Fractal analysis of CE CT lung tumours images

AIM The fractal dimension (FD) of a structure provides a measure of its complexity. This pilot study aims to determine FD values for lung cancers visualised on Computed Tomography (CT) and to assess the potential for tumour FD measurements to provide an index of tumour aggression. METHOD Pre-and post-contrast CT images of the thorax acquired from 15 patients with lung cancers of greater than 10mm were transformed to fractal dimension images using a box-counting algorithm at various scales. A region of interest (ROI) was determined covering tumour locations, which were more apparent on FD images as compared to images before processing. The average tumour FD (FDavg) was computed and compared with the intensity average before FD processing. FD values were correlated with 2 markers of tumour aggression: tumour stage and tumour uptake of fluorodeoxyglucose (FDG) as determined by Positron Emission Tomography. RESULTS For pre-contrast images, the tumour FDavg correlated with tumour stage (r = 0.537, p = 0.0387) and FDG uptake (r= 0.64, p< 0.001). FDavg decreased following contrast enhancement for most tumours. CONCLUSION Fractal analysis of CT images of lung tumours could potentially provide additional information about likely tumour aggression and so impact on clinical management decisions and choice of treatment

Texture analysis of aggressive and nonaggressive lung tumor CE CT images

This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluoro deoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure

Changes in endotoxin levels in T2DM subjects on anti-diabetic therapies

Introduction Chronic low-grade inflammation is a significant factor in the development of obesity associated diabetes. This is supported by recent studies suggesting endotoxin, derived from gut flora, may be key to the development of inflammation by stimulating the secretion of an adverse cytokine profile from adipose tissue. Aims The study investigated the relationship between endotoxin and various metabolic parameters of diabetic patients to determine if anti-diabetic therapies exerted a significant effect on endotoxin levels and adipocytokine profiles. Methods Fasting blood samples were collected from consenting Saudi Arabian patients (BMI: 30.2 ± (SD)5.6 kg/m2, n = 413), consisting of non-diabetics (ND: n = 67) and T2DM subjects (n = 346). The diabetics were divided into 5 subgroups based on their 1 year treatment regimes: diet-controlled (n = 36), metformin (n = 141), rosiglitazone (RSG: n = 22), a combined fixed dose of metformin/rosiglitazone (met/RSG n = 100) and insulin (n = 47). Lipid profiles, fasting plasma glucose, insulin, adiponectin, resistin, TNF-α, leptin, C-reactive protein (CRP) and endotoxin concentrations were determined. Results Regression analyses revealed significant correlations between endotoxin levels and triglycerides (R2 = 0.42; p < 0.0001); total cholesterol (R2 = 0.10; p < 0.001), glucose (R2 = 0.076; p < 0.001) and insulin (R2 = 0.032; p < 0.001) in T2DM subjects. Endotoxin showed a strong inverse correlation with HDL-cholesterol (R2 = 0.055; p < 0.001). Further, endotoxin levels were elevated in all of the treated diabetic subgroups compared with ND, with the RSG treated diabetics showing significantly lower endotoxin levels than all of the other treatment groups (ND: 4.2 ± 1.7 EU/ml, RSG: 5.6 ± 2.2 EU/ml). Both the met/RSG and RSG treated groups had significantly higher adiponectin levels than all the other groups, with the RSG group expressing the highest levels overall. Conclusion We conclude that sub-clinical inflammation in T2DM may, in part, be mediated by circulating endotoxin. Furthermore, that whilst the endotoxin and adipocytokine profiles of diabetic patients treated with different therapies were comparable, the RSG group demonstrated significant differences in both adiponectin and endotoxin levels. We confirm an association between endotoxin and serum insulin and triglycerides and an inverse relationship with HDL. Lower endotoxin and higher adiponectin in the groups treated with RSG may be related and indicate another mechanism for the effect of RSG on insulin sensitivity

Combined statistical and model based texture features for improved image classification

This paper aims to improve the accuracy of texture classification based on extracting texture features using five different texture measures and classifying the patterns using a naive Bayesian classifier. Three statistical-based and two model-based methods are used to extract texture features from eight different texture images, then their accuracy is ranked after using each method individually and in pairs. The accuracy improved up to 97.01% when model based - Gaussian Markov random field (GMRF) and fractional Brownian motion (fBm) - were used together for classification as compared to the highest achieved using each of the five different methods alone; and proved to be better in classifying as compared to statistical methods. Also, using GMRF with statistical based methods, such as grey level co-occurrence (GLCM) and run-length (RLM) matrices, improved the overall accuracy to 96.94% and 96.55%; respectively

SELECTIVE SMALL MOLECULE TARGETING OF MCL-1 IN MULTIPLE MYELOMA

Multiple Myeloma (MM) is a deadly blood malignancy, characterized by the uncontrolled proliferation of aberrantly differentiated plasma cells. MM is challenging to diagnose and treat, accounting for approximately 12% of hematologic malignancies. The overexpression of anti-apoptotic group of Bcl-2 family proteins, particularly Myeloid cell leukemia 1 (Mcl-1), play a critical role in the pathogenesis of MM. The overexpression of Mcl-1 is associated with drug resistance and overall poor prognosis. Thus, inhibition of the Mcl-1 protein is an attractive therapeutic strategy against myeloma cells. Over the last decade, the development of selective Mcl-1 inhibitors has seen remarkable advancement. In this project, we investigated the effect of the novel Mcl-1 inhibiting agent KS18 on MM cells. We demonstrated the molecules in vitro efficacy as well superior potency towards MM. However, Mcl-1 inhibition by KS18 was associated with a significant reduction of MM cell viability. Moreover, we observed that KS18 was able to induce apoptosis in MM cells in a caspase-dependent manner. Our results propose that targeting Mcl-1 by KS18 may represent a new viable strategy for MM treatment. Furthermore, the present study uncovers the mechanism of action of KS18 and provides the foundation for in vivo assessment of this novel molecule