SU-E-T-491: A FLUKA Monte Carlo Computational Model of a Scanning Proton Beam Therapy Nozzle at IU Proton Therapy Center.

PURPOSE: Charged particle therapy, especially proton therapy is a growing treatment modality worldwide. Monte Carlo (MC) simulation of the interactions of proton beam with equipment, devices and patient is a highly efficient tool that can substitute measurements for complex and unrealistic experiments. The purpose of this study is to design a MC model of a treatment nozzle to characterize the proton scanning beam and commissioning the model for the Indiana University Health Proton Therapy Center (IUHPTC. METHODS: The general purpose Monte Carlo code FLUKA was used for simulation of the proton beam passage through the elements of the treatment nozzle design. The geometry of the nozzle was extracted from the design blueprints. The initial parameters for beam simulation were determined from calculations of beam optics design to derive a semi-empirical model to describe the initial parameters of the beam entering the nozzle. The lateral fluence and energy distribution of the beam entering the nozzle is defined as a function of the requested range. The uniform scanning model at the IUHPTC is implemented. The results of simulation with the beam and nozzle model are compared and verified with measurements. RESULTS: The lateral particle distribution and energy spectra of the proton beam entering the nozzle were compared with measurements in the interval of energies from 70 MeV to 204.8 MeV. The accuracy of the description of the proton beam by MC simulation is better than 2% compared with measurements, providing confidence for complex simulation in phantom and patient dosimetry with the MC simulated nozzle and the uniform scanning proton beam. CONCLUSIONS: The treatment nozzle and beam model was accurately implemented in the FLUKA Monte Carlo code and suitable for the research purpose to simulate the scanning beam at IUHPTC.

Comparison between plasma and milk levels of leptin during pregnancy and lactation in cow, a relationship with beta-lactoglobulin.

Leptin gene is expressed in the mammary tissue and the expression of both leptin and its receptor changes significantly during pregnancy and lactation, with high levels during the first half of pregnancy and a decrease at delivery. The aim of this work was to investigate into leptin concentration in plasma and in milk during pregnancy and the first week after parturition in dairy cow and to analyze the correlation between leptin and beta-lactoglobulin (beta-LG) concentrations in plasma and in milk. The trial was conducted on six Holstein dairy cows, reared in the same environmental conditions and evaluated with similar body condition score, during the complete reproductive cycle from insemination to the delivery. Blood from the jugular vein and milk samples were collected at weekly intervals. Plasma leptin concentration showed a lower level (p < 0.05) at the beginning of pregnancy. Milk leptin concentration showed a higher level (p < 0.01) than plasma level from week 23 to week 29 of pregnancy. Plasma beta-LG concentrations were higher (p < 0.01) compared to plasma concentrations during the first part of pregnancy, then milk levels rise and become higher than plasma levels during the last weeks before dry period. A positive correlation (p < 0.01) was observed between leptin and beta-LG both in plasma and in milk profiles.

Non-linear dynamics of cardiovascular variability signals.

Long-term regulation of beat-to-beat variability involves several different kinds of controls. A linear approach performed by parametric models enhances the short-term regulation of the autonomic nervous system. Some non-linear long-term regulation can be assessed by the chaotic deterministic approach applied to the beat-to-beat variability of the discrete RR-interval series, extracted from the ECG. For chaotic deterministic systems, trajectories of the state vector describe a strange attractor characterized by a fractal of dimension D. Signals are supposed to be generated by a deterministic and finite dimensional but non-linear dynamic system with trajectories in a multi-dimensional space-state. We estimated the fractal dimension through the Grassberger and Procaccia algorithm and Self-Similarity approaches of the 24-h heart-rate variability (HRV) signal in different physiological and pathological conditions such as severe heat failure, or after heart transplantation. State-space representations through Return Maps are also obtained. Differences between physiological and pathological cases have been assessed and generally a decrease in the system complexity is correlated to pathological conditions.