The Monte Carlo method provides the most accurate dose calculations on a patient computed tomography (CT) geometry. The increase in accuracy is, at least in part, due to the fact that instead of treating human tissues as water of various densities as in analytical algorithms, the Monte Carlo method allows human tissues to be characterized by elemental composition and mass density, and hence allows the accurate consideration of all relevant electromagnetic and nuclear interactions. On the other hand, the algorithm to convert CT Hounsfield numbers to tissue materials for Monte Carlo dose calculation introduces uncertainties. There is not a simple one to one correspondence between Hounsfield numbers and tissue materials. To investigate the effects of Hounsfield number conversion for proton Monte Carlo dose calculations, clinical proton treatment plans were simulated using the Geant4 Monte Carlo code. Three Hounsfield number to material conversion methods were studied. The results were compared in forms of dose volume histograms of gross tumor volume and clinical target volume. The differences found are generally small but can be dosimetrically significant. Further, different methods may cause deviations in the predicted proton beam range in particular for deep proton fields. Typically, slight discrepancies in mass density assignments play only a minor role in the target region, whereas more significant effects are caused by different assignments in elemental compositions. In the presence of large tissue inhomogeneities, for head and neck treatments, treatment planning decisions could be affected by these differences because of deviations in the predicted tumor coverage. Outside the target area, differences in elemental composition and mass density assignments both may play a role. This can lead to pronounced effects for organs at risk, in particular in the spread-out Bragg peak penumbra or distal regions. In addition, the significance of the elemental composition effect (dose to water vs. dose to tissue) is tissue-type dependent and is also affected by nuclear reactions.
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http://dx.doi.org/10.1118/1.2715481 | DOI Listing |
Sci Rep
December 2024
Clermont Auvergne University, CNRS, IRD, OPGC, Magmas and Volcanoes Laboratory, 63000, Clermont-Ferrand, France.
The new submarine volcano Fani Maoré offshore Mayotte (Comoros archipelago) discovered in 2019 has raised the awareness of a possible future eruption in Petite-Terre island, located on the same 60 km-long volcanic chain. In this context of a renewal of the volcanic activity, we present here the first volcanic hazard assessment in Mayotte, focusing on the potential reactivation of the Petite-Terre eruptive centers. Using the 2-D tephra dispersal model HAZMAP and the 1979 - 2021 meteorological ERA-5 database, we first identify single eruptive scenarios of various impacts for the population of Mayotte.
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December 2024
Bioinformatics Laboratory, College of Computing, University Mohammed VI Polytechnic, Ben Guerir, Morocco.
Hepatitis C virus (HCV) presents a significant global health issue due to its widespread prevalence and the absence of a reliable vaccine for prevention. While significant progress has been achieved in therapeutic interventions since the disease was first identified, its resurgence underscores the need for innovative strategies to combat it. The nonstructural protein NS5A is crucial in the life cycle of the HCV, serving as a significant factor in both viral replication and assembly processes.
View Article and Find Full Text PDFAdv Mater
December 2024
Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
Magnetoplumbites are one of the most broadly studied families of hexagonal ferrites, typically with high magnetic ordering temperatures, making them excellent candidates for permanent magnets. However, magnetic frustration is rarely observed in magnetoplumbites. Herein, the discovery, synthesis, and characterization of the first Mn-based magnetoplumbite, as well as the first magnetoplumbite involving pnictogens (Sb), ASbMnO (A = K or Rb) are reported.
View Article and Find Full Text PDFAIP Adv
December 2024
Center for Natural Sciences, University of Pannonia, Egyetem u. 10, Veszprém 8200, Hungary.
We present simulation results for the Donnan equilibrium between a homogeneous bulk reservoir and inhomogeneous confining geometries with varying number of restricted dimensions, . Planar slits ( = 1), cylindrical pores ( = 2), and spherical cavities ( = 3) are considered. The walls have a negative surface charge density.
View Article and Find Full Text PDFACS Cent Sci
December 2024
Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172 Mestre, Italy.
Computational generation of cyclic peptide inhibitors using machine learning models requires large size training data sets often difficult to generate experimentally. Here we demonstrated that sequential combination of Random Forest Regression with the pseudolikelihood maximization Direct Coupling Analysis method and Monte Carlo simulation can effectively enhance the design pipeline of cyclic peptide inhibitors of a tumor-associated protease even for small experimental data sets. Further studies showed that such -evolved cyclic peptides are more potent than the best peptide inhibitors previously developed to this target.
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