episodic migraineurs. We focused on the complete tryptophan (Trp) catabolism, which comprises the serotonin and melatonin routes in addition to kynurenine metabolites. We investigated the relationship between metabolic alterations and clinical characteristics of migraine patients. Methods Female migraine patients aged between 25 and 50 years (n = 50) and healthy control subjects (n = 34) participated in this study. Blood samples were collected from the cubital veins of subjects (during both the interictal/ictal periods in migraineurs, n = 47/12, respectively). 12 metabolites of Trp pathway were determined by neurochemical measurements (UHPLC-MS/MS). Results Plasma concentrations of the most Trp metabolites were remarkably decreased in the interictal period of migraineurs compared to healthy control subjects, especially in the migraine without aura (MWoA) subgroup: Trp (p

Single-stranded DNA-binding protein (SSB) is a bacterial interaction hub and an appealing target for antimicrobial therapy. Understanding the structural adaptation of the disordered SSB C-terminus (SSB-Ct) to DNA metabolizing enzymes (e.g., ExoI and RecO) is essential for designing high-affinity SSB mimetic inhibitors. Molecular dynamics simulations revealed the transient interactions of SSB-Ct with two hot spots on ExoI and RecO. The residual flexibility of the peptide–protein complexes allows adaptive molecular recognition. Scanning with non-canonical amino acids revealed that modifications at both termini of SSB-Ct could increase the affinity, supporting the two-hot-spot binding model. Combining unnatural amino acid substitutions on both segments of the peptide resulted in enthalpy-enhanced affinity, accompanied by enthalpy–entropy compensation, as determined by isothermal calorimetry. NMR data and molecular modeling confirmed the reduced flexibility of the improved affinity complexes. Our results highlight that the SSB-Ct mimetics bind to the DNA metabolizing targets through the hot spots, interacting with both of segments of the ligands.

A tanulmány áttekinti a rámizmusnak a Magyar Királyság és Erdély tudományos, pedagógiai és kulturális életében kimutatható helyi hatásait, a hazai rámista nyelvtani, retorikai, homiletikai és logikai kiadványokat, a rámista megfontolásoknak és összetevőknek a hazai oktatásban való jelenlétét. A magyarországi és erdélyi recepció bemutatott tényei alapján kijelenthető, hogy a legfontosabb kálvinista iskolákban, a gyulafehérvári, a kolozsvári, a sárospataki, a váradi és a debreceni kollégiumban a 17. század közepén és második felében a tudományok egész rendszerezésére kiterjedő rámista hatás érvényesült, amelynek egyes összetevői egészen a 18. század közepéig megmaradtak. Az alapvetően a melanchthoniánus rendszerezések mellett kitartó lutheránus iskolákat szórványos, de szintén nem jelentéktelen rámista hatás érte el, többnyire szinkretikus formákban. A rámista magyar írók – gyakran puritán meggyőződést kifejező – irodalmi műveit fogalmi egyszerűség, áttekinthetőség, követhetőség jellemzi. A racionális érvelésben csak kevés belletrisztikus elem, érzelmi hatáskeltés, eredeti irodalmi eszköz fordul elő. Az elsősorban a gondolatmenet értelmi rendjére ügyelő kifejtési mód összefügg a szerzők rámista meggyőződésével. Szigorú rámista elméleti alapról csak elenyészően csekély hányada látható be azoknak a deviációs műveleteknek, amelyek az irodalmi műveket megkülönböztetik a szabatos, tárgyilagos, racionális diskurzus természetes rendjétől.

Novel 2- or 4-phosphonated 13α-estrone derivatives were synthesized via the Hirao reaction. Bromo regioisomers (2- or 4-) of 13α-estrone and its 3-benzyl or 3-methyl ether were reacted with diethyl phosphite or diphenylphosphine oxide using Pd(PPh3)4 as catalyst under microwave irradiation. The influence of the new compounds on the transport function of the organic anion transporting polypeptide OATP2B1 was investigated by measuring Cascade Blue uptake. Derivatives bearing a 3-benzyl ether function displayed substantial submicromolar OATP2B1 inhibitory activity. The inhibitory effects of the compounds on human placental steroid sulfatase (STS) and 17β-hydroxysteroid dehydrogenase type 1 isozyme (17β-HSD1) were investigated by in vitro radiosubstrate incubation methods. None of the test compounds inhibited the STS markedly. The structure–activity relationship evaluation revealed that 2-substituted 3-hydroxy derivatives are able to inhibit the 17β-HSD1 enzyme with submicromolar IC50 values. Dual OATP2B1 and 17β-HSD1 inhibitors have been identified.

Studies at Western European Academies and the Hungarian Ramism. Recent schools of the history of ideas do not study the „influence” of one outstanding personality primarily but more the way numerous thinkers receive and utilise elements of the sets of ideas connected to the name. It is especially true in the case of Petrus Ramus. The interpretation of Ramism is not feasible if we focus on exploring one coherent intention of the author (or several of them). It seems more fruitful to study the multi-faceted community of interpreters that was driven to hold certain positions by personal conviction, institutional needs, or confessional identity. These people found the suitable framework for asserting their positions in one or the other of the many processes of Ramism, that is, they connected their aspirations to paradigms that they believed to be Ramist. His earliest acquaintances from Hungary and Transylvania met Ramus in Paris, several of them before his conversion to Protestantism (1561). Outlining these early connections poses no problem, since there are only a handful of Hungarians who had personal connection to him. All of them are members of the humanist elite, professional philologists. The influence of Ramus that came from the German academic world to Hungary and Transylvania seems far more important than the sporadic and haphazard personal connections. These influences are much more multitudinous, because they grow out of the system of connections organically embedded in the studies of Hungarian youngsters at foreign universities. Obviously, Ramus could affect Hungarian and Transylvanian young people by this way only after his reception in Germany started to take shape. This means that this aspect of the processes can be discussed beginning with the 1570s. Early traces are sporadic, and a deeper, systemic influence on thinking and history of ideas in Hungary and Transylvania by Ramism is relevant only from the early 17th century.

Nasal drug delivery has become a popular research field in the last years. This is not surprising since the nose possesses unique anatomical and physical properties. Via the nasal mucosa local, systemic, and directly central nerve systemic (CNS) effect is achievable. Powders have favorable physicochemical properties over liquid formulations. Lamotrigine (LAM) is an antiepileptic agent with a relatively mild side effect spectrum, but only available in tablet form on market. Reducing the particle size to the nano range can affect the bioavailability of pharmaceutical products. The aim of this article was to continue the work started, compare the in vitro properties of a nanonized lamotrigine containing nasal powder (nanoLAMpowder) and its physical mixture (PM) that were prepared by dry milling. Moreover, to study their trans-epithelial absorption to reach the blood and target the brain by axonal transport. Due to the dry milling technique, the particle size of LAM, their surface and also their structure changed that led to higher in vitro dissolution and permeability rate. The results of the in vivo tests showed that the axonal transport of the drug was assumable by both intranasal formulations because the drug was present in the brain within a really short time, but the LAM from the nanoLAMpowder liberated even faster.

The aim of this study was to optimize the formulation of meloxicam (MEL)-containing human serum albumin (HSA) nanoparticles for nose-to-brain via a quality by design (QbD) approach. Liquid and dried formulations of nanoparticles containing Tween 80 and without the surfactant were investigated. Various properties, such as the Z-average, zeta potential, encapsulation efficacy (EE), conjugation of MEL and HSA, physical stability, in vitro dissolution, in vitro permeability, and in vivo plasma and brain distribution of MEL were characterized. From a stability point of view, a solid product (Mel-HSA-Tween) is recommended for further development since it met the desired critical parameters (176 ± 0.3 nm Z-average, 0.205 ± 0.01 PdI, −14.1 ± 0.7 mV zeta potential) after 6 months of storage. In vitro examination showed a significantly increased drug dissolution and permeability of MEL-containing nanoparticles, especially in the case of applying Tween 80. The in vivo studies confirmed both the trans-epithelial and axonal transport of nanoparticles, and a significantly higher cerebral concentration of MEL was detected with nose-to-brain delivery, in comparison with intravenous or per os administration. These results indicate intranasal the administration of optimized MEL-containing HSA formulations as a potentially applicable “value-added” product for the treatment of neuroinflammation.

Novel BODIPY–estrone conjugates were synthesized via Cu(I)-catalyzed azide–alkyne cycloaddition (CuAAC). Estrone-alkynes or an estrone-azide as starting compounds were synthesized via Michael addition or Sonogashira reaction as key steps. Fluorescent dyes based on BODIPY-core were provided by azide or alkyne functional groups. Fluorescent labeling of estrone was efficiently achieved at the C-2 or C-15 position. The newly-elaborated coupling procedures might have a broad applicability in the synthesis of fluorescent-labeled estrone conjugates suitable for biological assays.

The aim of this article was to study the trans-epithelial absorption to reach the blood and to target the brain by axonal transport using nasal formulations with nanonized meloxicam (nano MEL spray) and its salt form known as meloxicam potassium monohydrate (MELP spray). The physicochemical properties and the mucoadhesivity of nasal formulations were controlled. In vitro and in vivo studies were carried out. These forms were first investigated in “nose-to-brain” relation. It was found that the in vitro study and in vivo study did not show any significant correlation. In vitro experiments demonstrated faster dissolution rate and higher diffusion of MELP from the spray compared with the nano MEL spray. The administration of the nano MEL spray resulted in faster absorption and constant plasma concentration of the drug after five minutes of administration as compared to MELP. The axonal transport of the drug was justified. MEL appeared in the brain tissues after the first five minutes of administration in the case of both spray forms, but its amount was too small in comparison with the total plasma concentration. The application of the nano MEL spray resulted in the same AUC in the brain as the intravenous injection. The “nose-to-blood” results predicted the nasal applicability of MEL and MELP in pain management. The “nose-to-brain” pathway requires further study.

Abstract Clustering is widely used in cloud computing studies to extract vital information. These studies have ignored investigating the potential improvements in clustering quality from better selection of its dimensions and methods. Consequently, developing an automated technique to perform such a selection was not addressed thoroughly. Most of the recent attempts either relied on feature reduction or general non-automated techniques, which were deemed unreliable for sufficient selection. Therefore, we first conducted a comprehensive investigation to study the impact of selecting better clustering dimensions and methods. Our results indicate achieving significant improvement by 15–70% points through better selection. Then, we developed a novel technique (EFection) to detect the best selection in advance using a combination of internal validation metrics (Davies–Bouldin) and the Pearson correlation coefficient. We evaluate our technique’s accuracy by comparing the clustering quality of its suggestions with that of the optimal selection. We then compare EFection’s performance with recent attempts to measure its superiority. Finally, we validate its applicability when adopted in cloud clustering-based studies. The results show that EFection offers high accuracy, around 83%, and surpasses prior art by 11%.

Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation.

Abstract Serverless computing is stepping forward to provide a cloud environment that mainly focuses on managing infrastructure, resources and configurations on the behalf of a user. Research in this field can’t rely on commercial providers such as AWS and Azure, as their inflexibility and cost often limits the required levels of reproducibility and scalability. Therefore, simulators have been opted as an alternative solution by the research community. They offer a reduced-cost and easy-setup environment. To get respectable precision, simulators use real traces collected and offered by commercial providers. These traces represent comprehensive information of executed tasks that reflect user behaviour. Due to serverless computing’s recency, typical workload traces employed by IaaS simulators are not well adoptable to the new computing model. In this paper, we propose an approach for generating realistic serverless traces. We enhance our previous generator approach that was based on the Azure Functions dataset. Our new, genetic algorithm based approach improves the statistical properties of the generated traces. We also enabled arbitrary scaling of the workload, while maintaining real users’ behaviour. These advances further support reproducibility in the serverless research community. We validated the results of our generator approach using the coefficient of determination ( $$R^2$$ R 2 ), which shows that our generated workload closely matches the original dataset’s characteristics in terms of execution time, memory utilisation as well as user participation percentage. To demonstrate the benefits of the reusability of the generated traces, we applied them with a diverse set of simulators and shown that they offer reproducible results independently of the simulator used.

The modelling of IoT applications utilising the resources of cloud and fog computing is not straightforward because they have to support various trigger-based events that make human life easier. The sequence of tasks, such as performing a service call, receiving a data packet in the form of a message sent by an IoT device, and managing actuators or executing a computational task on a virtual machine, are often associated with and composed of IoT workflows. The development and deployment of such IoT workflows and their management systems in real life, including communication and network operations, can be complicated due to high operation costs and access limitations. Therefore, simulation solutions are often applied for such purposes. In this paper, we introduce a novel simulator extension of the DISSECT-CF-Fog simulator that leverages the workflow scheduling and its execution capabilities to model real-life IoT use cases. We also show that state-of-the-art simulators typically omit the IoT factor in the case of the scientific workflow evaluation. Therefore, we present a scalability study focusing on scientific workflows and on the interoperability of scientific and IoT workflows in DISSECT-CF-Fog.

The inevitable evolution of information technology has led to the creation of IoT-Fog-Cloud systems, which combine the Internet of Things (IoT), Cloud Computing and Fog Computing. IoT systems are composed of possibly up to billions of smart devices, sensors and actuators connected through the Internet, and these components continuously generate large amounts of data. Cloud and fog services assist the data processing and storage needs of IoT devices. The behaviour of these devices can change dynamically (e.g. properties of data generation or device states). We refer to systems allowing behavioural changes in physical position (i.e. geolocation), as the Internet of Mobile Things (IoMT). The investigation and detailed analysis of such complex systems can be fostered by simulation solutions. The currently available, related simulation tools are lacking a generic actuator model including mobility management. In this paper, we present an extension of the DISSECT-CF-Fog simulator to support the analysis of arbitrary actuator events and mobility capabilities of IoT devices in IoT-Fog-Cloud systems. The main contributions of our work are: (i) a generic actuator model and its implementation in DISSECT-CF-Fog, and (ii) the evaluation of its use through logistics and healthcare scenarios. Our results show that we can successfully model IoMT systems and behavioural changes of actuators in IoT-Fog-Cloud systems in general, and analyse their management issues in terms of usage cost and execution time.

In the age of the Internet of Things (IoT), more and more sensors, actuators and smart devices get connected to the network. Application providers often combine this connectivity with novel scenarios involving cloud computing. Before implementing changes in these large-scale systems, an in-depth analysis is often required to identify governance models, bottleneck situations, costs and unexpected behaviours. Distributed systems simulators help in such analysis, but they are often problematic to apply in this newly emerging domain. For example, most simulators are either too detailed (e.g., need extensive knowledge on networking), or not extensible enough to support the new scenarios. To overcome these issues, we discuss our IoT cost analysis oriented extension of DIScrete event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF). Thus, we present an in-depth analysis of IoT and cloud related pricing models of the most widely used commercial providers. Then, we show how the fundamental properties (e.g., data production frequency) of IoT entities could be linked to the identified pricing models. To allow the adoption of unforeseen scenarios and pricing schemes, we present a declarative modelling language to describe these links. Finally, we validate our extensions by analysing the effects of various identified pricing models through five scenarios coming from the field of weather forecasting.