Safety profile of oxcarbazepine: results from a prescription-event monitoring study

Purpose: To monitor safety of oxcarbazepine, prescribed in primary care in England, using prescription-event monitoring (PEM). Methods: Postmarketing surveillance using observational cohort technique of PEM. Exposure data were obtained from dispensed British National Health Service prescriptions issued by general practitioners (GPs) March 2000–July 2003. Demographic, drug utilization, and clinical event data were collected from questionnaires posted to GPs at least 6 months after first prescription date for each patient. Incidence densities (IDs) (number of first reports per 1,000 patient-months of treatment) were calculated and differences for events reported in month 1 (ID1) and months 2–6 (ID2–6) (99% confidence intervals) were examined for changes in event rates. Follow-up and causality assessment of medically significant events were undertaken. Results: The cohort comprised 2,243 patients [mean age 40.4 years; range 2–99 years; standard deviation (SD) 18.8; 46.3% (n = 1,038) male]. Most frequently reported primary indications were epilepsy, convulsion (n = 1,111; 49.5%, n = 209; 9.3%, respectively). GPs recorded 932 reasons for stopping medication in 698 (31.1%) patients; most frequent clinical reason “drowsiness/sedation” (n = 57; 2.5% of cohort). Clinical events (excluding indication) associated with starting treatment (lower 99% CI > 0) included: “drowsiness/sedation” (ID1-ID2–6 = 14.2), “nausea/vomiting” (ID1-ID2–6 = 13.0), and dizziness (ID1-ID2–6 = 11.6). Events followed up and assessed as probably related to oxcarbazepine use included rash (7 of 11) and hyponatremia (15 of 38). Discussion:  There were no serious adverse drug reactions reported during this study. Results of the study should be taken in context with other epidemiologic studies

Smart Antennas and Intelligent Sensors Based Systems: Enabling Technologies and Applications

open access articleThe growing communication and computing capabilities in the devices enlarge the connected world and improve the human life comfort level. The evolution of intelligent sensor networks and smart antennas has led to the development of smart devices and systems for real-time monitoring of various environments. The demand of smart antennas and intelligent sensors significantly increases when dealing with multiuser communication system that needs to be adaptive, especially in unknown adverse environment [1–3]. The smart antennas based arrays are capable of steering the main beam in any desired direction while placing nulls in the unwanted directions. Intelligent sensor networks integration with smart antennas will provide algorithms and interesting application to collect various data of environment to make intelligent decisions [4, 5]. The aim of this special issue is to provide an inclusive vision on the current research in the area of intelligent sensors and smart antenna based systems for enabling various applications and technologies. We cordially invite some researchers to contribute papers that discuss the issues arising in intelligent sensors and smart antenna based system. Hence, this special issue offers the state-of-the-art research in this field

An Improved Algorithm for Eye Corner Detection

In this paper, a modified algorithm for the detection of nasal and temporal eye corners is presented. The algorithm is a modification of the Santos and Proenka Method. In the first step, we detect the face and the eyes using classifiers based on Haar-like features. We then segment out the sclera, from the detected eye region. From the segmented sclera, we segment out an approximate eyelid contour. Eye corner candidates are obtained using Harris and Stephens corner detector. We introduce a post-pruning of the Eye corner candidates to locate the eye corners, finally. The algorithm has been tested on Yale, JAFFE databases as well as our created database

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment