Probing the mechanical properties of TNF-α stimulated endothelial cell with atomic force microscopy.

TNF-α (tumor necrosis factor-α) is a potent pro-inflammatory cytokine that regulates the permeability of blood and lymphatic vessels. The plasma concentration of TNF-α is elevated (> 1 pg/mL) in several pathologies, including rheumatoid arthritis, atherosclerosis, cancer, pre-eclampsia; in obese individuals; and in trauma patients. To test whether circulating TNF-α could induce similar alterations in different districts along the vascular system, three endothelial cell lines, namely HUVEC, HPMEC, and HCAEC, were characterized in terms of 1) mechanical properties, employing atomic force microscopy; 2) cytoskeletal organization, through fluorescence microscopy; and 3) membrane overexpression of adhesion molecules, employing ELISA and immunostaining. Upon stimulation with TNF-α (10 ng/mL for 20 h), for all three endothelial cells, the mechanical stiffness increased by about 50% with a mean apparent elastic modulus of E ~5 ± 0.5 kPa (~3.3 ± 0.35 kPa for the control cells); the density of F-actin filaments increased in the apical and median planes; and the ICAM-1 receptors were overexpressed compared with controls. Collectively, these results demonstrate that sufficiently high levels of circulating TNF-α have similar effects on different endothelial districts, and provide additional information for unraveling the possible correlations between circulating pro-inflammatory cytokines and systemic vascular dysfunction.

Role of TRP Channels in Mediating the Calcium Signaling Response of Brain Endothelial Cells to Mechanical Stretch

Traumatic brain injury (TBI) often results in disruption of the blood brain barrier (BBB), which is an integral component to maintaining the central nervous system homeostasis. Recently cytosolic calcium levels ([Ca2+]i), observed to elevate following TBI, have been shown to influence endothelial barrier integrity. However, the mechanism by which TBI-induced calcium signaling alters the endothelial barrier remains unknown. In the present study, an in vitro BBB model was utilized to address this issue. Exposure of cells to biaxial mechanical stretch, in the range expected for TBI, resulted in a rapid cytosolic calcium increase. Modulation of intracellular and extracellular Ca2+ reservoirs indicated that Ca2+ influx is the major contributor for the [Ca2+]i elevation. Application of pharmacological inhibitors was used to identify the calcium-permeable channels involved in the stretch-induced Ca2+ influx. Antagonist of transient receptor potential (TRP) channel subfamilies, TRPC and TRPP, demonstrated a reduction of the stretch-induced Ca2+ influx. RNA silencing directed at individual TRP channel subtypes revealed that TRPC1 and TRPP2 largely mediate the stretch-induced Ca2+ response. In addition, we found that nitric oxide (NO) levels increased as a result of mechanical stretch, and that inhibition of TRPC1 and TRPP2 abolished the elevated NO synthesis. Further, as myosin light chain (MLC) phosphorylation and actin cytoskeleton rearrangement are correlated with endothelial barrier disruption, we investigated the effect mechanical stretch had on the myosin-actin cytoskeleton. We found that phosphorylated MLC was increased significantly by 10 minutes post-stretch, and that inhibition of TRP channel activity or NO synthesis both abolished this effect. In addition, actin stress fibers formation significantly increased 2 minutes post-stretch, and was abolished by treatment with TRP channel inhibitors. These results suggest that, in brain endothelial cells, TRPC1 and TRPP2 are activated by TBI-mechanical stress and initiate actin-myosin contraction, which may lead to disruption of the BBB.

COMPREHENSIVE CALCULATION-BASED IMRT QA USING R&V DATA, TREATMENT RECORDS, AND A SECOND TREATMENT PLANNING SYSTEM

Purpose: Traditional patient-specific IMRT QA measurements are labor intensive and consume machine time. Calculation-based IMRT QA methods typically are not comprehensive. We have developed a comprehensive calculation-based IMRT QA method to detect uncertainties introduced by the initial dose calculation, the data transfer through the Record-and-Verify (R&V) system, and various aspects of the physical delivery. Methods: We recomputed the treatment plans in the patient geometry for 48 cases using data from the R&V, and from the delivery unit to calculate the “as-transferred” and “as-delivered” doses respectively. These data were sent to the original TPS to verify transfer and delivery or to a second TPS to verify the original calculation. For each dataset we examined the dose computed from the R&V record (RV) and from the delivery records (Tx), and the dose computed with a second verification TPS (vTPS). Each verification dose was compared to the clinical dose distribution using 3D gamma analysis and by comparison of mean dose and ROI-specific dose levels to target volumes. Plans were also compared to IMRT QA absolute and relative dose measurements. Results: The average 3D gamma passing percentages using 3%-3mm, 2%-2mm, and 1%-1mm criteria for the RV plan were 100.0 (σ=0.0), 100.0 (σ=0.0), and 100.0 (σ=0.1); for the Tx plan they were 100.0 (σ=0.0), 100.0 (σ=0.0), and 99.0 (σ=1.4); and for the vTPS plan they were 99.3 (σ=0.6), 97.2 (σ=1.5), and 79.0 (σ=8.6). When comparing target volume doses in the RV, Tx, and vTPS plans to the clinical plans, the average ratios of ROI mean doses were 0.999 (σ=0.001), 1.001 (σ=0.002), and 0.990 (σ=0.009) and ROI-specific dose levels were 0.999 (σ=0.001), 1.001 (σ=0.002), and 0.980 (σ=0.043), respectively. Comparing the clinical, RV, TR, and vTPS calculated doses to the IMRT QA measurements for all 48 patients, the average ratios for absolute doses were 0.999 (σ=0.013), 0.998 (σ=0.013), 0.999 σ=0.015), and 0.990 (σ=0.012), respectively, and the average 2D gamma(5%-3mm) passing percentages for relative doses for 9 patients was were 99.36 (σ=0.68), 99.50 (σ=0.49), 99.13 (σ=0.84), and 98.76 (σ=1.66), respectively. Conclusions: Together with mechanical and dosimetric QA, our calculation-based IMRT QA method promises to minimize the need for patient-specific QA measurements by identifying outliers in need of further review.