Parental Participation in a Sex Education Program

This paper reports selected findings of an evaluative study of parental participation in a sex education program. One of three hypotheses is confirmed, and the direction of findings suggests the need for further development and testing of hypotheses in this area

The concept of negative pressure wound therapy (NPWT) after poststernotomy mediastinitis – a single center experience with 54 patients

Deep sternal infections, also known as poststernotomy mediastinitis, are a rare but often fatal complication in cardiac surgery. They are a cause of increased morbidity and mortality and have a significant socioeconomic aspect concerning the health system. Negative pressure wound therapy (NPWT) followed by muscular pectoralis plasty is a quite new technique for the treatment of mediastinitis after sternotomy. Although it could be demonstrated that this technique is at least as safe and reliable as other techniques for the therapy of deep sternal infections, complications are not absent. We report about our experiences and complications using this therapy in a set of 54 patients out of 3668 patients undergoing cardiac surgery in our institution between January 2005 and April 2007

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Efficient deep neural network model for classification of grasp types using sEMG signals

Grasping is a challenging problem in robotics and prosthetic applications due to its control requirements. The visual perception and analyzing electromyography (EMG) signals are the two ways to give the inputs to robots and prosthetic amputees for grasping abilities. The EMG is a diagnostic manner that evaluates the fitness condition of skeletal muscles. Examination or evaluation of the EMG signals is time-consuming and arduous for experts. Hence, the state-of-the-art methods in artificial intelligence (AI) is employed to improve the accuracy rate for the detection and classification of EMG signals for grasping. Recently, deep learning architectures have been used in many engineering applications such as diagnosis of health conditions, computer vision, and human machine interaction (HMI). In this study, a new deep one-dimensional convolutional neural network model (1D-CNN) is proposed to classify six types of hand movements. Our proposed 1D-CNN model implemented using surface EMG (sEMG) has obtained the highest accuracy of 94.94% in classifying six hand movements. The strength of our model is that, it can perform the automated classification of various hard grasps using only one channel data. Our developed prototype model is ready to be tested with more data and can be used to assist in musculoskeletal disorders