Background and objectives: Novel individualized quality-of-life (IQOL) measures permit patients with chronic kidney disease (CKD) to nominate unique areas of their lives that contribute to their well-being. This study assessed for differences in domains nominated by patients with CKD. We also examined the strength of association between (1) multidimensional health-related quality-of-life measures and IQOL and (2) Results: Patients with CKD nominated many domains on the SEIQOL-DW, but family and health were the most common for all groups. Kidney disease was listed more frequently by peritoneal dialysis compared with hemodialysis patients or patients with CKD (31 versus 14 versus 5%, respectively). There were no significant differences in SEIQOL-DW scores between subgroups. SEIQOL-DW scores correlated with mental well-being and inversely correlated with chronic stress and depression.Conclusions: Patients with advanced CKD demonstrate compromised quality-of-life scores comparable to dialysis patients. IQOL measures provide unique information that may help guide interventions that are better tailored to address patients' concerns about their well-being. These findings also suggest that renal clinics should have staff available to address psychosocial aspects of patient well-being.

Background:Very few hospital-based or population-based studies are published in the context to the epidemiologic profile of pediatric brain tumors (PBTs) in India and Indian subcontinent.Aim:To study the demographic and histopathologic profile of PBTs according to World Health Organization 2007 classification in a single tertiary health care center in India.Materials and Methods:Data regarding age, gender, topography, and histopathology of 76 pediatric patients (0–19 years) with brain tumors operated over a period of 24 months (January-2012 to December-2013) was collected retrospectively and analyzed using EpiInfo 7. Chi-square test and test of proportions (Z-test) were used wherever necessary.Results:PBTs were more common in males (55.3%) as compared to females (44.7%) with male to female ratio of 1.23:1. Mean age was 10.69 years. Frequency of tumors was higher in childhood age group (65.8%) when compared to adolescent age group (34.2%). The most common anatomical site was cerebellum (39.5%), followed by hemispheres (22.4%). Supratentorial tumors (52.6%) were predominant than infratentorial tumors (47.4%). Astrocytomas (40.8%) and embryonal tumors (29.0%) were the most common histological types almost contributing more than 2/3rd of all tumors. Craniopharyngiomas (11.8%) and ependymomas (6.6%) were the third and fourth most common tumors, respectively.Conclusion:Astrocytomas and medulloblastomas are the most common tumors among children and adolescents in our region, which needs special attention from the neurosurgical department of our institute. Demographic and histopathologic profile of cohort in the present study do not differ substantially from that found in other hospital-based and population-based studies except for slight higher frequency of craniopharyngiomas.

The novel BioMime SES demonstrated excellent performance in single coronary lesions including high procedural success and efficacy, as demonstrated by the relatively low late lumen loss (a surrogate of neointimal hyperplasia) at eight-month angiographic FU. Overall, there were no safety concerns in this preliminary evaluation including absence of MACE or ST up to 12 months.

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Background and Objective: the aim of this study is to develop and validate an automated image segmentation-based frame selection and stitching framework to create enhanced composite images from otoscope videos. The proposed framework, called SelectStitch, is useful for classifying eardrum abnormalities using a single composite image instead of the entire raw otoscope video dataset. Methods: SelectStitch consists of a convolutional neural network (CNN) based semantic segmentation approach to detect the eardrum in each frame of the otoscope video, and a stitching engine to generate a high-quality composite image from the detected eardrum regions. In this study, we utilize two separate datasets: the first one has 36 otoscope videos that were used to train a semantic segmentation model, and the second one, containing 100 videos, which was used to test the proposed method. Cases from both adult and pediatric patients were used in this study. A configuration of 4-levels depth U-Net architecture was trained to automatically find eardrum regions in each otoscope video frame from the first dataset. After the segmentation, we automatically selected meaningful frames from otoscope videos by using a pre-defined threshold, i.e., it should contain at least an eardrum region of 20% of a frame size. We have generated 100 composite images from the test dataset. Three ear, nose, and throat (ENT) specialists (ENT-I, ENT-II, ENT-III) compared in two rounds the composite images produced by SelectStitch against the composite images that were generated by the base processes, i.e., stitching all the frames from the same video data, in terms of their diagnostic capabilities. Results: In the first round of the study, ENT-I, ENT-II, ENT-III graded improvement for 58, 57, and 71 composite images out of 100, respectively, for SelectStitch over the base composite, reflecting greater diagnostic capabilities. In the repeat assessment, these numbers were 56, 56, and 64, respectively. We observed that only 6%, 3%, and 3% of the cases received a lesser score than the base composite images, respectively, for ENT-I, ENT-II, and ENT-III in Round-1, and 4%, 0%, and 2% of the cases in Round-2. Conclusions: We conclude that the frame selection and stitching will increase the probability of detecting a lesion even if it appears in a few frames.