Bryostatin-1: a promising compound for neurological disorders

The central nervous system (CNS) is the most complex system in human body, and there is often a lack of effective treatment strategies for the disorders related with CNS. Natural compounds with multiple pharmacological activities may offer better options because they have broad cellular targets and potentially produce synergic and integrative effects. Bryostatin-1 is one of such promising compounds, a macrolide separated from marine invertebrates. Bryostatin-1 has been shown to produce various biological activities through binding with protein kinase C (PKC). In this review, we mainly summarize the pharmacological effects of bryostatin-1 in the treatment of multiple neurological diseases in preclinical studies and clinical trials. Bryostatin-1 is shown to have great therapeutic potential for Alzheimer’s disease, multiple sclerosis, fragile X syndrome, stroke, traumatic brain injury, and depression. It exhibits significant rescuing effects on the deficits of spatial learning, cognitive function, memory and other neurological functions caused by diseases, producing good neuroprotective effects. The promising neuropharmacological activities of bryostatin-1 suggest that it is a potential candidate for the treatment of related neurological disorders although there are still some issues needed to be addressed before its application in clinic

A DVH‐guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135098/1/mp5700.pd

GPU-Monte Carlo based fast IMRT plan optimization

Purpose: Intensity-modulated radiation treatment (IMRT) plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC) methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow.Methods: A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, a rough dose calculation is conducted with only a few number of particle per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final result.Results: For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion: A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.--------------------------------Cite this article as: Li Y, Tian Z, Shi F, Jiang S, Jia X. GPU-Monte Carlo based fast IMRT plan optimization. Int J Cancer Ther Oncol 2014; 2(2):020244. DOI: 10.14319/ijcto.0202.4