Adenosine Receptor Agonist HE-NECA Enhances Antithrombotic Activities of Cangrelor and Prasugrel in vivo by Decreasing of Fibrinogen Density in Thrombus

Blood platelets’ adenosine receptors (AR) are considered to be a new target for the anti-platelet therapy. This idea is based on in vitro studies which show that signaling mediated by these receptors leads to a decreased platelet response to activating stimuli. In vivo evidence for the antithrombotic activity of AR agonists published to date were limited, however, to the usage of relatively high doses given in bolus. The present study was aimed at verifying if these substances used in lower doses in combination with inhibitors of P2Y₁₂ could serve as components of dual anti-platelet therapy. We have found that a selective A_2A agonist 2-hexynyl-5’-N-ethylcarboxamidoadenosine (HE-NECA) improved the anti-thrombotic properties of either cangrelor or prasugrel in the model of ferric chloride-induced experimental thrombosis in mice. Importantly, HE-NECA was effective not only when applied in bolus as other AR agonists in the up-to-date published studies, but also when given chronically. In vitro thrombus formation under flow conditions revealed that HE-NECA enhanced the ability of P2Y₁₂ inhibitors to decrease fibrinogen content in thrombi, possibly resulting in their lower stability. Adenosine receptor agonists possess a certain hypotensive effect and an ability to increase the blood–brain barrier permeability. Therefore, the effects of anti-thrombotic doses of HE-NECA on blood pressure and the blood–brain barrier permeability in mice were tested. HE-NECA applied in bolus caused a significant hypotension in mice, but the effect was much lower when the substance was given in doses corresponding to that obtained by chronic administration. At the same time, no significant effect of HE-NECA was observed on the blood–brain barrier. We conclude that chronic administration of the A_2A agonist can be considered a potential component of a dual antithrombotic therapy. However, due to the hypotensive effect of the substances, dosage and administration must be elaborated to minimize the side-effects. The total number of animals used in the experiments was 146.     

Scalable P2P bot detection system based on network data stream

P2P Botnets are one of the most malevolent threats to the Internet users due to their resiliency against takedown efforts. In this paper, we propose a bot detection system that is capable of detecting stealthy bots in a network. This system treats network traffic as a data stream, segregating the traffic into two parallel streams. The detection is based on failure traffic and communication traffic. The traffic is analyzed during small time window, and the infected hosts are reported immediately. The network administrator can monitor the status of hosts in the network and can take the necessary action before the infected hosts harm the system or can involve in the attacks. Experiments and evaluation of the proposed system on a variety of P2P data transfer applications and P2P botnets have demonstrated high accuracy of detection. The scalability of the proposed system is exhibited through its implementation on Hadoop MapReduce.     

Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems

‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and its ‘Projection Based Learning’ (PBL) algorithm for classification problems in sequential framework is proposed in this paper and is referred to as PBL-McRBFN. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, namely the cognitive component and the meta-cognitive component. The cognitive component is a single hidden layer radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions are considered for proper initialization of new hidden neurons, thus minimizes the misclassification. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository and two practical problems, viz., the acoustic emission signal classification and the mammogram for cancer classification. The statistical performance evaluation on these problems has proven the superior performance of PBL-McRBFN classifier over results reported in the literature.     

An adaptive plant leaf mobile informatics using RSSC

Multimedia Tools and Applications - An automated plant biometric system is now an important step in preserving nature’s biodiversity. This paper presents a novel Relative Sub-image Sparse...     

Formulation and pharmacokinetics of gelucire solid dispersions of flurbiprofen

Context: Development of solid dispersions is to improve the therapeutic efficacy by increasing the drug solubility, dissolution rate, bioavailability as well as to attain rapid onset of action.

Objective: The present research deals with the development of solid dispersions of flurbiprofen which is poorly water soluble to improve the solubility and dissolution rate using gelucires.

Materials and methods: In this study, solid dispersions were prepared following solvent evaporation method using gelucire 44/14 and gelucire 50/13 as carriers in different ratios. Then the formulations were evaluated for different physical parameters, solubility studies, DSC, FTIR studies and in vitro dissolution studies to select the best formulation that shows rapid dissolution rate and finally subjected to pharmacokinetic studies.

Results and discussion: From the in vitro dissolution study, formulation F3 showed the better improvement in solubility and dissolution rate. From the pharmacokinetic evaluation, the control tablets produced peak plasma concentration (C_max) of 9140.84?±?614.36?ng/ml at 3?h T_max and solid dispersion tablets showed C_max?=?11?445.46?±?149.23?ng/ml at 2?h T_max. The area under the curve for the control and solid dispersion tablets was 31?495.16?±?619.92 and 43?126.52?±?688.89?ng h/ml and the mean resident time was 3.99 and 3.68?h, respectively.

Conclusion: From the above results, it is concluded that the formulation of gelucire 44/14 solid dispersions is able to improve the solubility, dissolution rate as well as the absorption rate of flurbiprofen than pure form of drug.     

Gelatin‐carbohydrate phase‐separated hydrogels as bioactive carriers in vaginal delivery: Preparation and physical characterizations

The current study focuses on the alteration of properties of the gelatin hydrogels using polysaccharides (e.g., maltodextrin, dextran, and sodium carboxymethyl cellulose) for probable use in vaginal delivery of antimicrobials. The hydrogels were prepared by varying the proportions of gelatin and polysaccharides and were characterized by microscopy, mechanical testing, and impedance spectroscopy. Metronidazole (MZ), drug of choice for the treatment of bacterial vaginosis, was incorporated within the hydrogels. In vitro release studies of MZ from the hydrogels was studied in‐depth using modified Franz's diffusion cell. Antimicrobial efficiency of the MZ‐loaded hydrogels was tested against E. coli and B. subtilis. The results suggested that the incorporation of polysaccharides resulted in the phase‐separated hydrogels. The properties of the hydrogels was found be suitable for vaginal delivery. The drug release and antimicrobial efficiency from the hydrogels suggested that the developed hydrogels may be used for the delivery of antimicrobials in the vaginal lumen. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40445.     

Data fusion with flexible message composition in Driver-in-the-Loop vehicular CPS

This work addresses a unique data fusion problem in Vehicular Cyber-Physical Systems (VCPS) arising from Human Factors (HF) considerations. Typically, a VCPS message intended for human drivers is composed of many data elements (DEs), and different messages can be fused by the sender before transmission e.g., by eliminating identical (or redundant) DEs in order to save transmission bandwidth in the wireless network. Still, not all distinct DEs can be received properly due to the limited transmission resources available to the sender and/or transmission errors. Subsequently, some of the messages intended for a driver cannot be delivered. On the other hand, a partially delivered message may still be beneficial (in terms of generating some utility) to a driver. More specifically, when considering HF, the DEs can be grouped into two distinct parts: essential and auxiliary. While a partially reconstructed message missing even a single essential DE fails to produce any benefit (or utility) for a driver, each auxiliary DE can independently produce an additional utility so long as all the essential DEs of the message are also available.In this paper, we deal with a new Driver-in-the-Loop Data Fusion Problem (DDFP) with the primary issue being: given a list of out-going messages and a limit on the number of DEs that can be transmitted, how does the sender choose which DEs (each carrying a different utility) to transmit, in order to maximize the system-wide utility at the receiver. We formulate DDFP mathematically, and prove it to be NP-Complete. We study DDFP in both ideal and lossy communication networks, and propose several efficient algorithms for them. Besides the Single-Sender-Single-Receiver model, we also look into DDFP in Multiple-Sender-Single-Receiver and Single-Sender-Multiple-Receiver models with several practical considerations. Numerical results from large scale simulations are also presented.     

Synchrotron infrared microspectroscopy detecting the evolution of Huntington's disease neuropathology and suggesting unique correlates of dysfunction in white versus gray brain matter

Huntington's disease (HD), caused by a mutation of the corresponding gene encoding the protein huntingtin (htt), is characterized by progressive deterioration of cognitive and motor functions, paralleled by extensive loss of striatal neurons. At the cellular level, pathogenesis involves an early and prolonged period of neuronal dysfunction followed by neuronal death. Understanding the molecular events driving these deleterious processes is critical to the successful development of therapies to slow down or halt the progression of the disease. Here, we examined biochemical processes in a HD ex vivo rat model, as well as in a HD model for cultured neurons using synchrotron-assisted Fourier transform infrared microspectroscopy (S-FTIRM). The model, based on lentiviral-mediated delivery of a fragment of the HD gene, expresses a mutant htt fragment in one brain hemisphere and a wild-type htt fragment in the control hemisphere. S-FTIRM allowed for high spatial resolution and distinction between spectral features occurring in gray and white matter. We measured a higher content of β-sheet protein in the striatal gray matter exposed to mutant htt as early as 4 weeks following the initiation of mutant htt exposure. In contrast, white matter tracts did not exhibit any changes in protein structure but surprisingly showed reduced content of unsaturated lipids and a significant increase in spectral features associated with phosphorylation. The former is reminiscent of changes consistent with a myelination deficiency, while the latter is characteristic of early pro-apoptotic events. These findings point to the utility of the label-free FTIRM method to follow mutant htt's β-sheet-rich transformation in striatal neurons ex vivo, provide further evidence for mutant htt amyloidogenesis in vivo, and demonstrate novel chemical features indicative of white matter changes in HD. Parallel studies in cultured neurons expressing the same htt fragments showed similar changes.     

Design and Flow Analysis of MEMS based Piezo-electric Micro Pump

Microsystem Technologies - Controlled drug delivery in medical application plays a prominent role, that can be achieved by micro-drug delivery devices. The efficient working of the controlled drug...     

Network-based detection of Android malicious apps

Users leverage mobile devices for their daily Internet needs by running various mobile applications (apps) such as social networking, e-mailing, news-reading, and video/audio streaming. Mobile device have become major targets for malicious apps due to their heavy network activity and is a research challenge in the current era. The majority of the research reported in the literature is focused on host-based systems rather than the network-based; unable to detect malicious activities occurring on mobile device through the Internet. This paper presents a detection app model for classification of apps. We investigate the accuracy of various machine learning models, in the context of known and unknown apps, benign and normal apps, with or without encrypted message-based app, and operating system version independence of classification. The best resulted machine learning(ML)-based model is embedded into the detection app for efficient and effective detection. We collect a dataset of network activities of 18 different malware families-based apps and 14 genuine apps and use it to develop ML-based detectors. We show that, it is possible to detect malicious app using network traces with the traditional ML techniques, and results revealed the accuracy (95–99.9 %) in detection of apps in different scenarios. The model proposed is proved efficient and suitable for mobile devices. Due to the widespread penetration of Android OS into the market, it has become the main target for the attackers. Hence, the proposed system is deployed on Android environment.