Marshalljoensen2957

INTRODUCTION Bacterial biofilm in wounds prevents healing by acting as a physical barrier to wound closure and hyperactivating local inflammatory processes, thus making its removal a high priority. The authors previously have shown that adding topical oxygen to standard wound care increased healing of Texas Grade II and III diabetic foot ulcers (DFUs), which they hypothesized was a result of alterations of the wound microbiome/biofilm. OBJECTIVE This study aims to determine the mechanism of action of topical oxygen in DFUs by examining the diversity of bacterial genera present in DFUs treated with topical oxygen. MATERIALS AND METHODS Six patients with chronic DFUs had their wounds swabbed weekly over an 8-week period of continuous topical oxygen treatment, and microbiome diversity was assessed by metagenomic 16S rDNA sequencing using a next-generation sequencing platform. check details RESULTS The wound microbiome shifted toward a diverse flora dominated by aerobes and facultative anaerobes with oxygen therapy in 5 healed wounds. In contrast, anaerobic flora persisted in a single nonhealing ulcer in the present study cohort. CONCLUSIONS Although the sample size was small, this study suggests topical oxygen therapy may have the ability to encourage the growth of aerobic members of the wound microbiome and be an effective alternative to antibiotics in this area.The lymphatic system is arguably the most neglected bodily system. As a result, its contribution to human health and disease is not well understood. In this review, the clinical approaches based on new knowledge and developments of the lymphatic system are covered. The lymphatic system has 3 major functions (1) the preservation of fluid balance; (2) a nutritional function, as intestinal lymphatics are responsible for fat absorption; and (3) host defense. Lymph vessels return the capillary ultrafiltrate and escaped plasma proteins from most tissues back, ultimately, to the blood circulation. Hence, lymphatics are responsible for maintaining tissue (and plasma) volume homeostasis. Impaired lymph drainage results in peripheral edema (lymphedema) and may have more far-reaching effects on cardiovascular disease, in particular hypertension and atherosclerosis. Lymphatics have an important immune surveillance function, as they represent the principal route of transport from tissues for antigen and immune cells. Intestinal lymphatics (lacteals) are responsible for most fat absorption, first documented by Gaspare Aselli in 1627, when the lymphatic system was discovered. A relationship between fat and lymphatics may exist well beyond the gut alone. Fat deposition is a defining clinical characteristic of lymphedema. Suction-assisted lipectomy of lymphedema has shown the swelling is not just fluid but is dominated by fat. Lymphatics are the preferred route for the metastatic spread of cancer. Accordingly, the lymphatic system may be important for defense against cancer by generating immune responses to malignant cell antigens. Preventing lymphatic entry and propagation of malignant metastasis would effectively render the cancer nonfatal. As one can see, the lymphatic circulation is fundamentally important to cardiovascular disease, infection and immunity, cancer, and, in all likelihood, obesity - 4 of the major challenges to health care in the 21st century.BACKGROUND The incidence of cardiac arrests per year in the United States continues to increase, yet in-hospital cardiac arrest survival rates significantly vary between hospitals. Current methods of training are expensive, time consuming, and difficult to scale, which necessitates improvements in advanced cardiac life support (ACLS) training. Virtual reality (VR) has been proposed as an alternative or adjunct to high-fidelity simulation (HFS) in several environments. No evaluations to date have explored the ability of a VR program to examine both technical and behavioral skills and demonstrate a cost comparison. OBJECTIVE This study aimed to explore the utility of a voice-based VR ACLS team leader refresher as compared with HFS. METHODS This prospective observational study performed at an academic institution consisted of 25 postgraduate year 2 residents. Participants were randomized to HFS or VR training and then crossed groups after a 2-week washout. Participants were graded on technical and nontechnical sIONS Utilization of a VR-based team leader refresher for ACLS skills is comparable with HFS in several areas, including learner satisfaction. The VR module was more cost-effective and was easier to proctor; however, HFS was better at delivering feedback to participants. Optimal education strategies likely contain elements of both modalities. Further studies are needed to examine the utility of VR-based environments at scale. ©Daniel Katz, Ronak Shah, Elizabeth Kim, Chang Park, Anjan Shah, Adam Levine, Garrett Burnett. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 12.03.2020.BACKGROUND Electrocardiographic (ECG) monitors have been widely used for diagnosing cardiac arrhythmias for decades. However, accurate analysis of ECG signals is difficult and time-consuming work because large amounts of beats need to be inspected. In order to enhance ECG beat classification, machine learning and deep learning methods have been studied. However, existing studies have limitations in model rigidity, model complexity, and inference speed. OBJECTIVE To classify ECG beats effectively and efficiently, we propose a baseline model with recurrent neural networks (RNNs). Furthermore, we also propose a lightweight model with fused RNN for speeding up the prediction time on central processing units (CPUs). METHODS We used 48 ECGs from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database, and 76 ECGs were collected with S-Patch devices developed by Samsung SDS. We developed both baseline and lightweight models on the MXNet framework. We trained both models on graphics processing units and measured both models' inference times on CPUs. RESULTS Our models achieved overall beat classification accuracies of 99.72% for the baseline model with RNN and 99.80% for the lightweight model with fused RNN. Moreover, our lightweight model reduced the inference time on CPUs without any loss of accuracy. The inference time for the lightweight model for 24-hour ECGs was 3 minutes, which is 5 times faster than the baseline model. CONCLUSIONS Both our baseline and lightweight models achieved cardiologist-level accuracies. Furthermore, our lightweight model is competitive on CPU-based wearable hardware. ©Eunjoo Jeon, Kyusam Oh, Soonhwan Kwon, HyeongGwan Son, Yongkeun Yun, Eun-Soo Jung, Min Soo Kim. Originally published in JMIR Medical Informatics (http//medinform.jmir.org), 12.03.2020.