A sham-controlled trial of acupressure on the quality of sleep and life in haemodialysis patients

Background Sleep disorder in haemodialysis patients can lead to disturbance in their psychosocial function and interpersonal relations, and reduced quality of life. The aim of the present study was to investigate the effect of acupressure on the quality of sleep of haemodialysis patients. Methods In a randomised controlled trial, 108 haemodialysis patients were randomly divided into three groups: true acupressure, placebo acupressure, and no treatment. The two acupressure groups received treatment three times a week for 4 weeks during dialysis. Routine care only was provided for the no treatment group. The main study outcome was sleep quality. Results The total Pittsburgh Sleep Quality Index score decreased significantly from 11.9±3.13 to 6.2±1.93 in the true acupressure group, from 11.3±3.69 to 10.6±3.82 in the sham acupressure group, and from 10.9±4.10 to 10.7±3.94 in the no treatment group. There was a significant difference between groups (p<0.001). Conclusions Acupressure seems to have a positive effect on the sleep quality in haemodialysis patients. Clinical trial registration IRCT201106145864N2

Effect of foot reflexology on fatigue in patients undergoing hemodialysis: A sham-controlled randomized trial

Background and purpose: Fatigue is a common symptom in patients undergoing hemodialysis. Reflexology is a nursing intervention that could reduce fatigue. This study aimed at determining the effects of foot reflexology on fatigue in patients undergoing hemodialysis. Materials and methods: A clinical trial with before and after design was conducted in hemodialysis patients attending Imam-Ali and Iran-mehr clinic in Bojnurd, 2013. Using randomized sampling 78 patients were allocated into three groups: intervention, placebo, and control group. The patients in intervention group received foot reflexology, and simple foot reflexology without pressing certain parts of the foot was done in placebo group. The patients in control group received only routine care. Piper Fatigue Scale was used to measure fatigue level before and after the intervention. Data was analyzed using descriptive statistics, one-way ANOVA and Paired t-test. Results: The results showed a significant difference between fatigue scores in intervention and control groups before and after the intervention (P<0.001). After the foot reflexology, the fatigue score in intervention group reduced to 3.8±1.27 (vs. 4.34±1.35 before the intervention), while the fatigue score in control group increased to 5.19±0.87 (vs. 4.91±1.04 before the intervention) (P<0.05). The placebo group showed no significant difference before and after the intervention (P=0.9). Conclusion: Reflexology can be used as a nursing intervention in reducing fatigue among patients undergoing hemodialysis. © 2016, Mazandaran University of Medical Sciences. All rights reserved

SLiMe: Segment Like Me

Significant strides have been made using large vision-language models, like Stable Diffusion (SD), for a variety of downstream tasks, including image editing, image correspondence, and 3D shape generation. Inspired by these advancements, we explore leveraging these extensive vision-language models for segmenting images at any desired granularity using as few as one annotated sample by proposing SLiMe. SLiMe frames this problem as an optimization task. Specifically, given a single training image and its segmentation mask, we first extract attention maps, including our novel "weighted accumulated self-attention map" from the SD prior. Then, using the extracted attention maps, the text embeddings of Stable Diffusion are optimized such that, each of them, learn about a single segmented region from the training image. These learned embeddings then highlight the segmented region in the attention maps, which in turn can then be used to derive the segmentation map. This enables SLiMe to segment any real-world image during inference with the granularity of the segmented region in the training image, using just one example. Moreover, leveraging additional training data when available, i.e. few-shot, improves the performance of SLiMe. We carried out a knowledge-rich set of experiments examining various design factors and showed that SLiMe outperforms other existing one-shot and few-shot segmentation methods

Deep Semantic Segmentation of Natural and Medical Images: A Review

The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.Comment: 45 pages, 16 figures. Accepted for publication in Springer Artificial Intelligence Revie