Purpose: The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). Materials and Methods: A retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm. Results: The mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. Conclusion The artifacts from the CBCT’s limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution.

Purpose: The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). Materials and Methods: A retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm. Results: The mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. Conclusion The artifacts from the CBCT’s limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution.

Both metabolic syndrome (MetS) and elevated LDL cholesterol (LDL-C) increase the risk for cardiovascular disease (CVD). We hypothesized that low HDL cholesterol (HDL-C) would further increase CVD risk in women having both conditions. To assess this, we recruited 89 women with MetS (25-72 y) and LDL-C > or = 2.6 mmol/L. To determine whether plasma HDL-C concentrations were associated with dietary components, circulating atherogenic particles, and other risk factors for CVD, we divided the subjects into two groups: high HDL-C (H-HDL) (> or = 1.3 mmol/L, n = 32) and low HDL-C (L-HDL) (< 1.3 mmol/L, n = 57). Plasma lipids, insulin, adiponectin, apolipoproteins, oxidized LDL, Lipoprotein(a), and lipoprotein size and subfractions were measured, and 3-d dietary records were used to assess macronutrient intake. Women with L-HDL had higher sugar intake and glycemic load (P < 0.05), higher plasma insulin (P < 0.01), lower adiponectin (P < 0.05), and higher numbers of atherogenic lipoproteins such as large VLDL (P < 0.01) and small LDL (P < 0.001) than the H-HDL group. Women with L-HDL also had larger VLDL and both smaller LDL and HDL particle diameters (P < 0.001). HDL-C was positively correlated with LDL size (r = 0.691, P < 0.0001) and HDL size (r = 0.606, P < 0.001), and inversely correlated with VLDL size (r = -0.327, P < 0.01). We concluded that L-HDL could be used as a marker for increased numbers of circulating atherogenic lipoproteins as well as increased insulin resistance in women who are already at risk for CVD.
Adiponectin ; Apolipoproteins ; Cardiovascular Diseases ; Cholesterol, HDL ; Cholesterol, LDL ; Diet Records ; Female ; Humans ; Insulin ; Insulin Resistance ; Lipoprotein(a) ; Lipoproteins ; Lipoproteins, LDL ; Plasma ; Risk Factors

Despite great progress in the detection and treatment of prostate cancer, this disease remains an incredible health and economic burden. Although androgen receptor (AR) signaling plays a key role in the development and progression of prostate cancer, aberrations in other molecular pathways also contribute to the disease, making it essential to identify and develop drugs against novel targets, both for the prevention and treatment of prostate cancer. One promising target is the peroxisome proliferator-activated receptor gamma (PPARγ) protein. PPARγ was originally thought to act as a tumor suppressor in prostate cells because agonist ligands inhibited the growth of prostate cancer cells; however, additional studies found that PPARγ agonists inhibit cell growth independent of PPARγ. Furthermore, PPARγ expression increases with cancer grade/stage, which would suggest that it is not a tumor suppressor but instead that PPARγ activity may play a role in prostate cancer development and/or progression. Indeed, two new studies, taking vastly different, unbiased approaches, have identified PPARγ as a target in prostate cancer and suggest that PPARγ inhibition might be useful in prostate cancer prevention and treatment. These findings could lead to a new therapeutic weapon in the fight against prostate cancer.
Humans ; Male ; PPAR gamma/metabolism* ; Prostatic Neoplasms/metabolism*