Stress Engineering using Si3N4 for stiction free release of SOI beams

We report on the effect of thin silicon nitride (Si3N4) induced tensile stress on the structural release of 200nm thick SOI beam, in the surface micro-machining process. A thin (20nm / 100nm) LPCVD grown Si3N4 is shown to significantly enhance the yield of released beam in wet release technique. This is especially prominent with increase in beam length, where the beams have higher tendency for stiction. We attribute this yield enhancement to the nitride induced tensile stress, as verified by buckling tendency and resonance frequency data obtained from optical profilometry and laser doppler vibrometry

An ophthalmology professionalism survey tool: outcomes from a multi-center study in Central India

Purpose: To describe a professionalism survey tool and its use to assess knowledge of medical professionalism in ophthalmology training programs in Central India. Settings and Design: Multi-center survey study. Methods: A validated 33-question, scenario-based survey addressing professionalism attributes was administered at five centers in central India. The attributes tested included “personal characteristics,” “physician–patient relationships,” “workplace practice and relationships,” and “socially responsible behaviors.” A mean attribute score (%) was calculated and compared to “gold standard” responses by a group of expert senior ophthalmologists (100% agreement for responses). Results: A total of 225 participants completed the survey; 124 residents, 47 fellows, and 54 consultants (98.4% response rate). The total mean attribute score was 80.7 ± 9.1 (min 16.67, max 100). There was variation in the mean attribute score by professionalism attribute (P < 0.001), and a trend toward higher mean attribute scores for consultants compared to trainees across all attribute groups. The scores for “personal characteristics” (93 ± 9.7) and “physician-patient relationship” (82 ± 15.8) were the highest, whereas scores for “socially responsible behaviors” (73.9 ± 18.6) and “workplace practices” were low (72 ± 13). Conclusions: There is a generally high level of professionalism knowledge among ophthalmologists in central India. The results suggest that experience does impact knowledge of professionalism. Potential for improvement in professionalism exists in around “workplace practices”, and around “socially responsible behaviors”. These findings may serve as a valuable discussion starter and teaching tool to enhance professionalism in ophthalmology training programs

The role of centralized reading of endoscopy in a randomized controlled trial of mesalamine for ulcerative colitis

Background & Aims: Interobserver differences in endoscopic assessments contribute to variations in rates of response to placebo in ulcerative colitis (UC) trials. We investigated whether centralized review of images could reduce these variations. Methods: We performed a 10-week, randomized, double-blind, placebo-controlled study of 281 patients with mildly to moderately active UC, defined by an Ulcerative Colitis Disease Activity Index (UCDAI) sigmoidoscopy score ≥2, that evaluated the efficacy of delayed-release mesalamine (Asacol 800-mg tablet) 4.8 g/day. Endoscopic images were reviewed by a single expert central reader. The primary outcome was clinical remission (UCDAI, stool frequency and bleeding scores of 0, and no fecal urgency) at week 6. Results: The primary outcome was achieved by 30.0% of patients treated with mesalamine and 20.6% of those given placebo, a difference of 9.4% (95% confidence interval [CI], -0.7% to 19.4%; P =.069). Significant differences in results from secondary analyses indicated the efficacy of mesalamine. Thirty-one percent of participants, all of whom had a UCDAI sigmoidoscopy score ≥2 as read by the site investigator, were considered ineligible by the central reader. After exclusion of these patients, the remission rates were 29.0% and 13.8% in the mesalamine and placebo groups, respectively (difference of 15%; 95% CI, 3.5%-26.0%; P =.011). Conclusions: Although mesalamine 4.8 g/day was not statistically different from placebo for induction of remission in patients with mildly to moderately active UC, based on an intent-to-treat analysis, the totality of the data supports a benefit of treatment. Central review of endoscopic images is critical to the conduct of induction studies in UC; ClinicalTrials.gov Number, NCT01059344. © 2013 by the AGA Institute

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset