The purpose of this study is to evaluate the performance of dose calculation algorithms used in radiotherapy treatment planning systems (TPSs) in comparison with Monte Carlo (MC) simulations in nonelectronic equilibrium conditions. MC simulations with PENELOPE package were performed for comparison of doses calculated by pencil beam convolution (PBC), analytical anisotropy algorithm (AAA), and Acuros XB TPS algorithms. Relative depth dose curves were calculated in heterogeneous water phantoms with layers of bone (1.8 g/cm) and lung (0.3 g/cm) equivalent materials for radiation fields between 1 × 1 cm and 10 × 10 cm. Analysis of relative depth dose curves at the water-bone interface shows that PBC and AAA algorithms present the largest differences to MC calculations (u = 0.5%), with maximum differences of up to 4.3% of maximum dose. For the lung-equivalent material and 1 × 1 cm field, differences can be up to 24.3% for PBC, 11.5% for AAA, and 7.5% for Acuros. Results show that Acurus presents the best agreement with MC simulation data with equivalent accuracy for modeling radiotherapy dose deposition especially in regions where electronic equilibrium does not hold. For typical (nonsmall) fields used in radiotherapy, AAA and PBC can exhibit reasonable agreement with MC results even in regions of heterogeneities.
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http://dx.doi.org/10.1016/j.meddos.2018.02.009 | DOI Listing |
Front Cell Infect Microbiol
March 2025
Department of Pharmacy, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China.
Objective: This study aimed to predict and evaluate the efficacy of various polymyxin B dosing regimens for Gram-negative bacteremia using Monte Carlo simulation, with a specific focus on assessing the efficacy in patients receiving continuous renal replacement therapy (CRRT). The goal was to optimize clinical dosing regimens and guide rational polymyxin B use in practice.
Methods: A total of 1,939 Gram-negative bacterial strains were analyzed, collected between April 2019 and December 2021 through the China Bloodstream Gram-negative Pathogens Antimicrobial Resistance and Virulence Surveillance Network (CARVIS-NET).
Camb Prism Extinct
December 2024
Research Institute for the Environment and Livelihoods, Charles Darwin University, Casuarina, NT, Australia.
Biodiversity is in rapid decline, but the extent of loss is not well resolved for poorly known groups. We estimate the number of extinctions for Australian non-marine invertebrates since the European colonisation of the continent. Our analyses use a range of approaches, incorporate stated uncertainties and recognise explicit caveats.
View Article and Find Full Text PDFFront Psychol
February 2025
The School of Information Resource Management, Renmin University of China, Beijing, China.
Based on Cognitive Load Theory, this study developed a moderated mediation model to examine the relationship between English as foreign language (EFL) teachers' air pollution appraisal and negative emotions. Specifically, it hypothesizes that air pollution appraisal significantly increases the mental effort of EFL teachers, which in turn leads to the manifestation of negative emotions. Additionally, the study introduces the concept that the working memory capacity of EFL teachers can negatively moderate the impact of increased mental effort on their emotions, effectively attenuating the overall mediating effect.
View Article and Find Full Text PDFFront Pharmacol
February 2025
Health Value, Madrid, Spain.
Objective: To estimate the economic impact of individualized dose optimization guided by antimicrobial therapeutic drug monitoring (TDM) in Spain, compared to no monitoring.
Methods: A cost analysis of antibiotic treatment of critically ill patients, with and without TDM, was performed using a probabilistic Markov model (with second-order Monte Carlo simulations). Three scenarios were analyzed based on three published meta-analyses (Analysis 1: Pai Mangalore, 2022; Analysis 2: Sanz-Codina, 2023; Analysis 3: Takahashi, 2023).
Philos Trans A Math Phys Eng Sci
March 2025
Division of Applied Mathematics, Brown University, Providence, RI 02906, USA.
When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible uncertainty due to noise in the data, uncertainty induced by insufficient data or inadequate parameterization and uncertainty related to the use of misspecified model equations. In addition, recently proposed approaches provide flexible ways to combine information from data with full or partial satisfaction of equations that typically encode physical principles.
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