Security Concepts in IPv6 Based Aeronautical Communications

Space scienc

Chapter Security Concepts in IPv6 Based Aeronautical Communications

Space scienc

N-Benzyl-(2,5-dioxopyrrolidin-1-yl)propanamide (AS-1) with hybrid structure as a candidate for a broad-spectrum antiepileptic drug

In our recent studies, we identified compound N-benzyl-2-(2,5-dioxopyrrolidin-1-yl)propanamide (AS-1) as a broad-spectrum hybrid anticonvulsant which showed potent protection across the most important animal acute seizure models such as the maximal electroshock (MES) test, the subcutaneous pentylenetetrazole (s.c. PTZ) test, and the 6-Hz (32 mA) test in mice. Therefore, AS-1 may be recognized as a candidate for new anticonvulsant effective in different types of human epilepsy with a favorable safety margin profile determined in the rotarod test in mice. In the aim of further pharmacological evaluation of AS-1, in the current study, we examined its activity in the 6-Hz (44 mA) test, which is known as the model of drug-resistant epilepsy. Furthermore, we determined also the antiseizure activity in the kindling model of epilepsy induced by repeated injection of pentylenetetrazole (PTZ) in mice. As a result, AS-1 revealed relatively potent protection in the 6-Hz (44 mA) test, as well as delayed the progression of kindling induced by repeated injection of PTZ in mice at doses of 15 mg/kg, 30 mg/kg, and 60 mg/kg. Importantly, the isobolographic analysis showed that a combination of AS-1 and valproic acid (VPA) at the fixed ratio of 1:1 displayed a supra-additive (synergistic) interaction against PTZinduced seizures inmice. Thus, AS-1may be potentially used in an add-on therapy with VPA. Moreover, incubation of zebrafish larvae with AS-1 substantially decreased the number, cumulative but not the mean duration of epileptiform-like events in electroencephalographic assay. Finally, the in vitro ADME-Tox studies revealed that AS-1 is characterized by a very good permeability in the parallel artificial membrane permeability assay test, excellent metabolic stability on human liver microsomes (HLMs), no significant influence on CYP3A4/CYP2D6 activity, and moderate inhibition of CYP2C9 in a concentration of 10 $\mu$M, as well as no hepatotoxic properties in HepG2 cells (concentration of 10 $\mu$M)

Discovery of (R)-N-benzyl-2-(2,5-dioxopyrrolidin-1-yl)propanamide [(R)-AS-1], a novel orally bioavailable EAAT2 modulator with drug-like properties and potent antiseizure activity in vivo

[Image: see text] (R)-7 [(R)-AS-1] showed broad-spectrum antiseizure activity across in vivo mouse seizure models: maximal electroshock (MES), 6 Hz (32/44 mA), acute pentylenetetrazol (PTZ), and PTZ-kindling. A remarkable separation between antiseizure activity and CNS-related adverse effects was also observed. In vitro studies with primary glia cultures and COS-7 cells expressing the glutamate transporter EAAT2 showed enhancement of glutamate uptake, revealing a stereoselective positive allosteric modulator (PAM) effect, further supported by molecular docking simulations. (R)-7 [(R)-AS-1] was not active in EAAT1 and EAAT3 assays and did not show significant off-target activity, including interactions with targets reported for marketed antiseizure drugs, indicative of a novel and unprecedented mechanism of action. Both in vivo pharmacokinetic and in vitro absorption, distribution, metabolism, excretion, toxicity (ADME-Tox) profiles confirmed the favorable drug-like potential of the compound. Thus, (R)-7 [(R)-AS-1] may be considered as the first-in-class small-molecule PAM of EAAT2 with potential for further preclinical and clinical development in epilepsy and possibly other CNS disorders

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selection of the best-performing classifiers as well as for the analysis of higher-dimensionality data. The last aspect is especially important when analyzing large datasets. The approach of cross-validation between different datasets for supervised learning is applied in the meta-algorithm. Three machine-learning methods allowing a user to classify e-mails as desirable (ham) or potentially harmful (spam) messages were compared in the paper to illustrate the operation of the meta-algorithm. The used methods are simple, but as the results showed, they are powerful enough. We use the following classifiers: k-nearest neighbours (k-NNs), support vector machines (SVM), and the naïve Bayes classifier (NB). The conducted research gave us the conclusion that multinomial naïve Bayes classifier can be an excellent weapon in the fight against the constantly increasing amount of spam messages. It was also confirmed that the proposed solution gives very accurate results