Macgregormark4202

PURPOSE OF REVIEW Telehealth is the delivery of health care using the most recent technological advances. With the growing complexity of inflammatory bowel disease (IBD) care, telehealth allows for delivery of specialty services to an extended population. We reviewed the most recent literature on telehealth modalities, including patient-reported and disease outcomes associated with use of telehealth. RECENT FINDINGS Current methods of telemedicine include telehealth, remote patient monitoring, and the use of mobile applications. Remote patient monitoring via web applications has been studied with improvement in patient-reported quality of life, medication adherence, and decreased heath care costs. Mobile applications can be used for symptom reporting and alert the medical team if a patient is reporting increased symptoms. These web and mobile applications allow for treatment decisions to occur without the delay of an office visit. There remain limitations to telehealth including technological concerns, physician acceptance, and licensing and reimbursement inequities. Telemedicine is a safe, effective, and accepted method of meeting the growing demand for complex IBD care throughout the world. The use of telehealth video conference and remote patient monitoring with web-based applications and text messaging has been shown to ease financial burdens of chronic disease, improve patient quality of life, and lead to improved clinical outcomes.A rise in antimicrobial resistance, seen especially since 2000, is in part caused by indiscriminate antimicrobial use. Varied types of persuasive interventions aimed to optimize antimicrobial use have been tried with varying success. Our review seeks to identify and assess factors associated with the successful implementation of persuasive interventions. We searched five databases (MEDLINE, EMBASE, The Cochrane Library, PsycINFO, and ERIC) to identify critical studies published between 2000 and December 2018 of interventions employing audit and feedback, education through meetings, academic detailing, reminders, and patient, family, or public education. Outcome measures of interest were any means to measure antimicrobial use. We included 26 articles in our analysis. Seventeen examined multimodal interventions and the most common was audit and feedback and meeting (four studies). Nine examined single interventions and the most common was audit and feedback (five studies). Our findings inform four evidence-based strategies to enable healthcare administrators, clinicians, and researchers to make informed choices when planning and designing an antimicrobial stewardship program (1) implement a combination of persuasive interventions from both groups audit and feedback, academic detailing, or patient, family, or provider education; and meeting or reminders, (2) design interventions that last one year or longer; post-intervention, assess the intervention's long-term effects for at least another one year, (3) conduct quality improvement projects examining persuasive interventions if the prescribing database provides adequate diagnosis information, and most importantly, (4) make patient, family, or provider education an integral component of multimodal intervention.Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a lung cancer patient at 4 severity stages. We surveyed 1075 patients. The presented model is constructed by using the demographic, diagnosed-based, and prior-utilization variables. The proposed model for the survivability prognosis at different four stages performed R2 of 93.57%, 86.83%, 67.22%, and 52.94%, respectively. The model predicted the lung cancer survivability with high accuracy compared with the reported models. Our model also shows that it reached the ceiling of an ideal Bayesian network.Interest for deep learning in radiology has increased tremendously in the past decade due to the high achievable performance for various computer vision tasks such as detection, segmentation, classification, monitoring, and prediction. This article provides step-by-step practical guidance for conducting a project that involves deep learning in radiology, from defining specifications, to deployment and scaling. Specifically, the objectives of this article are to provide an overview of clinical use cases of deep learning, describe the composition of multi-disciplinary team, and summarize current approaches to patient, data, model, and hardware selection. Key ideas will be illustrated by examples from a prototypical project on imaging of colorectal liver metastasis. RG-7112 cell line This article illustrates the workflow for liver lesion detection, segmentation, classification, monitoring, and prediction of tumor recurrence and patient survival. Challenges are discussed, including ethical considerations, cohorting, data collection, anonymization, and availability of expert annotations. The practical guidance may be adapted to any project that requires automated medical image analysis.The effect of unintentionally doped hydrogen on the properties of Mg-doped p-GaN samples grown via metal-organic chemical vapor deposition (MOCVD) is investigated through room temperature photoluminescence (PL) and Hall and secondary ion mass spectroscopy (SIMS) measurements. It is found that there is an interaction between the residual hydrogen and carbon impurities. An increase of the carbon doping concentration can increase resistivity of the p-GaN and weaken blue luminescence (BL) band intensity. However, when hydrogen incorporation increased with carbon doping concentration, the increase of resistivity caused by carbon impurity is weaken and the BL band intensity is enhanced. This suggests that the co-doped hydrogen not only passivate MgGa, but also can passivate carbon impurities in Mg-doped p-GaN.Gyriform restricted diffusion (GRD) refers to hyperintense signal involving the cerebral cortex on diffusion-weighted images (DWI) with corresponding hypointensity on apparent diffusion coefficient (ADC) images. These changes are commonly seen following a vascular occlusion, reflecting the limitation of water molecule movement across cell membranes (restricted diffusion) due to the failure of Na+/K+-ATPase pumps (cytotoxic oedema). However, GRD can occur in several other neurological conditions as well. A thorough understanding of these conditions and their anatomic predilection plays a critical role in identifying and differentiating them from vascular thrombo-occlusion, with impact towards appropriate clinical management. This review highlights the less commonly encountered, non-stroke causes of GRD in adults with case-based examples. A tabulated chart of the patterns of cortical and subcortical involvement associated with these aetiologies is provided for a quick, pattern-based reference for daily radiological reporting.