1,190 research outputs found

    Wound and Stoma Care Education for Primary Care Providers

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    Abstract Problem. Patients with percutaneous endoscopic gastrostomy (PEG) tubes and their families report physical and psychosocial changes associated with complications after gastrointestinal surgeries. They are at particular risk for tube dislodgement, leakage, infection, poor fit, and skin irritation. The lack of specialization and fundamental stoma care knowledge among community health care providers potentially results in unintended consequences, leaving the system inefficient with fundamental inequities of stoma care delivery. While there is an increasing amount of accessibility to specialty centers for other types of wound care, stoma care support resources are scarce and not readily available to patients. Context: This Doctor of Nursing Practice project focused on conducting a stoma care education workshop to primary care providers in outpatient clinics within the community. There is a need to enhance the quality of care for patients and families caring for stomas at home and improve barriers in access to specialized care, especially in rural communities. Utilization of trained primary care providers has the potential to reduce the costs associated with emergency room visits and hospital admissions. Intervention. Emphasis of this project was placed on providing comprehensive education for wound and stoma care focused on gastrostomy tubes to providers at the California Association of Nurse Practitioners for the 43rd Annual Educational Conference. However, due to the current covid-19 pandemic the conference was cancelled and the project was implemented with a small group of nurse practitioners at Lucile Packard Children’s Hospital. Outcome Measures. Outcome measures for evaluation of this project consisted of data collected from pre/post training assessment surveys to evaluate change in provider knowledge and confidence in implementing evidence-based wound and stoma care. Results. Data analysis revealed an increase in overall provider knowledge with providing wound and stoma care by 25%. Of the participants, 66.67% (4 of 6) considered themselves as experts in gastrostomy tube care, while two participants indicated a proficient level of knowledge on the self-assessment questionnaire prior to the workshop. Significant changes were seen in provider recognition of common mechanical feeding tube complications and methods for treating peristomal hyper granulation tissue with a 33% increase, followed by a 25% increase in identifying risk factors for complications, and 22% improvement pertaining to treatment and causes of minor and major wound complications. Limitations. Due to competing provider priorities focused on the novel covid-19 pandemic and social distancing considerations, only a small volume of providers were available to participate in the education workshop. Participants indicated that they were either an expert or proficient in gastrostomy tube care, therefore leading to bias. Conclusions. Despite provider knowledge level pertaining to wound and stoma care, continuing education is an effective approach to enhance clinical skills and knowledge. Written educational materials are helpful tools for both providers and patients, especially in rural areas where access to specialized providers are limited. Keywords: PEG, feeding tubes, complications, hospital readmission, emergency room visits, wounds, and stoma

    Early aspects: aspect-oriented requirements engineering and architecture design

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    This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

    Medical Image Segmentation with Deep Convolutional Neural Networks

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    Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

    Order out of Randomness : Self-Organization Processes in Astrophysics

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    Self-organization is a property of dissipative nonlinear processes that are governed by an internal driver and a positive feedback mechanism, which creates regular geometric and/or temporal patterns and decreases the entropy, in contrast to random processes. Here we investigate for the first time a comprehensive number of 16 self-organization processes that operate in planetary physics, solar physics, stellar physics, galactic physics, and cosmology. Self-organizing systems create spontaneous {\sl order out of chaos}, during the evolution from an initially disordered system to an ordered stationary system, via quasi-periodic limit-cycle dynamics, harmonic mechanical resonances, or gyromagnetic resonances. The internal driver can be gravity, rotation, thermal pressure, or acceleration of nonthermal particles, while the positive feedback mechanism is often an instability, such as the magneto-rotational instability, the Rayleigh-B\'enard convection instability, turbulence, vortex attraction, magnetic reconnection, plasma condensation, or loss-cone instability. Physical models of astrophysical self-organization processes involve hydrodynamic, MHD, and N-body formulations of Lotka-Volterra equation systems.Comment: 61 pages, 38 Figure

    Evolving Clustering Algorithms And Their Application For Condition Monitoring, Diagnostics, & Prognostics

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    Applications of Condition-Based Maintenance (CBM) technology requires effective yet generic data driven methods capable of carrying out diagnostics and prognostics tasks without detailed domain knowledge and human intervention. Improved system availability, operational safety, and enhanced logistics and supply chain performance could be achieved, with the widespread deployment of CBM, at a lower cost level. This dissertation focuses on the development of a Mutual Information based Recursive Gustafson-Kessel-Like (MIRGKL) clustering algorithm which operates recursively to identify underlying model structure and parameters from stream type data. Inspired by the Evolving Gustafson-Kessel-like Clustering (eGKL) algorithm, we applied the notion of mutual information to the well-known Mahalanobis distance as the governing similarity measure throughout. This is also a special case of the Kullback-Leibler (KL) Divergence where between-cluster shape information (governed by the determinant and trace of the covariance matrix) is omitted and is only applicable in the case of normally distributed data. In the cluster assignment and consolidation process, we proposed the use of the Chi-square statistic with the provision of having different probability thresholds. Due to the symmetry and boundedness property brought in by the mutual information formulation, we have shown with real-world data that the algorithm’s performance becomes less sensitive to the same range of probability thresholds which makes system tuning a simpler task in practice. As a result, improvement demonstrated by the proposed algorithm has implications in improving generic data driven methods for diagnostics, prognostics, generic function approximations and knowledge extractions for stream type of data. The work in this dissertation demonstrates MIRGKL’s effectiveness in clustering and knowledge representation and shows promising results in diagnostics and prognostics applications

    The distribution of Quiet Sun magnetic field strengths from 0 to 1800 G

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    The quiet Sun photospheric plasma has a variety of magnetic field strengths going from zero to 1800 G. The empirical characterization of these field strengths requires a probability density function (PDF), i.e., a function P(B) describing the fraction of quiet Sun occupied by each field strength B. We show how to combine magnetic field strength measurements based on the Zeeman effect and the Hanle effect to estimate an unbiased P(B). The application of the method to real observations renders a set of possible PDFs, which outline the general characteristics of the quiet Sun magnetic fields. Their most probable field strength differs from zero. The magnetic energy density is a significant fraction of the kinetic energy of the granular motions at the base of the photosphere (larger than 15% or larger than 2 10^{3} erg cm^{-3}). The unsigned flux density (or mean magnetic field strength) has to be between 130 G and 190 G. A significant part of the unsigned flux (between 10% and 50%) and of the magnetic energy (between 45% and 85%) are provided by the field strengths larger than 500 G which, however, occupy only a small fraction of the surface (between 1% and 10%). The fraction of kG fields in the quiet Sun is even smaller, but they are important for a number of reasons. The kG fields still trace a significant fraction of the total magnetic energy, they reach the high photosphere, and they appear in unpolarized light images. The quiet Sun photosphere has far more unsigned magnetic flux and magnetic energy than the active regions and the network all together.Comment: To appear in ApJ. 14 pages, 9 figure

    Clinical Relevance of Dissolution Testing in Quality by Design

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    Quality by design (QbD) has recently been introduced in pharmaceutical product development in a regulatory context and the process of implementing such concepts in the drug approval process is presently on-going. This has the potential to allow for a more flexible regulatory approach based on understanding and optimisation of how design of a product and its manufacturing process may affect product quality. Thus, adding restrictions to manufacturing beyond what can be motivated by clinical quality brings no benefits but only additional costs. This leads to a challenge for biopharmaceutical scientists to link clinical product performance to critical manufacturing attributes. In vitro dissolution testing is clearly a key tool for this purpose and the present bioequivalence guidelines and biopharmaceutical classification system (BCS) provides a platform for regulatory applications of in vitro dissolution as a marker for consistency in clinical outcomes. However, the application of these concepts might need to be further developed in the context of QbD to take advantage of the higher level of understanding that is implied and displayed in regulatory documentation utilising QbD concepts. Aspects that should be considered include identification of rate limiting steps in the absorption process that can be linked to pharmacokinetic variables and used for prediction of bioavailability variables, in vivo relevance of in vitro dissolution test conditions and performance/interpretation of specific bioavailability studies on critical formulation/process variables. This article will give some examples and suggestions how clinical relevance of dissolution testing can be achieved in the context of QbD derived from a specific case study for a BCS II compound
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