Preparation and Characterization of Cerium (III) Doped Captopril Nanoparticles and Study of their Photoluminescence Properties

Indexación: Web of Science. DOAJ.In this research Ce3+ doped Captopril nanoparticles (Ce3+ doped CAP-NP) were prepared by a cold welding process and have been studied. Captopril may be applied in the treatment of hypertension and some types of congestive heart failure and for preventing kidney failure due to high blood pressure and diabetes. CAP-NP was synthesized by a cold welding process. The cerium nitrate was added at a ratio of 10% and the optical properties have been studied by photoluminescence (PL). The synthesized compounds were characterized by Fourier transform infrared spectroscopy. The size of CAP-NP was calculated by X-ray diffraction (XRD). The size of CAP-NP was in the range of 50 nm. Morphology of surface of synthesized nanoparticles was studied by scanning electron microscopy (SEM). Finally the luminescence properties of undoped and doped CAP-NP were compared. PL spectra from undoped CAP-NP show a strong pack in the range of 546 nm after doped cerium ion into the captopril appeared two bands at 680 and 357 nm, which is ascribed to the well-known 5d–4f emission band of the cerium.http://www.degruyter.com/view/j/chem.2016.14.issue-1/chem-2016-0008/chem-2016-0008.xm

Systemic enzyme therapy in chronic venous disease: a review

Chronic venous disease (CVD), a sequel of venous insufficiency, has great medical and socioeconomic impact. Varicose veins and venous ulcer are amongst its commonest manifestations. In CVD, incompetent valves, weakened vascular walls, venous hypertension and increased permeability of venous walls lead to the release of proinflammatory mediators like tumor necrosis factor (TNF)-α, interleukin (IL)-1β, reactive oxygen species (R.O.S.), and reactive nitrogen species (R.N.S.) in the venous milieu. Pharmacotherapy with nonsteroidal anti-inflammatory drugs (NSAIDs) is often used to relieve pain caused by venous disease. However, there is a need for therapies that target the microcirculatory disorders and act on chronic inflammatory processes. Systemic enzyme therapy (SET), with orally administered combination of proteolytic enzymes- trypsin, bromelain, and flavonoid rutoside, has been used since decades for their anti-inflammatory, analgesic, anti-edematous, antithrombotic and antioxidant properties. This review discusses the various relevant pharmacodynamic properties demonstrated by the ingredients, followed by clinical studies of SET, which have demonstrated benefit in both subjective and objective parameters. These studies indicate that SET has good efficacy, tolerability and holds great promise to improve the quality of life of a patient with CVD.  

Morbidity and mortality meetings; a new digital portal to enhance learning and objectivity

Morbidity and mortality meetings have long been part of surgical education and practice. They have undergone several modifications over time to include improvement in patient safety and outcomes as an essential utility of conducting morbidity and mortality meetings. Time and again, it has been proposed in literature that standardisation of case discussion results in the efficiency of these meetings. Learning opportunities can be compiled for system improvement. The current review article was planned to present a newly implemented digital morbidity and mortality portal at the Aga Khan University Hospital (AKUH), Karachi, aiming at homogenising the discussion and to add objectivity to the outcome. It is believed that this uniform system across all surgical specialties may play a significant role in enhancing surgical trainees\u27 learning experience

Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things

The Internet of bio-nano things (IoBNT) is an emerging paradigm employing nanoscale (~1–100 nm) biological transceivers to collect in vivo signaling information from the human body and communicate it to healthcare providers over the Internet. Bio-nano-things (BNT) offer external actuation of in-body molecular communication (MC) for targeted drug delivery to otherwise inaccessible parts of the human tissue. BNTs are inter-connected using chemical diffusion channels, forming an in vivo bio-nano network, connected to an external ex vivo environment such as the Internet using bio-cyber interfaces. Bio-luminescent bio-cyber interfacing (BBI) has proven to be promising in realizing IoBNT systems due to their non-obtrusive and low-cost implementation. BBI security, however, is a key concern during practical implementation since Internet connectivity exposes the interfaces to external threat vectors, and accurate classification of anomalous BBI traffic patterns is required to offer mitigation. However, parameter complexity and underlying intricate correlations among BBI traffic characteristics limit the use of existing machine-learning (ML) based anomaly detection methods typically requiring hand-crafted feature designing. To this end, the present work investigates the employment of deep learning (DL) algorithms allowing dynamic and scalable feature engineering to discriminate between normal and anomalous BBI traffic. During extensive validation using singular and multi-dimensional models on the generated dataset, our hybrid convolutional and recurrent ensemble (CNN + LSTM) reported an accuracy of approximately ~93.51% over other deep and shallow structures. Furthermore, employing a hybrid DL network allowed automated extraction of normal as well as temporal features in BBI data, eliminating manual selection and crafting of input features for accurate prediction. Finally, we recommend deployment primitives of the extracted optimal classifier in conventional intrusion detection systems as well as evolving non-Von Neumann architectures for real-time anomaly detection

Multi-locus sequence type analysis of Shigellas pp. Isolates from Tehran, Iran

Background and Objectives: Strains of Shigella spp. can cause shigellosis, or bacillary dysentery. That is a public health problem worldwide. The aim of this study was to describe the population structure and genetic relatedness of multidrug resistant S. sonnei and S. flexneri isolated during a one year period from children with diarrhea in Tehran, Iran. Materials and Methods: A total of 70 Shigella spp. were detected during the study period. Twenty MDR isolates of Shigella spp. were randomly selected and used in this study. Bacterial identification was performed by conventional biochemical and serological and confirmed by molecular method. After antimicrobial susceptibility testing, we used Multilocus sequence typing (MLST) for subtyping isolates. Results: We found 14 Shigella sonnei and 6 Shigella flexneri isolates. Results of MLST showed five sequence types (ST) (145, 152, 241, 245, 1502) and BURST analysis revealed the largest number of single locus variant (SLV) and highest frequency (FREQ) for ST152. ST 152 with nine members was predicted as the founder by BURST. Frequency for ST 1502 and ST 245 was four isolates and the least frequency was seen for ST 241 and 145 with one and two members, respectively. ST 145 and ST 245 were described as singletons in BURST. All isolates with ST145 and ST245 were identified as Shigella flexneri. Conclusion: Annual Multi locus sequence typing of MDR Shigella would help us in better understanding of dominant species and comparing our results with the same studies in other countries especially our neighbor countries in source tracking purposes. © Tehran University of Medical Science. All rights reserved

The force within: Recommendations via gravitational attraction between items

Recommendation systems rely on various definitions of similarities. These definitions while having numerous design factors in different domains help identify and recommend relevant content. For example, similarity between users, or items, are measured based on, but not limited to, explicit feedback such as ratings, thumbs up; or/and implicit feedback such as clicks, views etc; or/and based on composition of item such as tags, metadata etc. In this paper, we explore a similarity model while very intuitive to find similar items using a very common natural law of attraction between bodies, that is gravitational law. We show how the two attributes, relative mass and distance between the bodies, of gravitation law can be interpreted for an effective personalized recommendations; in both spatial and non-spatial domains. Finally, we illustrate the use of distance and mass in a non-spatial domain and we exhibit the accuracy in recommendations against popular baselines

A Symmetry-Based Method to Infer Structural Brain Networks from Probabilistic Tractography Data

Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP) have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs) from probabilistic tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold. Instead, we exploit a basic limitation of the tractography process: the observed streamlines from a source to a target do not provide any information about the polarity of the underlying white matter, and so if there are some fibers connecting two voxels (or two ROIs) X and Y, tractography should be able in principle to follow this connection in both directions, from X to Y and from Y to X. We leverage this limitation to formulate the network inference process as an optimization problem that minimizes the (appropriately normalized) asymmetry of the observed network. We evaluate the proposed method using both the FiberCup dataset and based on a noise model that randomly corrupts the observed connectivity of synthetic networks. As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are associated with various neuropsychiatric conditions including depression, anxiety and addiction