The Clinical Misdiagnosis of Lichen Planus and its Potential for Untoward Outcome

The diagnosis of lichen planus may be incorrectly applied to a solitary white lesion and to lesions with ulceration, referred pain and lack of response to corticosteroid therapy. In two patients, the diagnosis of lichen planus lead to the delayed recognition of squamous cell carcinoma requiring extensive surgery.https://digitalcommons.unmc.edu/cod_pres/1002/thumbnail.jp

Adaptive video segmentation

The efficiency of a video indexing technique depends on the efficiency of the video segmentation algorithm which is a fundamental step in video indexing. Video segmentation is a process of splitting up a video sequence into its constituent scenes. This work focuses on the problem of video segmentation. A content-based approach has been used which segments a video based on the information extracted from the video itself. The main emphasis is on using structural information in the video such as edges as they are largely invariant to illumination and motion changes. The edge-based features have been used in conjunction with the intensity-based features in a multi-resolution framework to improve the performance of the segmentation algorithm.;To further improve the performance and to reduce the problem of automated choice of parameters, we introduce adaptation in the video segmentation process. (Abstract shortened by UMI.)

USP6 Translocation in Giant Cell Granulomas of the Jaws

Central giant cell granulomas (CGCG) account for 7% of all benign tumors of the jaw while peripheral giant cell granulomas (PGCG) occur on the gingiva (Table 1). The underlying pathophysiology of CGCG and PGCG is not known. Therefore there are studies attempting to identify biomarkers to increase understanding the pathogenesis of CGCG and PGCG. Some authors consider CGCG in jaw bones similar to giant cell tumors of long bones while others believe them to be reactive or non-neoplastic lesions. Recurrence of these lesions following conservative treatment is attributed to matrix metalloproteinases, namely MMP9. Recent studies have shown an increase in levels of MMP9 in central and peripheral giant cell granulomas as in aneurysmal bone cysts (ABC). De-ubiquitinating enzymes play an important role in cellular processes, though their precise role in normal physiology is not fully understood. USP6 is the first de-ubiquitinating enzyme recognized as an oncogene. Recently studies have described the USP6 translocation in CGCG as transforming this lesion to a neoplasm. This retrospective study analyzed two cases of CGCG and one PGCG for the USP6 translocation.https://digitalcommons.unmc.edu/cod_pres/1001/thumbnail.jp

Bacterial epidemiology of post-operative infections at a cardiac center

Background: Cardiac patients belong to a different group in microbiological perspective as infection causing organisms are different from infection causing pathogens in other patients. Postoperative infections in this group are mainly sternal wound infections and blood stream infections. The present study involved patients from a cardiac center in eastern province, Saudi Arabia, who underwent surgery during a 2 years period.Methods: All positive culture results from cardiac center were collected during this period and data noted. Data included patient demographics, type of surgery/procedure, infection site, infecting organism and the same was analyzed.Results: A total of 4891 patients underwent surgery/procedure at this center during the study period. Overall infection rate was 2.7%. Coagulase negative staphylococci were the predominant pathogens both in sternal wound and blood stream infections.Conclusions: This study provided a data base for this cardiac center to formulate antibiotic prophylaxis and treatment guidelines. In microbiology laboratory, criteria for considering these organisms of low virulence as pathogenic were included in the standard operating procedures

Solution of Heat Transfer and Fluid Flow problems using meshless Radial Basis Function method

In the past, the world of numerical solutions for Heat Transfer and Fluid Flow problems has been dominated by Finite Element Method, Finite Difference Method, Finite Volume Method, and more recently the Boundary Element Method. These methods revolve around using a mesh or grid to solve problems. However, problems with irregular boundaries and domains can be difficult to properly discretize; In this thesis, heat transfer and fluid flow problems are solved using Radial Basis Functions. This method is meshless, easy to understand, and even easier to implement. Radial Basis Functions are used to solve lid-driven cavity flow, natural convection in a square enclosure, flow with forced convection over backward facing step and flow over an airfoil. Codes are developed using MATLAB. The results are compared with COMSOL and FLUENT, two popular commercial codes widely used. COMSOL is a finite element model while FLUENT is a finite volume-based code

Meso-Scale Analysis of Deformation Induced Heating in Granular Metalized Explosives by Piston Supported Waves

Shock sensitivity of heterogeneous explosive composites is dependent on the formation of hot-spots which are small regions of elevated temperatures within the material. Changes in the initial meso-structure (i.e. packing density, composition, particle size and shapes) of the explosives can significantly alter the hot-spot fields in the material and thereby affect its shock sensitivity. In this study, an explicit, 2D, Lagrangian finite and discrete element technique is used to numerically simulate the deformation induced heating of granular mixtures of explosive (HMX), and metal (Al) particles due to piston supported uniaxial deformation waves (400 ≤ Up ≤ 800 m/s). A number of simulations are performed by systematically varying the effective initial packing densities φs, metal mass fractions λm, and particle size distributions. Emphasis is placed on charactering how the inclusion of metal (Al) affects both the effective wave end states (Hugoniots) and the hot-spot fields within the explosive (HMX) component relative to neat HMX. Variations in hot-spot volumetric quantities such as number density and volume fraction are characterized since these quantities can be used in the ignition and growth models to describe macro-scale material sensitivity. Predictions indicate that porosity has a leading order effect on the shock sensitivity of the material due to enhanced dissipation resulting from plastic pore collapse. For a fixed porosity and piston speed, inclusion of metal is found to enhance the effective plasticity in the material due to higher pressures. This leads to larger hot-spots within the metalized formulations. However, due to the high thermal conductivity of the metal, frictional induced hot-spots are suppressed within the material since most of the frictional dissipation at the Al-HMX interfaces is absorbed by the metal. Additionally, hot-spot formation is found to have a highly non-linear dependence on Al particle size with a substantial decrease in hot-spot number density and volume fraction predicted with increasing metal particle size. Meso-structural stochasticity arising due to random seeding of particles, and/or large particle clustering were found to affect the hot-spot statistics minimally

Predictive Analysis of Covid-19 Disease Severity in X-ray images: using Deep Learning Techniques

Healthcare systems are evolving in order to deal with the issues of death in human. The most current worldwide pandemic, COVID-19, which first appeared in 2019, has spread throughout the world. Covid sickness is currently one of the leading causes of death in humans. The signs of COVID-19 include fever, coughing, exhaustion, body pains, and shortness of breath. These symptoms can range in severity from moderate to severe. Also possible for some people are sore throats, congestion, runny noses, and loss of taste or smell. The COVID-19 pandemic has prompted researchers to create imaging-based medical treatments, allowing medical staff to detect COVID-19-infected patients more quickly and begin necessary treatments on schedule. The new coronavirus (COVID-19) illness is extremely contagious, thus there are often too many patients waiting in line for chest X-rays. This burdens the radiologists and physicians and has a detrimental impact on the patient's treatment and pandemic management. Due to this highly contagious condition, there aren't as many clinical amenities available, such as hospitals with critical care units and ventilatory machines, it is now crucial to categorise the patients according to their severity levels. Using deep learning techniques, we categorized the individual based on the severity levels of moderate, severe, and extreme if they tested positive for COVID-19. The COVID-19 patient severity divided into three groups: moderate, serious, and extreme, using Convolution Neural Network (CNN) three architecture: VGG19, ResNet-50 and DenseNet201 model that was constructed with an average accuracy of VGG19-89.63%, ResNet-50 with 92.62% and DenseNet201 with 96.4% with the input of chest X-ray pictures