Unused Medication Collection: An Emerging Service-Learning Experience for Pharmacy Students

While the U.S. makes up around 5% of the world’s population, we consume approximately 75% of the world’s prescription drugs—well over 4 billion prescriptions per year. Approximately one third of those are never used, creating an array of public health challenges. These challenges include land and water pollution; unintentional inappropriate use and unintentional human, pet, and wildlife poisonings; and intentional drug abuse and diversion. Misuse of prescription drugs now exceeds that of all illegal drugs combined. Reducing the number of medications prescribed is of primary importance, as well as collecting unnecessary medications from households and disposing of them through environmentally friendly methods. There are a variety of medication collection methods, including public take-back events, permanent drop boxes at pharmacies, collection by police departments, as well as prepaid mailers available from some pharmacies and the Internet. When bulk medication is collected at take-back events or by police departments, it is generally quantified as “pounds collected,” with no determination of specific medications collected, quantities of prescribed medications left unused, or length of household storage beyond the expiration date. Pharmacy students are uniquely positioned to explore these unknowns and develop solutions with their high level of drug expertise. This research study combined with community service events involved the collaboration of pharmacy students with environmentalists, community volunteers, and law enforcement officers to collect and analyze unwanted medications, as well as explore factors pertinent to this public hazard

Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. <br><br> Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data

HIRA directly targets the enhancers of selected cardiac transcription factors during in vitro differentiation of mouse embryonic stem cells

HIRA is a histone chaperone known to modulate gene expression through the deposition of H3.3. Conditional knockout of Hira in embryonic mouse hearts leads to cardiac septal defects. Loss of function mutation in HIRA, together with other chromatin modifiers, was found in patients with congenital heart diseases. However, the effects of HIRA on gene expression at earlier stages of cardiogenic mesoderm differentiation have not yet been studied. Differentiation of mouse embryonic stem cells (mESCs) towards cardiomyocytes mimics some of these early events and is an accepted model of these early stages. We performed RNA-Seq and H3.3-HA ChIP-seq on both WT and Hira-null mESCs and early cardiomyocyte progenitors of both genotypes. Analysis of RNA-seq data showed differential down regulation of cardiovascular development-related genes in Hira-null cardiomyocytes compared to WT cardiomyocytes. We found HIRA-dependent H3.3 deposition at these genes. In particular, we observed that HIRA influenced directly the expression of the transcription factors Gata6, Meis1 and Tbx2, essential for cardiac septation, through H3.3 deposition. We therefore identified new direct targets of HIRA during cardiac differentiation

The diagnosis of hypertrophic cardiomyopathy by cardiovascular magnetic resonance

Hypertrophic cardiomyopathy (HCM) is the most common genetic disease of the heart. HCM is characterized by a wide range of clinical expression, ranging from asymptomatic mutation carriers to sudden cardiac death as the first manifestation of the disease. Over 1000 mutations have been identified, classically in genes encoding sarcomeric proteins. Noninvasive imaging is central to the diagnosis of HCM and cardiovascular magnetic resonance (CMR) is increasingly used to characterize morphologic, functional and tissue abnormalities associated with HCM. The purpose of this review is to provide an overview of the clinical, pathological and imaging features relevant to understanding the diagnosis of HCM. The early and overt phenotypic expression of disease that may be identified by CMR is reviewed. Diastolic dysfunction may be an early marker of the disease, present in mutation carriers prior to the development of left ventricular hypertrophy (LVH). Late gadolinium enhancement by CMR is present in approximately 60% of HCM patients with LVH and may provide novel information regarding risk stratification in HCM. It is likely that integrating genetic advances with enhanced phenotypic characterization of HCM with novel CMR techniques will importantly improve our understanding of this complex disease

An enhanced error model for EKF-based tightly-coupled integration of GPS and land vehicle’s motion sensors

Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers’ measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer’s errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors. The new error model and the augmented EKF design are developed in a tightly-coupled RISS/GPS integrated navigation system. The proposed system was tested on real trajectories’ data under degraded GPS environments, and the results were compared to earlier works on RISS/GPS systems. The findings demonstrated that the proposed enhanced system introduced significant improvements in navigational performance

Exploring Proteus mirabilis methionine tRNA synthetase active site: homology model construction, molecular dynamics, pharmacophore and docking validation

Currently, the treatment of Proteus mirabilis infections is considered to be complicated as the organism has become resistant to numerous antibiotic classes. Therefore, new inhibitors should be developed, targeting bacterial molecular functions. Methionine tRNA synthetase (MetRS), a member of the aminoacyl-tRNA synthetase family, is essential for protein biosynthesis offering a promising target for novel antibiotics discovery. In the context of computer-aided drug design (CADD), the current research presents the construction and analysis of a comparative homology model for P. mirabilis MetRS, enabling development of novel inhibitors with greater selectivity. Molecular Operating Environment (MOE) software was used to build a homology model for P. mirabilis MetRS using Escherichia coli MetRS as a template. The model was evaluated, and the active site of the target protein predicted from its sequence using conservation analysis. Molecular dynamic simulations were performed to evaluate the stability of the modeled protein structure. In order to evaluate the predicted active site interactions, methionine (the natural substrate of MetRS) and several inhibitors of bacterial MetRS were docked into the constructed model using MOE. After validation of the model, pharmacophore-based virtual screening for a systemically prepared dataset of compounds was performed to prove the feasibility of the proposed model, identifying possible parent compounds for further development of MetRS inhibitors against P. mirabilis

INS/GPS/LiDAR integrated navigation system for urban and indoor environments using hybrid scan matching algorithm

This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory

Adaptive covariance estimation method for LiDAR-Aided multi-sensor integrated navigation systems

The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose chan

Contraception use among Muslim women in Alexandria, Egypt: a descriptive pilot study

Background: This pilot study aimed to create a questionnaire survey directed to understand knowledge gaps related to contraception among Muslim women in Alexandria, Egypt, so potential interventions could be designed to enable more informed decision-making. The project was a mixed-method, cross-sectional study using a questionnaire survey.Methods: Participants were randomly selected at outpatient clinics at Alexandria university hospitals in September and October 2020. The inclusion criteria were to be an 18 year old or older woman and able to give consent. The recruitment goal for this pilot study was 100 participants. The consented participants were interviewed about demographics, socio-structural and contraception use. The questionnaire was tested using a focus group of 11 women. The study data was collected using KoBoToolbox and exported to the SPSS software for descriptive analysis. The primary outcome was to validate the survey questionnaire and the secondary outcome to assess knowledge regarding contraception methods and emergency contraception.Results: The age of study participants ranged from 18-60 with a mean of 34 years. Almost all participants had previously heard of various contraceptive methods and 75% used them before. The majority did not know about emergency contraception. Most respondents had a favorable attitude toward family planning, and their primary sources of information were family and friends.Conclusions: Preliminary findings show that most women knew about contraception methods, though few of them heard of emergency contraception. Because of the patriarchal nature of Egyptian society, family planning education should target the whole population