Introduction: Epicardial fat volume (EFV) has been reported to correlate with the severity of coronary artery disease (CAD). Pericardial fat volume (PFV) has recently been reported to be strongly associated with CAD severity and presence. We aimed to investigate the relationship between EFV and PFV with severity of coronary artery stenosis in patients undergoing 64-slice multi-slice computed tomography (MSCT). Methods: One hundred and fifty one patients undergoing MSCT for suspected CAD were enrolled. Non-enhanced images were acquired to assess calcium score. Contrast enhanced images were used to quantify EFV, PFV and severity of luminal stenosis. Results: Coronary artery stenosis was mild in 25 cases (16.6%), moderate in 58 cases (38.4%) and severe in 68 cases (45%). With increase in severity of coronary artery stenosis, there was significant increase in PFV, EFV as well as epicardial fat thickness in right ventricle free wall in basal view and epicardial fat thickness in left ventricle posterior wall in mid and apical view. There was significant linear correlation between PFV with coronary calcification score (r=0.18, P=0.02), between coronary artery stenosis severity and PFV (r=0.75, P<0.001), EFV (r=0.79, P<0.001), apical epicardial fat thickness in right ventricle free wall (r=0.29, P<0.001), Mid (r=0.28, P<0.001) and basal (r=0.23, P=0.004) epicardial fat thickness in left ventricle posterior wall. Conclusion: PFV, EFV and regional epicardial thickness are correlated with severity of CAD and could be used as a reliable marker in predicting CAD severity.

Background: The treatment of chronic low back pain (LBP) should target both behavioral variables and physical performance factors. Hypothesis: Cognitive functional treatment (CFT) and lumbar stabilization treatment (LST) will result in positive changes in pain and lumbar movement control (LMC) in patients with LBP. Study Design: Pretest-posttest intervention. Level of Evidence: Level 3. Methods: After screening, 52 participants (mean age, 44.3 ± 2.46 years) with chronic LBP were allocated into CFT (n = 17), LST (n = 17), or control (n = 18) groups. Pain and LMC were evaluated before and after 8 weeks of intervention with visual analog scale (VAS) and Luomajoki LMC battery tests, respectively. Results: Compared with baseline, pain and LMC were reduced and improved significantly in both groups after 8 weeks. However, the changes in both variables were not significantly different between groups. Percent change for pain between pretest and posttest values in the LST group was a decrease of 45% ( P = 0.003), compared with a decrease of 40% ( P = 0.003) in the CFT group. Change in LMC in the LST group was a decrease of 100% ( P = 0.026), compared with a decrease of 200% ( P = 0.018) in the CFT group. There as no change for both variables in the control group. Conclusion: Both CFT and LST groups improved LMC scores and reduced pain intensity. However, there was no difference between the 2 experimental groups on pain and LMC test results. Clinical Relevance: In this study, intended to construct an intervention for people with chronic LBP, the primary aims were to help individuals “make sense of their pain,” develop effective pain control strategies via body relaxation and extinction of safety behaviors, and adopt healthy lifestyle behaviors to affect cognitive factors known to affect pain sensitivity and disability. These primary aims were achieved through an emphasis on factors such as development of positive beliefs, reduced fear, increased awareness, enhanced understanding and control of pain, adaptive coping, enhanced self-efficacy, confidence, and improved mood through the class-based intervention.

Heart mediastinal and epicardial fat tissues are related to several adverse metabolic effects and cardiovascular risk factors, especially coronary artery disease (CAD). The manual segmentation of those fats is that the high dependence on user intervention and time-consuming analyzes. As a result, the automated measurement of cardiac fats could be considered as one of the most important biomarkers for cardiovascular risks in imaging and medical visualization by physicians. In this paper, we validate an automatic approach for the cardiac fat segmentation in non-contrast CT images then investigate the correlation between cardiac fat volume and CAD using the association rule mining algorithm. The pre-processing step includes threshold and contrast enhancement, the feature extraction step includes Gabor filter bank based on GLCM, the cardiac fat segmentation step is predicated on pattern recognition classification algorithms, and eventually, the step of investigating the relationship between cardiac fat volume and CAD is using FP-Growth algorithm. Experimental validation using CT images of two databases points to a good performance in cardiac fat segmentation. Experiments showed that the accuracy of the designed algorithm using the ensemble classifier with the best performance over other classifiers for the cardiac fat segmentation was 99.2%, with a sensitivity of 96.3% and a specificity of 99.8%. The results of using the FP-Growth algorithm showed that the low volume of epicardial (Confidence=0.6818, Lift=1.0626) and mediastinal (Confidence=0.6696, Lift=1.0436) fat are associated with healthy individuals and the high volume of epicardial (Confidence=0.8, Lift=2.2326) and mediastinal (Confidence=0.75, Lift=2.093) fat are related to individuals of CAD. As a result, cardiac fats can be used as a reliable biomarker tool in predicting the extent of CAD stenosis.

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