DNA Microarray Profiling of a Diverse Collection of Nosocomial Methicillin-Resistant Staphylococcus aureus Isolates Assigns the Majority to the Correct Sequence Type and Staphylococcal Cassette Chromosome mec (SCCmec) Type and Results in the Subsequent Identification and Characterization of Novel SCCmec-SCCM1 Composite Islands

One hundred seventy-five isolates representative of methicillin-resistant Staphylococcus aureus (MRSA) clones that predominated in Irish hospitals between 1971 and 2004 and that previously underwent multilocus sequence typing (MLST) and staphylococcal cassette chromosome mec (SCCmec) typing were characterized by spa typing (175 isolates) and DNA microarray profiling (107 isolates). The isolates belonged to 26 sequence type (ST)-SCCmec types and subtypes and 35 spa types. The array assigned all isolates to the correct MLST clonal complex (CC), and 94% (100/107) were assigned an ST, with 98% (98/100) correlating with MLST. The array assigned all isolates to the correct SCCmec type, but subtyping of only some SCCmec elements was possible. Additional SCCmec/SCC genes or DNA sequence variation not detected by SCCmec typing was detected by array profiling, including the SCC-fusidic acid resistance determinant Q6GD50/fusC. Novel SCCmec/SCC composite islands (CIs) were detected among CC8 isolates and comprised SCCmec IIA-IIE, IVE, IVF, or IVg and a ccrAB4-SCC element with 99% DNA sequence identity to SCC(M1) from ST8/t024-MRSA, SCCmec VIII, and SCC-CI in Staphylococcus epidermidis. The array showed that the majority of isolates harbored one or more superantigen (94%; 100/107) and immune evasion cluster (91%; 97/107) genes. Apart from fusidic acid and trimethoprim resistance, the correlation between isolate antimicrobial resistance phenotype and the presence of specific resistance genes was ≥97%. Array profiling allowed high-throughput, accurate assignment of MRSA to CCs/STs and SCCmec types and provided further evidence of the diversity of SCCmec/SCC. In most cases, array profiling can accurately predict the resistance phenotype of an isolate.     

Final-year student nurses' perceptions of role transition

Role transition can be both challenging and exciting. This study presents the findings of phase one of a two-part study conducted by Deasy et al (2011), which explored final-year student nurses' (n=116) perceptions and expectations of role transition. The students were registered on four-year BSc nursing programmes at an Irish university. Data was analyzed using SPSS (version 16). A response rate of 84% was achieved. Over half of respondents said they were adequately prepared for the post of registered nurse. Respondents generally perceived themselves to be competent across a range of domains: managing workloads; prioritizing care delivery; interpersonal skills; time management skills; ethical decision making; and providing health information and education. In contrast, not all were confident about their knowledge and many expected the transition to be problematic. Most expected to be supported and to receive constructive feedback. Recommendations include nurturing supportive work environments to reduce stress and increase confidence.     

Autism in the criminal justice detention system: A review of the literature

Little is known about the experience of people with autism in custody. A review of the literature that explored the relationship between autism and criminality and the criminal justice detention system was conducted. Literature suggests that people with autism are potentially overrepresented within the criminal justice system. There is little research that has examined the experiences of people with autism spectrum disorders in custody. There is very little to guide service design to develop autism support services for people in prison.     

Successes and Challenges of Interprofessional Physiologic Birth and Obstetric Emergency Simulations in a Nurse‐Midwifery Education Program

This article describes childbirth simulation design and implementation within the nurse‐midwifery education program at the University of California, San Francisco. Nurse‐midwife and obstetrician faculty coordinators were supported by faculty from multiple professions and specialties in curriculum review and simulation development and implementation. The primary goal of the resulting technology‐enhanced simulations of normal physiologic birth and obstetric emergencies was to assist learners’ development of interprofessional competencies related to communication, teamwork, and patient‐centered care. Trainees included nurse‐midwifery students; residents in obstetrics, pediatrics, and family medicine; medical students; and advanced practice nursing students in pediatrics. The diversity of participant types and learning levels provided benefits and presented challenges to effective scenario‐based simulation design among numerous other theoretical and logistical considerations. This project revealed practical solutions informed by emerging health sciences and education research literature, faculty experience, and formal course evaluations by learners. Best practices in simulation development and implementation were incorporated, including curriculum revision grounded in needs assessment, case‐ and event‐based clinical scenarios, optimization of fidelity, and ample time for participant debriefing. Adequate preparation and attention to detail increased the immersive experience and benefits of simulation. Suggestions for fidelity enhancement are provided with examples of simulation scenarios, a timeline for preparations, and discussion topics to facilitate meaningful learning by maternity and newborn care providers and trainees in clinical and academic settings. Pre‐ and postsimulation measurements of knowledge, skills, and attitudes are ongoing and not reported. This article is part of a special series of articles that address midwifery innovations in clinical practice, education, interprofessional collaboration, health policy, and global health.     

Flank hernia secondary to phenol nerve block

Background  

The management of patients with chronic pain is challenging. The aim of treatment is alleviation of symptoms in an attempt to increase functional capacity. Interventional procedures, such as chemical neurolysis are adopted when other techniques fail to provide adequate pain control.     

Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods

In FDG-PET imaging of thoracic tumors, blurring due to breathing motion often significantly degrades the quality of the observed image, which then obscures the tumor boundary. We demonstrate a deblurring technique that combines patient-specific motion estimates of tissue trajectories with image deconvolution techniques, thereby partially eliminating breathing-motion induced artifacts. Two data sets were used to evaluate the methodology including mobile phantoms and clinical images. The clinical images consist of PET/CT co-registered images of patients diagnosed with lung cancer. A breathing motion model was used to locally estimate the location-dependent tissue location probability function (TLP) due to breathing. The deconvolution process is carried by an expectation-maximization (EM) iterative algorithm using the motion-based TLP. Several methods were used to improve the robustness of the deblurring process by mitigating noise amplification and compensating for motion estimate uncertainties. The mobile phantom study with controlled settings demonstrated significant reduction in underestimation error of concentration in high activity case without significant superiority between the different applied methods. In case of medium activity concentration (moderate noise levels), less improvement was reported (10%-15% reduction in underestimation error relative to 15%-20% reduction in high concentration). Residual denoising using wavelets offered the best performance for this case. In the clinical data case, the image spatial resolution was significantly improved, especially in the direction of greatest motion (cranio-caudal). The EM algorithm converged within 15 and 5 iterations in the large and small tumor cases, respectively. A compromise between a figure-of-merit and entropy minimization was suggested as a stopping criterion. Regularization techniques such as wavelets and Bayesian methods provided further refinement by suppressing noise amplification. Our initial results show that the proposed method provides a feasible framework for improving PET thoracic images, without the need for gated/4-D PET imaging, when 4-D CT is available to estimate tumor motion.