Factors promoting health-related quality of life in people with rheumatic diseases: a 12 month longitudinal study

<p>Abstract</p> <p>Background</p> <p>Rheumatic diseases have a significant adverse impact on the individual from physical, mental and social aspects, resulting in a low health-related quality of life (HRQL). There is a lack of longitudinal studies on HRQL in people with rheumatic diseases that focus on factors promoting HRQL instead of risk factors. The aim of this study was to investigate the associations between suggested health promoting factors at baseline and outcome in HRQL at a 12 month follow-up in people with rheumatic diseases.</p> <p>Methods</p> <p>A longitudinal cohort study was conducted in 185 individuals with rheumatic diseases with questionnaires one week and 12 months after rehabilitation in a Swedish rheumatology clinic. HRQL was assessed by SF-36 together with suggested health factors. The associations between SF-36 subscales and the health factors were analysed by multivariable logistic regressions.</p> <p>Results</p> <p>Factors predicting better outcome in HRQL in one or several SF-36 subscales were being younger or middle-aged, feeling painless, having good sleep structure, feeling rested after sleep, performing low effort of exercise more than twice per week, having strong sense of coherence (SOC), emotional support and practical assistance, higher educational level and work capacity. The most important factors were having strong SOC, feeling rested after sleep, having work capacity, being younger or middle-aged, and having good sleep structure.</p> <p>Conclusions</p> <p>This study identified several factors that promoted a good outcome in HRQL to people with rheumatic diseases. These health factors could be important to address in clinical work with rheumatic diseases in order to optimise treatment strategies.</p

Concomitant Cisplatin Plus Radiotherapy and High-Dose-Rate Brachytherapy Versus Radiotherapy Alone for Stage IIIB Epidermoid Cervical Cancer: A Randomized Controlled Trial

Purpose The benefits of chemoradiotherapy (CRT) for cervical cancer compared with radiation (RT) alone seem to diminish in later-stage disease. However, these modalities have not been directly compared for disease-free interval (DFI) and overall survival (OS) of women with stage IIIB cervical cancer. Patients and Methods We conducted a randomized controlled clinical trial comparing DFI and OS of 147 women with stage IIIB squamous cervical cancer who received either cisplatin plus RT (CRT) or RT alone (72 patients in the CRT group and 75 patients in the RT-only group). Results The CRT group had significantly better DFI (hazard ratio [HR], 0.52; 95% CI, 0.29 to 0.93; P = .02). However, patients in the CRT group did not have significantly better OS than those in the RT-only group (HR, 0.67; 95% CI, 0.38 to 1.17; P = .16). Toxicity was graded according to criteria of the Radiation Therapy Oncology Group. The organs affected (excluding hematologic effects) did not differ significantly between groups. Also, late toxicity events and organs affected were not significantly disproportionate between the study groups. Conclusion For stage IIIB cervical cancer, the addition of cisplatin offers a small but significant benefit in DFI, with acceptable toxicity. (C) 2014 by American Society of Clinical Oncology32654254

Enhanced Avatar Control Using Neural Networks

NoThis paper presents realistic avatar movements using a limited number of sensors. An inverse kinematics algorithm, SHAKF, is used to configure an articulated skeletal model, and a neural network is employed to predict the movement of joints not bearing sensors. The results show that the neural network is able to give a very close approximation to the actual rotation of the joints. This allows a substantial reduction in the number of sensors to configure an articulated human skeletal model

Deep learning with convolutional neural networks for histopathology image analysis

In the recent years, deep learning based methods and, in particular, convolutional neural networks, have been dominating the arena of medical image analysis. This has been made possible both with the advent of new parallel hardware and the development of efficient algorithms. It is expected that future advances in both of these directions will increase this domination. The application of deep learning methods to medical image analysis has been shown to significantly improve the accuracy and efficiency of the diagnoses. In this chapter, we focus on applications of deep learning in microscopy image analysis and digital pathology, in particular. We provide an overview of the state-of-the-art methods in this area and exemplify some of the main techniques. Finally, we discuss some open challenges and avenues for future work