Evolution of Preprofessional Pharmacy Curricula

Objectives. To examine changes in preprofessional pharmacy curricular requirements and trends, and determine rationales for and implications of modifications. Methods. Prerequisite curricular requirements compiled between 2006 and 2011 from all doctor of pharmacy (PharmD) programs approved by the Accreditation Council of Pharmacy Education were reviewed to ascertain trends over the past 5 years. An online survey was conducted of 20 programs that required either 3 years of prerequisite courses or a bachelor’s degree, and a random sample of 20 programs that required 2 years of prerequisites. Standardized telephone interviews were then conducted with representatives of 9 programs. Results. In 2006, 4 programs required 3 years of prerequisite courses and none required a bachelor’s degree; by 2011, these increased to 18 programs and 7 programs, respectively. Of 40 programs surveyed, responses were received from 28 (70%), 9 (32%) of which reported having increased the number of prerequisite courses since 2006. Reasons given for changes included desire to raise the level of academic achievement of students entering the PharmD program, desire to increase incoming student maturity, and desire to add clinical sciences and experiential coursework to the pharmacy curriculum. Some colleges and schools experienced a temporary decrease in applicants. Conclusions. The preprofessional curriculum continues to evolve, with many programs increasing the number of course prerequisites. The implications of increasing prerequisites were variable and included a perceived increase in maturity and quality of applicants and, for some schools, a temporary decrease in the number of applicants

An Experimental Analysis of Deep Learning Architectures for Supervised Speech Enhancement

Recent speech enhancement research has shown that deep learning techniques are very effective in removing background noise. Many deep neural networks are being proposed, showing promising results for improving overall speech perception. The Deep Multilayer Perceptron, Convolutional Neural Networks, and the Denoising Autoencoder are well-established architectures for speech enhancement; however, choosing between different deep learning models has been mainly empirical. Consequently, a comparative analysis is needed between these three architecture types in order to show the factors affecting their performance. In this paper, this analysis is presented by comparing seven deep learning models that belong to these three categories. The comparison includes evaluating the performance in terms of the overall quality of the output speech using five objective evaluation metrics and a subjective evaluation with 23 listeners; the ability to deal with challenging noise conditions; generalization ability; complexity; and, processing time. Further analysis is then provided while using two different approaches. The first approach investigates how the performance is affected by changing network hyperparameters and the structure of the data, including the Lombard effect. While the second approach interprets the results by visualizing the spectrogram of the output layer of all the investigated models, and the spectrograms of the hidden layers of the convolutional neural network architecture. Finally, a general evaluation is performed for supervised deep learning-based speech enhancement while using SWOC analysis, to discuss the technique’s Strengths, Weaknesses, Opportunities, and Challenges. The results of this paper contribute to the understanding of how different deep neural networks perform the speech enhancement task, highlight the strengths and weaknesses of each architecture, and provide recommendations for achieving better performance. This work facilitates the development of better deep neural networks for speech enhancement in the future

High-level multiplexing in digital PCR with intercalating dyes by coupling real-time kinetics and melting curve analysis.

Digital polymerase chain reaction (dPCR) is a mature technique that has enabled scientific breakthroughs in several fields. However, this technology is primarily used in research environments with high-level multiplexing representing a major challenge. Here, we propose a novel method for multiplexing, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic information in real-time amplification data and the thermodynamic melting profile using an affordable intercalating dye (EvaGreen). The method trains a system comprised of supervised machine learning models for accurate classification, by virtue of the large volume of data from dPCR platforms. As a case study, we develop a new 9-plex assay to detect mobilised colistin resistant (mcr) genes as clinically relevant targets for antimicrobial resistance. Over 100,000 amplification events have been analysed, and for the positive reactions, the AMCA approach reports a classification accuracy of 99.33 ± 0.13%, an increase of 10.0% over using melting curve analysis. This work provides an affordable method of high-level multiplexing without fluorescent probes, extending the benefits of dPCR in research and clinical settings

Cauda equnia syndrome due to Brucella spondylodiscitis and epidural abscess formation: A case report

Brucellosis is an infection with a widening clinical disease spectrum, has been reported as the causative agent of lumbar spine complications but rarely accompanying CES Injury. We report a female patient with Brucella spondylodiscitis affecting the lumbosacral region resulting in CES due to epidural abscess formation. Brucella spondylodiscitis should be suspected in patients with unexplained neurological features and low back pain in endemic regions. © 201

Emerging Carbapenem-Resistant Pseudomonas aeruginosa Isolates Carrying bla_IMP Among Burn Patients in Isfahan, Iran

Background: Metallo-β-lactamase (MBL)-producing Pseudomonas aeruginosa is a significant pathogen in burn patients. Objectives: The aim of this study was to determine the prevalence of carbapenem-resistant P. aeruginosa isolates, including those resistant to imipenemase (IMP), in a burn unit in Isfahan, Iran. Patients and Methods: One hundred and fifty P. aeruginosa isolates from burn patients were tested for antibiotic susceptibility by the disc diffusion method in accordance with CLSI guidelines. Production of MBL was identified with the EDTA disk method. DNA was purified from the MBL-positive isolates, and detection of the blaIMP gene was performed with PCR. Results: Fifty-seven out of 150 (38%) isolates were multi-drug resistant (MDR), and 93 (62%) were extensively-drug resistant (XDR). Among all isolates, the resistance rate to ciprofloxacin, tobramycin, imipenem, meropenem, amikacin, ceftazidime, and cefepime was higher than 90%, while the resistance rates to piperacillin/tazobactam and aztreonam were 70.7% and 86%, respectively. Colistin and polymyxin B remained the most effective studied antibiotics. All of the imipenem-resistant P. aeruginosa isolates were MBL-positive, and 107 out of 144 (74.3%) of the MBL isolates were positive for the blaIMP gene. Conclusions: The results of this study show that the rate of P. aeruginosa-caused burn wound infections was very high, and many of the isolates were resistant to three or more classes of antimicrobials. Such extensive resistance to antimicrobial classes is important because few treatment options remain for patients with burn wound infections. blaIMP-producing P. aeruginosa isolates are a rising threat in burn-care units, and should be controlled by conducting infection-control assessments

Amplification curve analysis: Data-driven multiplexing using real-time digital PCR

Information about the kinetics of PCR reactions are encoded in the amplification curve. However, in digital PCR (dPCR), this information is typically neglected by collapsing each amplification curve into a binary output (positive/negative). Here, we demonstrate that the large volume of raw data obtained from realtime dPCR instruments can be exploited to perform data-driven multiplexing in a single fluorescent channel using machine learning methods, by virtue of the information in the amplification curve. This new approach, referred to as amplification curve analysis (ACA), was shown using an intercalating dye (EvaGreen), reducing the cost and complexity of the assay and enabling the use of melting curve analysis for validation. As a case study, we multiplexed 3 carbapenem-resistant genes to show the impact of this approach on global challenges such as antimicrobial resistance. In the presence of single targets, we report a classification accuracy of 99.1% (N = 16188) which represents a 19.7% increase compared to multiplexing based on the final fluorescent intensity. Considering all combinations of amplification events (including coamplifications), the accuracy was shown to be 92.9% (N = 10383). To support the analysis, we derived a formula to estimate the occurrence of co-amplification in dPCR based on multivariate Poisson statistics, and suggest reducing the digital occupancy in the case of multiple targets in the same digital panel. The ACA approach takes a step towards maximizing the capabilities of existing real-time dPCR instruments and chemistries, by extracting more information from data to enable data-driven multiplexing with high accuracy. Furthermore, we expect that combining this method with existing probe-based assays will increase multiplexing capabilities significantly. We envision that once emerging point-of-care technologies can reliably capture real-time data from isothermal chemistries, the ACA method will facilitate the implementation of dPCR outside of the lab

H -supplemented modules with respect to a preradical

Let M be a right R-module and τ a preradical. We call M τ-H-supplemented if for every submodule A of M there exists a direct summand D of M such that (A+D)/D⊆τ(M/D) and (A+D)/A⊆τ(M/A). Let τ be a cohereditary preradical. Firstly, for a duo module M=M₁⊕M₂ we prove that M is τ-H-supplemented if and only if M₁ and M₂ are τ-H-supplemented. Secondly, let M=⊕ⁿi=1Mi be a τ-supplemented module. Assume that Mi is τ-Mj-projective for all j>i. If each Mi is τ-H-supplemented, then M is τ-H-supplemented. We also investigate the relations between τ-H-supplemented modules and τ-(⊕-)supplemented modules