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The present study was designed to examine the effects of Aloe vera on whole body X-ray exposure induced injury to heart, lung, and bone of male balb/c mice. Animals were divided into four groups: control, Aloe vera (50 mg/kg body weight on alternate days for 30 days), X-ray (2Gy) and Aloe vera+ X-ray. X-ray irradiation led to enhanced lipid peroxidation level associated with decline in reduced glutathione concentration in pulmonary tissue of mice. Moreover, lipid peroxidation level and reduced glutathione content in cardiac tissue remained unaltered after radiation exposure. In addition, X-ray exposure caused poor and delayed uptake of 99mTc-mebrofenin as observed in hepatobiliary clearance study. Dual energy X-ray absorptiometry scan revealed a significant decrease in bone mineral density after X-ray irradiation. Aloe vera administration to radiation exposed animals restored pulmonary reduced glutathione content and lipid peroxidation level along with significantly improved bone mineral density and hepatobiliary clearance profile as compared to irradiated counterparts. The current observations suggest that Aloe vera plays vital role in modulating deleterious effects caused by X-ray exposure in various organs of mice, which may attributed its free radical scavenging ability and strengthening of antioxidant defense system. [ABSTRACT FROM AUTHOR]

Abstract Background The current study’s purpose is to compare hip structural analysis variables in a group of postmenopausal women with sarcopenia and another group of postmenopausal women with normal skeletal muscle mass index. To do so, the current study included 8 postmenopausal women (whose ages ranged between 65 and 84 years) with sarcopenia and 60 age-matched controls (with normal skeletal muscle mass index (SMI)). Body composition and bone parameters were evaluated by dual-energy X-ray absorptiometry (DXA). Results Weight, lean mass, body mass index, femoral neck cross-sectional area (FN CSA), FN section modulus (Z), FN cross sectional moment of inertia (CSMI), intertrochanteric (IT) CSA, IT Z, IT CSMI, IT cortical thickness (CT), femoral shaft (FS) CSA, FS Z and FS CSMI were significantly greater (p

Background: The current study's purpose is to compare hip structural analysis variables in a group of postmenopausal women with sarcopenia and another group of postmenopausal women with normal skeletal muscle mass index. To do so, the current study included 8 postmenopausal women (whose ages ranged between 65 and 84 years) with sarcopenia and 60 age-matched controls (with normal skeletal muscle mass index (SMI)). Body composition and bone parameters were evaluated by dual-energy X-ray absorptiometry (DXA). Results: Weight, lean mass, body mass index, femoral neck cross-sectional area (FN CSA), FN section modulus (Z), FN cross sectional moment of inertia (CSMI), intertrochanteric (IT) CSA, IT Z, IT CSMI, IT cortical thickness (CT), femoral shaft (FS) CSA, FS Z and FS CSMI were significantly greater (p < 0.05) in women with normal SMI compared to women with sarcopenia. In the whole population, SMI was positively associated with IT CSA, IT Z, IT CSMI, IT CT, FS CSA, FS Z, FS CSMI, FS CT but negatively correlated to IT buckling ratio (BR) and FS BR. Conclusion: The current study suggests that sarcopenia has a negative effect on hip bone strength indices in postmenopausal women. [ABSTRACT FROM AUTHOR]

Background Conventional DXA imaging of spine and hip to measure bone mineral density (BMD) has limitations in patients with ankylosing spondylitis (AS). We investigated the correlation of hip and spine BMD measurements in patients with AS to determine if hip DXA will prove clinically useful while avoiding the confounding effect of spinal disease. Also, we studied risk factors for osteoporosis (OP) and osteopenia in AS. Methods We randomly identified patients from our validated AS registry ≥18 years of age who met the Modified New York Classification criteria for AS. BMD was measured and interpreted using ISCD 2007 guidelines and diagnosis of OP was based on WHO criteria. ESR, CRP, urinary N-telopeptide, and 25-hydroxy vitamin D were also measured. Correlation between the BMD (total hip and/or femoral neck) and lumbar spine was calculated. Statistical comparisons between the 2 sites, lumbar spine (AP) and hip (total hip and or femoral neck) were made using Bowker’s test for symmetry and kappa statistics. Chi-square and odds ratio using logistic regression were used to assess the association of the purported risk factors for OP in these patients. Results Frequency of OP among AS patients ≥50 years of age was 23%, and that of osteopenia was 41%. Among patients <50 years of age, the frequency of low bone mass for expected age ( Z -score ≤−2.0) was 14.7%. There was moderate correlation ( ρ = 0.59) and a fair agreement (κ = 0.26; 95% CI: 0.10–0.42) between the lowest T -values of hip and lumbar spine (AP view). OP was significantly associated with elevated CRP level [OR = 4.2 (95% CI: 1.13–15.9), p < 0.03] and African American race [OR = 7.2 (95% CI: 1.18–44.99), p < 0.03]. Conclusion Our results demonstrated a moderate correlation and fair agreement between the T -scores of hip and the lumbar spine (AP view) in patients with AS, suggesting that DXA of the hip and the lumbar spine (AP view) may both be useful for OP and osteopenia screening in patients with AS without fused spines. We confirm the previous reports of an association of elevated CRP level with an increased risk of OP in patients with AS, but this is the first study to demonstrate that African American patients with AS may be at a higher risk of developing OP compared to Caucasians. [ABSTRACT FROM AUTHOR]

Regression-based schemes have proven effective for locating landmarks on images. Most previous approaches either predict the positions of all points simultaneously, or use regressors that predict individual points combined with a global shape constraint. The former can be efficient, but such models tend to be less robust. Conversely, Random Forest (RF) voting methods for individual points have been shown to be robust and accurate, but can lead to very large models. We explore the continuum between these two approaches by training RF regressors to predict subsets of points. Multi-point regression voting was implemented within the Random Forest Regression Voting Constrained Local Model frame- work and evaluated on clinical and facial images. Significant model size reductions were achieved with little difference in accuracy. The approach may therefore be useful where high numbers of points, and limitations on memory or disk space, make single-point models impractically large. [ABSTRACT FROM AUTHOR]