The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES

Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.

Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity toward tadpoles?

Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation (IIC) and correlation intensity index (CII) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The CII was more effective than IIC for the models considered here.

[Research Photon Energy Spectrum of Medical Linear Accelerator by Monte Carlo Method]

Objective

The distribution of the photon energy spectrum in isocenter plane of the medical linear accelerator and the influence of secondary collimator on the photon energy spectrum are studied. Methods Use the BEAMnrc program to simulate the transmission of the 6 MeV electrons and photons in 5 cm×5 cm,10 cm×10 cm,15 cm×15 cm and 20 cm×20 cm fields in treatment head of the medical linear accelerator, where a phase space file was set up at the isocenter plane to record the particle information passing through this plane. The BEAMdp program is used to analyze the phase space file, in order to obtain the distribution of the photon energy spectrum in isocenter plane and the influence of secondary collimator on the photon energy spectrum.

Results

By analyzing the photon energy spectrum of a medical linear accelerator with a nominal energy of 6 MV, it is found that the secondary collimator has little effect on the photon energy spectrum; different fields have different photon energy spectrum distributions; the photon energy spectrum in different central regions of the same field have the same normalized distribution.

Conclusion

In the dose calculation of radiation therapy, the influence of photon energy spectrum should be carefully considered.

Monte Carlo confidence intervals for the indirect effect with missing data

Missing data is a common occurrence in mediation analysis. As a result, the methods used to construct confidence intervals around the indirect effect should consider missing data. Previous research has demonstrated that, for the indirect effect in data with complete cases, the Monte Carlo method performs as well as nonparametric bootstrap confidence intervals (see MacKinnon et al., Multivariate Behavioral Research, 39(1), 99-128, 2004; Preacher & Selig, Communication Methods and Measures, 6(2), 77-98, 2012; Tofighi & MacKinnon, Structural Equation Modeling: A Multidisciplinary Journal, 23(2), 194-205, 2015). In this manuscript, we propose a simple, fast, and accurate two-step approach for generating confidence intervals for the indirect effect, in the presence of missing data, based on the Monte Carlo method. In the first step, an appropriate method, for example, full-information maximum likelihood or multiple imputation, is used to estimate the parameters and their corresponding sampling variance-covariance matrix in a mediation model. In the second step, the sampling distribution of the indirect effect is simulated using estimates from the first step. A confidence interval is constructed from the resulting sampling distribution. A simulation study with various conditions is presented. Implications of the results for applied research are discussed.

Enhancing analytical development in the pharmaceutical industry: A DoE-QSRR model for virtual Method Operable Design Region assessment

Recently, the pharmaceutical industry has increasingly adopted the Analytical Quality by Design (AQbD) approach for analytical development. To facilitate AQbD approach implementation in the development of chromatographic methods for determining cephalosporin antibiotics, an in silico tool capable of performing virtual DoEs was developed enabling to obtain virtual operable regions of method. To this end, the drugs cephalexin, cefazolin, cefotaxime and ceftriaxone were analyzed using four experimental designs, deriving a DoE-QSRR model and employing Monte Carlo method. The DoE-QSRR model and virtual DoEs were validated using data not used in model's construction, obtaining coefficients of determination of 84.72 % for DoE-QSRR model and over 77 % for virtual DoEs. Virtual MODRs were constructed using data from the virtual DoEs. The virtual MODRs were validated by comparing them with experimental MODRs under various scenarios, with overlap areas reaching values exceeding 84 %. Therefore, the in silico tool was considered suitable for indicating analyte trends under different analytical conditions, being capable of performing virtual DoEs for cephalosporin drugs with sufficient assertiveness to guide analytical development and allow obtaining a MODR capable of providing results of adequate quality.

Statistical power analysis and sample size planning for moderated mediation models

Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this paper, we introduce new simulation-based methods for power analysis of conditional process models with a focus on moderated mediation models. These simulation-based methods provide intuitive ways for sample-size planning based on regression coefficients in a moderated mediation model as well as selected variance and covariance components. We demonstrate how the methods can be applied to five commonly used moderated mediation models using a simulation study, and we also assess the performance of the methods through the five models. We implement our approaches in the WebPower R package and also in Web apps to ease their application.

Calibrating GESPECOR model of computing the full-energy peak efficiency of coaxial high-purity germanium detectors by Monte Carlo simulation

In this work, a classical approach was used for calibrating the GESPECOR detector model for computing the full-energy peak efficiency of p-type coaxial HPGe detectors that is based on the use of linear least squares optimization. The key element of the work is the multiplicative model developed for approximating the values of the full-energy peak efficiency provided by GESPECOR code. It was linearized using the logarithmic transformation to allow an easy use of the linear least squares optimization. A procedure was also developed to estimate the optimal values of the parameters, describing the p-type coaxial HPGe detectors. Its application to a Canberra detector GC3018 showed that it is possible to determine accurate values of the full-energy peak efficiency computed by GESPECOR code using the optimized parameter values.

Methods for the Design and Analysis of Analytical Ultracentrifugation Experiments

Analytical ultracentrifugation experiments play an integral role in the solution-phase characterization of biological macromolecules and their interactions. This unit discusses the design of sedimentation velocity and sedimentation equilibrium experiments performed with a Beckman Proteomelab XL-A or XL-I analytical ultracentrifuge and with a Beckman Optima AUC. Instrument settings and experimental design considerations are explained, and strategies for the analysis of experimental data with the UltraScan data analysis software package are presented. Special attention is paid to the strengths and weaknesses of the available detectors, and guidance is provided on how to extract maximum information from analytical ultracentrifugation experiments. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.

Neutron flux evaluation algorithm with a combination of Monte Carlo and removal-diffusion calculation methods for boron neutron capture therapy

BACKGROUND

In Japan, the clinical treatment of boron neutron capture therapy (BNCT) has been applied to unresectable, locally advanced, and recurrent head and neck carcinomas using an accelerator-based neutron source since June of 2020. Considering the increase in the number of patients receiving BNCT, efficiency of the treatment planning procedure is becoming increasingly important. Therefore, novel and rapid dose calculation algorithms must be developed. We developed a novel algorithm for calculating neutron flux, which comprises of a combination of a Monte Carlo (MC) method and a method based on the removal-diffusion (RD) theory (RD calculation method) for the purpose of dose calculation of BNCT.

PURPOSE

We present the details of our novel algorithm and the verification results of the calculation accuracy based on the MC calculation result.

METHODS

In this study, the "MC-RD" calculation method was developed, wherein the RD calculation method was used to calculate the thermalization process of neutrons and the MC method was used to calculate the moderation process. The RD parameters were determined by MC calculations in advance. The MC-RD calculation accuracy was verified by comparing the results of the MC-RD and MC calculations with respect to the neutron flux distributions in each of the cubic and head phantoms filled with water.

RESULTS

Comparing the MC-RD calculation results with those of MC calculations, it was found that the MC-RD calculation accurately reproduced the thermal neutron flux distribution inside the phantom, with the exception of the region near the surface of the phantom.

CONCLUSIONS

The MC-RD calculation method is useful for the evaluation of the neutron flux distribution for the purpose of BNCT dose calculation, except for the region near the surface.

Monte Carlo simulations of organ and effective doses and dose-length product for dental cone-beam CT

OBJECTIVES

The use of dental cone-beam CT (CBCT) has increased in recent years. We aimed to calculate the organ and effective doses in dental CBCT using Monte Carlo simulation (MCS) and to correlate the effective dose with the dose-length product (DLP), which is a radiation dose index.

METHODS

Organ and effective doses were calculated by MCS using the adult male and female reference phantoms of the International Commission on Radiological Protection publication 110 in a half-rotation scan of the CBCT scanner Veraviewepocs 3Df. The simulations were performed by setting nine protocols in combination with the field-of-view (FOV) and imaging region. In addition, DLPs were calculated by MCS using the virtual CT Dose Index (CTDI) and CBCT phantoms, with the same protocol.

RESULTS

The effective doses were 55 and 195 μSv at the minimum FOV of Φ40 × H40 mm and maximum FOV of Φ 80 × H80 mm, respectively. The organs with the major contribution to the effective dose were the red bone marrow (11.0‒12.8%), thyroid gland (4.0‒12.7%), salivary gland (21.8‒33.2%), and remaining tissues (35.1‒45.7%). Positive correlations were obtained between the effective dose and calculated DLP using the CTDI and CBCT phantoms.

CONCLUSIONS

Organ and effective doses for each protocol of dental CBCT could be estimated using MCS. There was a positive correlation between the effective dose and DLP, suggesting that DLP can be used to estimate the effective dose of CBCT.

Strategies to convert hospital beds for COVID-19 patients to minimize emergency department overcrowding

Background: The shortage of hospital beds for COVID-19 patients has been one critical cause of Emergency Department (ED) overcrowding. Purpose: We aimed at elaborating a strategy of conversion of hospital beds, from non-COVID-19 to COVID-19 care, minimizing both ED overcrowding and the number of beds eventually converted. Research Design: Observational retrospective study. Study Sample: We considered the centralized database of all ED admissions in the Lombardy region of Italy during the second "COVID-19 wave" (October to December 2020). Data collection and Analysis: We analyzed all admissions to 82 EDs. We devised a family of Monte Carlo simulations to evaluate the performance of hospital beds' conversion strategies triggered by ED crowding of COVID-19 patients, determining a critical number of beds to be converted when passing an ED-specific crowding threshold. Results: Our results suggest that the maximum number of patients waiting for hospitalization could have been decreased by 70% with the proposed strategy. Such a reduction would have been achieved by converting 30% more hospital beds than the total number converted in the region. Conclusions: The disproportion between reduction in ED crowding and additionally converted beds suggests that a wide margin to improve the efficiency of the conversions exists. The proposed simulation apparatus can be easily generalized to study management policies synchronizing ED output and in-hospital bed availability.

Impact of possible errors in natural language processing-derived data on downstream epidemiologic analysis

Objective

To assess the impact of potential errors in natural language processing (NLP) on the results of epidemiologic studies.

Materials and Methods

We utilized data from three outcomes research studies where the primary predictor variable was generated using NLP. For each of these studies, Monte Carlo simulations were applied to generate datasets simulating potential errors in NLP-derived variables. We subsequently fit the original regression models to these partially simulated datasets and compared the distribution of coefficient estimates to the original study results.

Results

Among the four models evaluated, the mean change in the point estimate of the relationship between the predictor variable and the outcome ranged from -21.9% to 4.12%. In three of the four models, significance of this relationship was not eliminated in a single of the 500 simulations, and in one model it was eliminated in 12% of simulations. Mean changes in the estimates for confounder variables ranged from 0.27% to 2.27% and significance of the relationship was eliminated between 0% and 9.25% of the time. No variables underwent a shift in the direction of its interpretation.

Discussion

Impact of simulated NLP errors on the results of epidemiologic studies was modest, with only small changes in effect estimates and no changes in the interpretation of the findings (direction and significance of association with the outcome) for either the NLP-generated variables or other variables in the models.

Conclusion

NLP errors are unlikely to affect the results of studies that use NLP as the source of data.

Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks

OBJECTIVE

We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions.

METHODS

The voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed.

RESULTS

The model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was  - 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy,  - 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were  - 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy,  - 1.11 ± 2.90%, 2.34 ± 2.03%, respectively.

CONCLUSION

Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation.

CLINICAL RELEVANCE STATEMENT

We proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations.

KEY POINTS

• We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation. • Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation. • By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters.

Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes

Cluster randomized trials (CRTs) refer to a popular class of experiments in which randomization is carried out at the group level. While methods have been developed for planning CRTs to study the average treatment effect, and more recently, to study the heterogeneous treatment effect, the development for the latter objective has currently been limited to a continuous outcome. Despite the prevalence of binary outcomes in CRTs, determining the necessary sample size and statistical power for detecting differential treatment effects in CRTs with a binary outcome remain unclear. To address this methodological gap, we develop sample size procedures for testing treatment effect heterogeneity in two-level CRTs under a generalized linear mixed model. Closed-form sample size expressions are derived for a binary effect modifier, and in addition, a computationally efficient Monte Carlo approach is developed for a continuous effect modifier. Extensions to multiple effect modifiers are also discussed. We conduct simulations to examine the accuracy of the proposed sample size methods. We present several numerical illustrations to elucidate features of the proposed formulas and to compare our method to the approximate sample size calculation under a linear mixed model. Finally, we use data from the Strategies and Opportunities to Stop Colon Cancer in Priority Populations (STOP CRC) CRT to illustrate the proposed sample size procedure for testing treatment effect heterogeneity.

Evolution Mechanism of Sputtered Film Uniformity with the Erosion Groove Size: Integrated Simulation and Experiment

In this work, Cu thin films were experimentally fabricated at different target-substrate distances by 2-inch and 4-inch circular planar magnetron targets. Meanwhile, the sputtering deposition of Cu thin films was investigated via an integrated multiscale simulation, where the magnetron sputtering discharge was modeled using the Monte Carlo (MC) method, and the sputtered particle transport was simulated using a coupled Monte Carlo (MC) and molecular dynamics (MD) method. Experimental results indicated that, as the target-substrate distance increased from 30 to 120 mm, the film thickness distribution of the 2-inch target sputtering changed from a bell-shaped curve to a line-shaped curve, while that of the 4-inch target sputtering varied from a saddle-shaped curve to a line-shaped curve. The simulation results were accordant with the experimental results. The simulation results revealed that, at a target-substrate distance of 30 mm, the sputtering particle flow from the 2-inch target overlapped strongly near the substrate center, leading to a bell-shaped film thickness distribution, while the increased diameter of the erosion groove on the 4-inch target reduced the superposition effect of the sputtering particle flow near the substrate center, resulting in a saddle-shaped film thickness distribution. In addition, when the target-substrate distance ranged from 30 to 120 mm, the film thickness uniformity of 4-inch target sputtering was superior to that of 2-inch target sputtering, and the underlying mechanism was discussed in detail.

The System of Self-Consistent Models: The Case of Henry's Law Constants

Data on Henry's law constants make it possible to systematize geochemical conditions affecting atmosphere status and consequently triggering climate changes. The constants of Henry's law are desired for assessing the processes related to atmospheric contaminations caused by pollutants. The most important are those that are capable of long-term movements over long distances. This ability is closely related to the values of Henry's law constants. Chemical changes in gaseous mixtures affect the fate of atmospheric pollutants and ecology, climate, and human health. Since the number of organic compounds present in the atmosphere is extremely large, it is desirable to develop models suitable for predictions for the large pool of organic molecules that may be present in the atmosphere. Here, we report the development of such a model for Henry's law constants predictions of 29,439 compounds using the CORAL software (2023). The statistical quality of the model is characterized by the value of the coefficient of determination for the training and validation sets of about 0.81 (on average).

Structural Uncertainty Analysis of High-Temperature Strain Gauge Based on Monte Carlo Stochastic Finite Element Method

The high-temperature strain gauge is a sensor for strain measurement in high-temperature environments. The measurement results often have a certain divergence, so the uncertainty of the high-temperature strain gauge system is analyzed theoretically. Firstly, in the conducted research, a deterministic finite element analysis of the temperature field of the strain gauge is carried out using MATLAB software. Then, the primary sub-model method is used to model the system; an equivalent thermal load and force are loaded onto the model. The thermal response of the grid wire is calculated by the finite element method (FEM). Thermal-mechanical coupling analysis is carried out by ANSYS, and the MATLAB program is verified. Finally, the stochastic finite element method (SFEM) combined with the Monte Carlo method (MCM) is used to analyze the effects of the physical parameters, geometric parameters, and load uncertainties on the thermal response of the grid wire. The results show that the difference of temperature and strain calculated by ANSYS and MATLAB is 1.34% and 0.64%, respectively. The calculation program is accurate and effective. The primary sub-model method is suitable for the finite element modeling of strain gauge systems, and the number of elements is reduced effectively. The stochastic uncertainty analysis of the thermal response on the grid wire of a high-temperature strain gauge provides a theoretical basis for the dispersion of the measurement results of the strain gauge.

Hierarchical Structuring of Black Silicon Wafers by Ion-Flow-Stimulated Roughening Transition: Fundamentals and Applications for Photovoltaics

Ion-flow-stimulated roughening transition is a phenomenon that may prove useful in the hierarchical structuring of nanostructures. In this work, we have investigated theoretically and experimentally the surface texturing of single-crystal and multi-crystalline silicon wafers irradiated using ion-beam flows. In contrast to previous studies, ions had relatively low energies, whereas flow densities were high enough to induce a quasi-liquid state in the upper silicon layers. The resulting surface modifications reduced the wafer light reflectance to values characteristic of black silicon, widely used in solar energetics. Features of nanostructures on different faces of silicon single crystals were studied numerically based on the mesoscopic Monte Carlo model. We established that the formation of nano-pyramids, ridges, and twisting dune-like structures is due to the stimulated roughening transition effect. The aforementioned variety of modified surface morphologies arises due to the fact that the effects of stimulated surface diffusion of atoms and re-deposition of free atoms on the wafer surface from the near-surface region are manifested to different degrees on different Si faces. It is these two factors that determine the selection of the allowable "trajectories" (evolution paths) of the thermodynamic system along which its Helmholtz free energy, F, decreases, concomitant with an increase in the surface area of the wafer and the corresponding changes in its internal energy, U (dU>0), and entropy, S (dS>0), so that dF=dU - TdS<0, where T is the absolute temperature. The basic theoretical concepts developed were confirmed in experimental studies, the results of which showed that our method could produce, abundantly, black silicon wafers in an environmentally friendly manner compared to traditional chemical etching.

Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity

The minimal inhibitory concentrations (pMIC) are a valuable measure of the biological activity of polypeptides. Numerical data on the pMIC are necessary to systematize knowledge on polypeptides' biochemical behaviour. The model of negative decimal logarithm of pMIC of polypeptides in the form of a mathematical function of a sequence of amino acids is suggested. The suggested model is based on the so-called correlation weights of amino acids together with the correlation weights of fragments of local symmetry (FLS). Three kinds of the FLS are considered: (i) three-symbol fragments '…xyx…', (ii) four-symbol fragments '…xyyx…', and (iii) five-symbol fragments '…xyzyx…'. The models built using the Monte Carlo technique improved by applying the index of ideality of correlation (IIC) and correlation intensity index (CII).

In silico prediction of the mutagenicity of nitroaromatic compounds using correlation weights of fragments of local symmetry

Most quantitative structure-property/activity relationships (QSPRs/QSARs) techniques involve using different programs separately for generating molecular descriptors and separately for building models based on available descriptors. Here, the capabilities of the CORAL program are evaluated. A user of the program should apply as the basis for models the representation of the molecular structure by means of the simplified molecular input-line entry system (SMILES) as well as experimental data on the endpoint of interest. The local symmetry of SMILES is a novel composition of symmetrically represented symbols, which are three 'xyx', four 'xyyx', or five symbols 'xyzyx'. We updated our CORAL software using this optimal, new flexible descriptor, sensitive to the symmetric composition of a specific part of the molecule. Computational experiments have shown that taking account of these attributes of SMILES can improve the predictive potential of models for the mutagenicity of nitroaromatic compounds. In addition, the above computational experiments have confirmed the advantage of using the index of ideality of correlation (IIC) and the correlation intensity index (CII) for Monte Carlo optimization of the correlation weights for various attributes of SMILES, including the local symmetry. The average value of the coefficient of determination for the validation set (five different models) without fragments of local symmetry is 0.8589 ± 0.025, whereas using fragments of local symmetry improves this criterion of the predictive potential up to 0.9055 ± 0.010.

Uncertainty Evaluation for the Quantification of Urinary Amphetamine and 4-Hydroxyamphetamine Using Liquid Chromatography-Tandem Mass Spectrometry: Comparison of the Guide to the Expression of Uncertainty in Measurement Approach and the Monte Carlo Method with R

Estimating the measurement uncertainty (MU) is becoming increasingly mandatory in analytical toxicology. This study evaluates the uncertainty in the quantitative determination of urinary amphetamine (AP) and 4-hydroxyamphetamine (4HA) using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method based on the dilute-and-shoot approach. Urine sample dilution, preparation of calibrators, calibration curve, and method repeatability were identified as the sources of uncertainty. To evaluate the MU, the Guide to the Expression of Uncertainty in Measurement (GUM) approach and the Monte Carlo method (MCM) were compared using the R programming language. The MCM afforded a smaller coverage interval for both AP (94.83, 104.74) and 4HA (10.52, 12.14) than that produced by the GUM (AP (92.06, 107.41) and 4HA (10.21, 12.45)). The GUM approach offers an underestimated coverage interval for Type A evaluation, whereas the MCM provides an exact coverage interval under an abnormal probability distribution of the measurand. The MCM is useful in complex settings where the measurand is combined with numerous distributions because it is generated from the uncertainties of input quantities based on the propagation of the distribution. Therefore, the MCM is more practical than the GUM for evaluating the MU of urinary AP and 4HA concentrations using LC-MS/MS.

Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning

Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys with a low Young's modulus, and without the presence of cytotoxic alloying elements. Machine learning was used with aim to analyze biocompatible titanium alloys and predict the composition of Ti alloys with a low Young's modulus. A database was created using experimental data for alloy composition, Young's modulus, and mechanical and thermal properties of biocompatible titanium alloys. The Extra Tree Regression model was built to predict the Young's modulus of titanium alloys. By processing data of 246 alloys, the specific heat was discovered to be the most influential parameter that contributes to the lowering of the Young's modulus of titanium alloys. Further, the Monte Carlo method was used to predict the composition of future alloys with the desired properties. Simulation results of ten million samples, with predefined conditions for obtaining titanium alloys with a Young's modulus lower than 70 GPa, show that it is possible to obtain several multicomponent alloys, consisting of five main elements: titanium, zirconium, tin, manganese and niobium.

Using the Correlation Intensity Index to Build a Model of Cardiotoxicity of Piperidine Derivatives

The assessment of cardiotoxicity is a persistent problem in medicinal chemistry. Quantitative structure-activity relationships (QSAR) are one possible way to build up models for cardiotoxicity. Here, we describe the results obtained with the Monte Carlo technique to develop hybrid optimal descriptors correlated with cardiotoxicity. The predictive potential of the cardiotoxicity models (pIC50, Ki in nM) of piperidine derivatives obtained using this approach provided quite good determination coefficients for the external validation set, in the range of 0.90-0.94. The results were best when applying the so-called correlation intensity index, which improves the predictive potential of a model.

In vitro Antimicrobial Activity and Dose Optimization of Eravacycline and Other Tetracycline Derivatives Against Levofloxacin-Non-Susceptible and/or Trimethoprim-Sulfamethoxazole-Resistant Stenotrophomonas maltophilia

Purpose

To better guide clinical use, we determined the in vitro antimicrobial activity of the new drug eravacycline and other tetracycline derivatives against levofloxacin (LVFX)-non-susceptible and/or trimethoprim-sulfamethoxazole (TMP-SMZ)-resistant Stenotrophomonas maltophilia and evaluated their dosing regimens.

Methods

Seventy-seven unique strains of S. maltophilia were isolated from sputa samples and airway aspirate samples that were either LVFX-non-susceptible and/or TMP-SMZ-resistant. Monte Carlo simulations were performed for different dosing regimens according to the population pharmacokinetic parameters of antibiotics in patients with respiratory tract infections at the minimum inhibitory concentration (MIC).

Results

Eravacycline had excellent in vitro antibacterial activity against LVFX-non-susceptible and/or TMP-SMZ-resistant S. maltophilia. Monte Carlo simulations showed that for LVFX-non-susceptible strains, the cumulative fraction of response (CFR) of minocycline at the conventional recommended dose of 100 mg q12 h was 90.90%; for TMP-SMZ-resistant strains, the CFR of minocycline at a high dose of 200 mg q12 h was only 91.64%. For strains resistant to both LVFX and TMP-SMZ, the CFR of minocycline at a high dose of 200 mg q12 h was 89.81%. In contrast, the CFR of tigecycline was less than 40%, even at a dose of 100 mg q12 h.

Conclusion

For pneumonia, minocycline is better for S. maltophilia that is non-susceptible to LVFX; for TMP-SMZ-resistant strains and strains that are not susceptible to either LVFX or TMP-SMZ, the efficiency of eravacycline requires further evaluation. Eravacycline may be a better choice for extremely resistant S. maltophilia strains that are non-susceptible to LVFX, TMP-SMZ, and minocycline.

Reputation and reciprocity

Reputation and reciprocity are key mechanisms for cooperation in human societies, often going hand in hand to favor prosocial behavior over selfish actions. Here we review recent researches at the interface of physics and evolutionary game theory that explored these two mechanisms. We focus on image scoring as the bearer of reputation, as well as on various types of reciprocity, including direct, indirect, and network reciprocity. We review different definitions of reputation and reciprocity dynamics, and we show how these affect the evolution of cooperation in social dilemmas. We consider first-order, second-order, as well as higher-order models in well-mixed and structured populations, and we review experimental works that support and inform the results of mathematical modeling and simulations. We also provide a synthesis of the reviewed researches along with an outlook in terms of six directions that seem particularly promising to explore in the future.

The system of self-consistent models for pesticide toxicity to Daphnia magna

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool of modern theoretical and computational chemistry. The self-consistent model system is both a method to build up a group of QSPR/QSAR models and an approach to checking the reliability of these models. Here, a group of models of pesticide toxicity toward Daphnia magna for different distributions into training and test sub-sets is compared. This comparison is the basis for formulating the system of self-consistent models. The so-called index of the ideality of correlation (IIC) has been used to improve the above models' predictive potential of pesticide toxicity. The predictive potential of the suggested models should be classified as high since the average value of the determination coefficient for the validation sets is 0.841, and the dispersion is 0.033 (on all five models). The best model (number 4) has an average determination coefficient of 0.89 for the external validation sets (related to all five splits).

Quantitative Measurement of Thermal Conductivity by SThM Technique: Measurements, Calibration Protocols and Uncertainty Evaluation

Thermal management is a key issue for the downsizing of electronic components in order to optimise their performance. These devices incorporate more and more nanostructured materials, such as thin films or nanowires, requiring measurement techniques suitable to characterise thermal properties at the nanoscale, such as Scanning Thermal Microscopy (SThM). In active mode, a hot thermoresistive probe scans the sample surface, and its electrical resistance R changes as a function of heat transfers between the probe and sample. This paper presents the measurement and calibration protocols developed to perform quantitative and traceable measurements of thermal conductivity k using the SThM technique, provided that the heat transfer conditions between calibration and measurement are identical, i.e., diffusive thermal regime for this study. Calibration samples with a known k measured at the macroscale are used to establish the calibration curve linking the variation of R to k. A complete assessment of uncertainty (influencing factors and computational techniques) is detailed for both the calibration parameters and the estimated k value. Outcome analysis shows that quantitative measurements of thermal conductivity with SThM (with an uncertainty value of 10%) are limited to materials with low thermal conductivity (k<10Wm-1K-1).

Structure of Strongly Adsorbed Polymer Systems: A Computer Simulation Study

The structure of very thin polymer films formed by strongly adsorbed macromolecules was studied by computer simulation. A coarse-grained model of strictly two-dimensional polymer systems was built, and its properties determined by an efficient Monte Carlo simulation algorithm. Properties of the model system were determined by means of Monte Carlo simulations with a sampling algorithm that combines Verdier-Stockmayer, pivot and reputation moves. The effects of temperature, chain length and polymer concentration on the macromolecular structure were investigated. It was shown that at low temperatures, the chain size increases with the concentration, that is, inversely with high temperatures. This behavior should be explained by the influence of inter-chain interactions.

Adjusting for hidden biases in sexual behaviour data: a mechanistic approach

Background

Two required inputs to mathematical models of sexually transmitted infections are the average duration in epidemiological risk states (e.g., selling sex) and the average rates of sexual partnership change. These variables are often only available as aggregate estimates from published cross-sectional studies, and may be subject to distributional, sampling, censoring, and measurement biases.

Methods

We explore adjustments for these biases using aggregate estimates of duration in sex work and numbers of reported sexual partners from a published 2011 survey of female sex worker in Eswatini. We develop adjustments from first principles, and construct Bayesian hierarchical models to reflect our mechanistic assumptions about the bias-generating processes.

Results

We show that different mechanisms of bias for duration in sex work may "cancel out" by acting in opposite directions, but that failure to consider some mechanisms could over- or underestimate duration in sex work by factors approaching 2. We also show that conventional interpretations of sexual partner numbers are biased due to implicit assumptions about partnership duration, but that unbiased estimators of partnership change rate can be defined that explicitly incorporate a given partnership duration. We highlight how the unbiased estimator is most important when the survey recall period and partnership duration are similar in length.

Conclusions

While we explore these bias adjustments using a particular dataset, and in the context of deriving inputs for mathematical modelling, we expect that our approach and insights would be applicable to other datasets and motivations for quantifying sexual behaviour data.

Microdosimetric assessment about proton spread-out Bragg peak at different depths based on the normal human mesh-type cell population model

Objective.In clinical proton therapy, the spread-out Bragg peak (SOBP) is commonly used to fit the target shape. Dose depositions at microscopic sites vary, even with a consistent absorbed dose (D) in SOBP. In the present study, monolayer mesh-type cell population models were developed for microdosimetric assessment at different SOBP depths.Approach.Normal human bronchial epithelial (BEAS-2B) and hepatocytes (L-O2) mesh-type cell models were constructed based on fluorescence tomography images of normal human cells. Particle transport simulation in cell populations was performed coupled with Monte Carlo software PHITS. The relationship between microdosimetry and macrodosimetry of SOBP at different depths was described by analyzing the microdosimetric indicators such as specific energyz,specific energy distributionfz,D,and relative standard deviationσz/z¯within cells. Additionally, the microdosimetric distributions characteristics and their contributing factors were also discussed.Main results.The microscopic dose distribution is strongly influenced by cellular size, shape, and material. The mean specific energyz¯of nucleus and cytoplasm in the cell population is greater than the overall absorbed dose of the cell population model (Dp), with a maximumz¯/Dpof 1.1. The cellular dose distribution is different between the BEAS-2B mesh-type model and its concentric ellipsoid geometry-type model, which difference inz¯is about 10.3% for the nucleus and about 7.5% for the cytoplasm with the SOBP depth of 15 cm. WhenD= 2 Gy, the maximumzof L-O2 nucleus reaches 2.8 Gy andσz/z¯is 5.1% at the mid-depth SOBP (16-18 cm); while the maximumzof the BEAS-2B nucleus reaches 2.2 Gy with only 2.7% ofσz/z¯.Significance.The significant variation of microdosimetric distributions of SOBP different depths indicates the necessity to use mesh-type cell population models, which have the potential to be compared with biological results and build the bio-physical model.

Comparison of Bayesian Spatiotemporal Models for Small-Area Life Expectancy: A Simulation Study

The purpose of this study was to assess the precision, uncertainty, and normality of small-area life expectancy estimates calculated using Bayesian spatiotemporal models. We hypothesized 6 scenarios in which all 247 districts of South Korea had the same year-specific female population of 500, 1,000, 2,000, 5,000, 10,000, and 25,000 individuals during the study period (2013-2017). We generated 1,000 hypothetical data sets for each scenario and calculated district-year life expectancies. The precision and uncertainty of life expectancy estimates were compared between 2 Bayesian spatiotemporal models and the traditional method and Bayesian spatial models. We examined the normality of the life expectancy distributions generated by each method and investigated an optimal cutoff value for the comparisons. The Bayesian spatiotemporal models produced precise life expectancy estimates. However, the 95% uncertainty interval contained the true value with a probability of less than 95%. The Bayesian spatiotemporal models violated the normality assumption in scenarios with small population sizes. Therefore, life expectancy comparisons should be conducted using a cutoff value that minimizes false-positive and false-negative rates. We propose 0.8 as a cutoff value for determining the statistical significance of the difference in life expectancy.

Towards a Systemic Concept of the Brain Ishemia Stroke: Monte Carlo Driven in Silico Model

BACKGROUND

The work proposes a new mathematical model of dynamic processes of a typical spatially heterogeneous biological system, and sets and solves a mathematical problem of modeling the dynamics of the system of neurovascular units of the brain in conditions of ischemic stroke. There is a description of only a small number of mathematical models of stroke in the literature. This model is being studied and a numerical and software implementation of the corresponding mathematical problem is proposed.

METHODS

This work is the first attempt ever aiming to employ a Monte Carlo computational approach for In Silico simulation of the most critical parameters in molecular and cellular pathogenesis of the brain ischemic stroke. In this work, a new mathematical model of the development of ischemic stroke is proposed in the form of a discrete model based on neurovascular units (NVU) as elements.

RESULTS

As a result of testing the program with the assignment of empirically selected coefficients, data were obtained on the evolution of the states of the lattice of the cellular automaton of the model for the spread of stroke in a region of the brain tissue. A resulting new theoretical model of the particular pathologically altered biosystem might be taken as a promising tool for further studies in neurology; general pathology and cell biology.

CONCLUSION

For the first time, a mathematical model has been constructed that allows us to represent the spatial dynamics of the development of the affected area in ischemic stroke of the brain, taking into account neurovascular units as single morphofunctional structures.

The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds

CONTEXT

To apply the quantitative relationships "structure-endpoint" approach, the reliability of prediction is necessary but sometimes challenging to achieve. In this work, an attempt is made to accomplish the reliability of forecasts by creating a set of random partitions of data into training and validation sets, followed by constructing random models. A system of random models for a helpful approach should be self-consistent, giving a similar or at least comparable statistical quality of the predictions for models obtained using different splits of available data into training and validation sets.

METHOD

The carried out computer experiments aimed at obtaining blood-brain barrier permeation models showed that, in principle, can be used such an approach (the Monte Carlo optimization of the correlation weights for different molecular features) for the above purpose taking advantage of specific algorithms to optimize the modelling steps with applying of new statistical criteria such as the index of ideality of correlation (IIC) and the correlation intensity index (CII). The results so obtained are good and better than what was reported previously. The suggested approach to validation of models is non-identic to traditionally applied manners of the checking up models. The concept of validation can be used for arbitrary models (not only for models of the blood-brain barrier).

Dependence of Induced Biological Damage on the Energy Distribution and Intensity of Clinical Intra-Operative Radiotherapy Electron Beams

The survival fraction of epithelial HaCaT cells was analysed to assess the biological damage caused by intraoperative radiotherapy electron beams with varying energy spectra and intensities. These conditions were achieved by irradiating the cells at different depths in water using nominal 6 MeV electron beams while consistently delivering a dose of 5 Gy to the cell layer. Furthermore, a Monte Carlo simulation of the entire irradiation procedure was performed to evaluate the molecular damage in terms of molecular dissociations induced by the radiation. A significant agreement was found between the molecular damage predicted by the simulation and the damage derived from the analysis of the survival fraction. In both cases, a linear relationship was evident, indicating a clear tendency for increased damage as the averaged incident electron energy and intensity decreased for a constant absorbed dose, lowering the dose rate. This trend suggests that the radiation may have a more pronounced impact on surrounding healthy tissues than initially anticipated. However, it is crucial to conduct additional experiments with different target geometries to confirm this tendency and quantify the extent of this effect.

Health risk assessment of exposure to triclosan in pregnant women using Monte Carlo simulation techniques: based on biomonitoring data

This study aimed to assess the triclosan (TCS) health risk in an Iranian pregnant women sample by Monte Carlo simulation (MCS). The urinary TCS of 99 women after the 28th week of pregnancy was detected by gas chromatography/mass spectrometry detector (GC/MS), and the MCS model implemented a health risk assessment. The corresponding hazard quotient (HQ) and the sensitivity analysis were calculated. TCS was measured in 100% of the urine samples with a median concentration of 2.89 µg/L. The median of HQ was obtained at 1.93 × 10-4. The TCS exposure risk in the studied population was lower than the allowable limit. A comparison between HQ values in the two weight subgroups of pregnant women showed that the risk level is almost equal, and there was minimal health risk in pregnant women from exposure to TCS.

Optical Properties and Interference Effects of the Lens Mitochondrion

The lens mitochondrion of the tree shrew, located along the optical pathway between the lens and photoreceptors, has been investigated. The results suggest that the lens mitochondrion acts as a quasi-bandgap or imperfect photonic crystal. Interference effects cause a shift in the focus and introduce wavelength-dependent behavior similar to dispersion. Optical channels within the mitochondrion form a mild waveguide, preferentially propagating light within certain compartments. The lens mitochondrion also functions as an imperfect UV-shielding interference filter. Overall, this study provides insights into the dual role of the lens mitochondrion and the complex behavior of light within biological systems.

Shielding design aspects of SR in 3 GeV ILSF

Iranian Light Source Facility (ILSF) is an under-construction synchrotron radiation accelerator consisting of a 150 MeV linac, a booster synchrotron operating from 150 MeV to 3 GeV, and a 3 GeV storage ring that stores a maximum of 400 mA current of electrons. As the stored beam circulates, a fraction of the beam is lost due to interactions with gas molecules, interactions among beam particles, and orbital bending, which produce radiation. The bulk shielding calculation for the ILSF and the input parameters used for this analysis are discussed in this paper. The potential of skyshine neutrons to cause radiation hazards is investigated as well. Moreover, the design and shielding simulation using the FLUKA Monte Carlo code is presented for the linac beam stop and primary and scattered gas bremsstrahlung for the first optics enclosure of the ILSF spectro microscopy beamline. Our designed radiation shielding system guarantees that the annual dose in all areas around the ILSF machine does not exceed the dose limit of 1 mSv.

Development of Self-Consistency Models of Anticancer Activity of Nanoparticles under Different Experimental Conditions Using Quasi-SMILES Approach

Algorithms of the simulation of the anticancer activity of nanoparticles under different experimental conditions toward cell lines A549 (lung cancer), THP-1 (leukemia), MCF-7 (breast cancer), Caco2 (cervical cancer), and hepG2 (hepatoma) have been developed using the quasi-SMILES approach. This approach is suggested as an efficient tool for the quantitative structure-property-activity relationships (QSPRs/QSARs) analysis of the above nanoparticles. The studied model is built up using the so-called vector of ideality of correlation. The components of this vector include the index of ideality of correlation (IIC) and the correlation intensity index (CII). The epistemological component of this study is the development of methods of registration, storage, and effective use of experimental situations that are comfortable for the researcher-experimentalist in order to be able to control the physicochemical and biochemical consequences of using nanomaterials. The proposed approach differs from the traditional models based on QSPR/QSAR in the following respects: (i) not molecules but experimental situations available in a database are considered; in other words, an answer is offered to the question of how to change the plot of the experiment in order to achieve the desired values of the endpoint being studied; and (ii) the user has the ability to select a list of controlled conditions available in the database that can affect the endpoint and evaluate how significant the influence of the selected controlled experimental conditions is.

NEMA NU 1-2018 performance characterization and Monte Carlo model validation of the Cubresa Spark SiPM-based preclinical SPECT scanner

BACKGROUND

The Cubresa Spark is a novel benchtop silicon-photomultiplier (SiPM)-based preclinical SPECT system. SiPMs in SPECT significantly improve resolution and reduce detector size compared to preclinical cameras with photomultiplier tubes requiring highly magnifying collimators. The NEMA NU 1 Standard for Performance Measurements of Gamma Cameras provides methods that can be readily applied or extended to characterize preclinical cameras with minor modifications. The primary objective of this study is to characterize the Spark according to the NEMA NU 1-2018 standard to gain insight into its nuclear medicine imaging capabilities. The secondary objective is to validate a GATE Monte Carlo simulation model of the Spark for use in preclinical SPECT studies.

METHODS

NEMA NU 1-2018 guidelines were applied to characterize the Spark's intrinsic, system, and tomographic performance with single- and multi-pinhole collimators. Phantoms were fabricated according to NEMA specifications with deviations involving high-resolution modifications. GATE was utilized to model the detector head with the single-pinhole collimator, and NEMA measurements were employed to tune and validate the model. Single-pinhole and multi-pinhole SPECT data were reconstructed with the Software for Tomographic Image Reconstruction and HiSPECT, respectively.

RESULTS

The limiting intrinsic resolution was measured as 0.85 mm owing to a high-resolution SiPM array combined with a 3 mm-thick scintillation crystal. The average limiting tomographic resolution was 1.37 mm and 1.19 mm for the single- and multi-pinhole collimators, respectively, which have magnification factors near unity at the center of rotation. The maximum observed count rate was 15,400 cps, and planar sensitivities of 34 cps/MBq and 150 cps/MBq were measured at the center of rotation for the single- and multi-pinhole collimators, respectively. All simulated tests agreed well with measurement, where the most considerable deviations were below 7%.

CONCLUSIONS

NEMA NU 1-2018 standards determined that a SiPM detector mitigates the need for highly magnifying pinhole collimators while preserving detailed information in projection images. Measured and simulated NEMA results were highly comparable with differences on the order of a few percent, confirming simulation accuracy and validating the GATE model. Of the collimators initially provided with the Spark, the multi-pinhole collimator offers high resolution and sensitivity for organ-specific imaging of small animals, and the single-pinhole collimator enables high-resolution whole-body imaging of small animals.

Optimized Monte Carlo simulations for voxel-based internal dosimetry

OBJECTIVE

The scientific community has considered internal dosimetry by the Monte Carlo method the gold standard. However, there is a trade-off between simulation processing time and the statistical quality of the results that makes it a challenge to obtain accurate absorbed dose values in some situations, such as dose estimation in organs affected by cross-irradiation or limited computing power. Variance reduction techniques are used to reduce computational processing time without impairing the statistical quality of the results, such as tracking energy cutoff, secondary particle production threshold, and parallelism of different types of emissions from radionuclides.

APPROACH

In this work, GATE Monte Carlo code and its variance reduction techniques were evaluated to calculate S values of organs from the International Commission on Radiological Protection (ICRP) report 110 male phantom for the lutetium-177, iodine-131, yttrium-90, and radium-223 radionuclides. The results are compared with the data from the OpenDose collaboration.

MAIN RESULTS

A cutoff of 5 MeV for local electron deposition and 2.0 mm of secondary particle production range resulted in a computational efficiency increase of 7.9 and 1.05 times, respectively. Simulation of ICRP 107 spectra-based source proved to be about 5 times more efficient when compared to a decay simulation using \verb|G4RadioactiveDecay| (Geant4-based radioactive decay processes). TLE (Track Length Estimator) and seTLE (Split Exponential Track Length Estimator) techniques were used to calculate the absorbed dose of photon emissions, resulting in computational efficiency up to 29.4 and 62.5 times higher when compared to traditional simulations, respectively. In particular, the seTLE technique accelerates the simulation time by up to 1426 times, achieving a statistical uncertainty of 10\% in volumes affected by cross-irradiation.

SIGNIFICANCE

The variance reduction techniques used in this work drastically reduced the simulation time and maintained the statistical quality of the calculated absorbed dose values, proving the feasibility of the use of the Monte Carlo method in internal dosimetry under challenging situations and making it viable for clinical routine or web applications.

The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity

Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the active (1)-inactive (0) format. The so-called system of self-consistent models has been suggested as an approach for two tasks: (i) building up a model and (ii) estimating its predictive potential. However, this approach has been tested so far for regression models. Here, the approach is applied to building up and estimating a categorical hepatotoxicity model using the CORAL software. This new process yields good results: sensitivity = 0.77, specificity = 0.75, accuracy = 0.76, and Matthew correlation coefficient = 0.51 (all compounds) and sensitivity = 0.83, specificity = 0.81, accuracy = 0.83 and Matthew correlation coefficient = 0.63 (validation set).

Development and evaluation of dose calculation algorithm with a combination of Monte Carlo and point-kernel methods for boron neutron capture therapy

We developed a 'hybrid algorithm' that combines the Monte Carlo (MC) and point-kernel methods for fast dose calculation in boron neutron capture therapy. The objectives of this study were to experimentally verify the hybrid algorithm and to verify the calculation accuracy and time of a 'complementary approach' adopting both the hybrid algorithm and the full-energy MC method. In the latter verification, the results were compared with those obtained using the full-energy MC method alone. In the hybrid algorithm, the moderation process of neutrons is simulated using only the MC method, and the thermalization process is modeled as a kernel. The thermal neutron fluxes calculated using only this algorithm were compared with those measured in a cubic phantom. In addition, a complementary approach was used for dose calculation in a geometry simulating the head region, and its computation time and accuracy were verified. The experimental verification indicated that the thermal neutron fluxes calculated using only the hybrid algorithm reproduced the measured values at depths exceeding a few centimeters, whereas they overestimated those at shallower depths. Compared with the calculation using only the full-energy MC method, the complementary approach reduced the computation time by approximately half, maintaining nearly same accuracy. When focusing on the calculation only using the hybrid algorithm only for the boron dose attributed to the reaction of thermal neutrons, the computation time was expected to reduce by 95% compared with the calculation using only the full-energy MC method. In conclusion, modeling the thermalization process as a kernel was effective for reducing the computation time.

Monte Carlo modelling and validation of the elekta synergy medical linear accelerator equipped with radiosurgical cones

Monte Carlo simulations of medical linear accelerator heads help in visualizing the energy spectrum and angular spread of photons and electrons, energy deposition, and scattering from each of the head components. Hence, the purpose of this study was to validate the Monte Carlo model of the Elekta synergy medical linear accelerator equipped with stereotactic radio surgical connical collimators. For this, the Elekta synergy medical linear accelerator was modelled using the EGSnrc Monte Carlo code. The model results were validated using the measured data. The primary electron beam parameters, beam size, and energy were tuned to match the measured data; a dose profile with a field size of 40 × 40 cm² and percentage depth dose with a field size of 10 × 10 cm² were matched during tuning. The validation of the modelled data with the measurement results was performed using gamma analysis, point dose, and field size comparisons. For small radiation fields, relative output factors were also compared. The gamma analysis revealed good agreement between the Monte Carlo modeling results and the measured data. A gamma pass rate of more than 95% was obtained for field sizes of 40 × 40 cm² to 2 × 2 cm² with gamma criteria of 1% and 1 mm for the dose difference (DD) and distance to agreement (DTA), respectively; this gamma pass rate was more than 98% for the corresponding values of 2% and 2 mm for the DD and DTA, respectively. A gamma pass rate of more than 99% was obtained for a percentage depth dose with 1 mm and 1% criteria. The field size was also in good agreement with the measurement results, and the maximum deviation observed was 1.1%. The stereotactic cone field also passed this analysis with a gamma pass rate of more than 98% for dose profiles and 99% for the percentage depth dose. The small field output factor exhibited a deviation of 4.3%, 3.4%, and 1.9% for field sizes of 5 mm, 7.5 mm, and 10 mm, respectively. Thus, the Monte Carlo model of the Elekta Linear accelerator was successfully validated. The validation of radio surgical cones passed the analysis in terms of the dose profiles and percentage depth dose. The small field relative output factors exhibited deviations of up to 4.3%, and to resolve this, detector-specific and field-specific correction factors must be derived.

CORAL Models for Drug-Induced Nephrotoxicity

Drug-induced nephrotoxicity is a major cause of kidney dysfunction with potentially fatal consequences. The poor prediction of clinical responses based on preclinical research hampers the development of new pharmaceuticals. This emphasises the need for new methods for earlier and more accurate diagnosis to avoid drug-induced kidney injuries. Computational predictions of drug-induced nephrotoxicity are an attractive approach to facilitate such an assessment and such models could serve as robust and reliable replacements for animal testing. To provide the chemical information for computational prediction, we used the convenient and common SMILES format. We examined several versions of so-called optimal SMILES-based descriptors. We obtained the highest statistical values, considering the specificity, sensitivity and accuracy of the prediction, by applying recently suggested atoms pairs proportions vectors and the index of ideality of correlation, which is a special statistical measure of the predictive potential. Implementation of this tool in the drug development process might lead to safer drugs in the future.

A Monte Carlo study on dose distribution of an orthovoltage radiation therapy system

It is important to plan radiotherapy treatment and establish optimal dose distribution to reduce the chances of side effects and injury. Because there are no commercially available tools for calculating dose distribution in orthovoltage radiotherapy in companion animals, we developed an algorithm to accomplish this and verified its characteristics using tumor disease cases. First, we used the Monte Carlo method to develop an algorithm to calculate the dose distribution of orthovoltage radiotherapy (280 kVp; MBR-320, Hitachi Medical Corporation, Tokyo, Japan) using BEAMnrc at our clinic. Using development of Monte Carlo method, dose distribution for tumor and normal organs were evaluated in brain tumors, squamous cell carcinomas of the head, and feline nasal lymphomas. In all cases of brain tumors, the mean dose delivered to the GTV ranged from 36.2 to 76.1% of the prescribed dose due to the decrease through the skull. In the nasal lymphoma in cats, the eyes with covered a 2 mm-thick lead plate, the respective average dose to the eyes was 71.8% and 89.9% less than that to the uncovered eyes. The findings may be useful for informed decision making in orthovoltage radiotherapy with more effective and targeted irradiation and data collection allowing detailed informed consent.

Shielding Capability Research on Composite Base Materials in Hybrid Neutron-Gamma Mixed Radiation Fields

The ¹⁶N monitoring system operates in a mixed neutron-gamma radiation field and is subject to high background radiation, thus triggering instability in the ¹⁶N monitoring system measurement data. Due to its property of actual physical process simulation, the Monte Carlo method was adopted to establish the model of the ¹⁶N monitoring system and design a structure-functionally integrated shield to realize neutron-gamma mixed radiation shielding. First, the optimal shielding layer with a thickness of 4 cm was determined in this working environment, which had a significant shielding effect on the background radiation and improved the measurement of the characteristic energy spectrum and the shielding effect on neutrons was better than gamma shielding with the increase in the shield thickness. Then, functional fillers such as B, Gd, W, and Pb were added to the matrix to compare the shielding rates of three matrix materials of polyethylene, epoxy resin, and 6061 aluminum alloy at 1 MeV neutron and gamma energy. The shielding performance of epoxy resin as the matrix material was better than that of the aluminum alloy and polyethylene, and the shielding rate of boron-containing epoxy resin was 44.8%. The γ-ray mass attenuation coefficients of lead and tungsten in the three matrix materials were simulated to determine the best material for the gamma shielding performance. Finally, the optimal materials for neutron shielding and gamma shielding were combined, and the shielding performance of single-layer shielding and double-layer shielding in mixed radiation field was compared. The optimal shielding material-boron-containing epoxy resin was determined as the shielding layer of the ¹⁶N monitoring system to realize the integration of structure and function, which provides a theoretical basis for the selection of shielding materials in a special working environment.

An assessment of the dose and image quality difference between AP and PA positioned adult radiographic knee examinations

INTRODUCTION

Knee X-rays are a standard examination to diagnose multiple conditions ranging from traumatic injuries, degeneration, and cancer. This study explores the differences between adult Anterior-Posterior (AP) and Posterior-Anterior (PA) weight-bearing knee examinations using absorbed radiation dose data and image quality.

METHODS

The study modelled and compared AP and PA knee X-ray radiation dose data using Monte-Carlo software, an Ion Chamber, and thermoluminescence dosemeters (TLDs) on a Rando phantom. Imaging parameters used were 66kVp, 4mAs, 100cm distance and 13 × 24cm collimation. The interval data analysis used a two-tailed t-test. The image quality of a sample of the AP and PA knee X-rays was assessed using Likert 5-point ordinal Image Quality Scoring (IQS) and the Wilcoxon matched pairs test.

RESULTS

Monte-Carlo modelling provided limited results; the Ion Chamber data for absorbed dose provided no variation between AP and PA positions but was similar to the AP TLD dose. The absorbed doses recorded with batches of TLDs demonstrated a 27.4% reduction (46.1μGy; p=0.01) in Skin Entrance Dose (ESD) and 9 - 58% dose reduction (1.6 - 16.4μGy; p=0.00-0.2) to the tissues and organs while maintaining diagnostic image quality (p=0.67).

CONCLUSION

The study has highlighted the various challenges of using different dosimetry approaches to measure absorbed radiation dose in extremity (knee) X-ray imaging. The Monte-Carlo simulated absorbed knee dose was overestimated, but the simulated body organ/tissue doses were lower than the actual TLD absorbed doses. The Ion Chamber absorbed doses did not differentiate between the positions. The TLD organ/tissue absorbed doses demonstrated a reduction in dose in the PA position compared to the AP position, without a detrimental effect on image quality. The study findings in laboratory conditions raise awareness of opportunities and potential to lower radiation dose, with further study replicated in a clinical site recommended.

An Integrated Approach of Learning Genetic Networks From Genome-Wide Gene Expression Data Using Gaussian Graphical Model and Monte Carlo Method

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets.

Development and optimization of stability-indicating method of ethinylestradiol, levonorgestrel, and their main impurities using quality by design approach

The association of Ethinylestradiol 0.03 mg and Levonorgestrel 0.15 mg is a hormonal contraceptive that combines estrogen and progestogen. According to a bibliographic survey, these combined drugs present at least 18 known degradation products, which are required to control the potential impurities harmful to human health. The high number of impurities and the low concentrations of the active pharmaceutical ingredients (APIs) and their respective degradation products increase the complexity of the stability-indicating method development for this medicine. Thus, this work aimed to develop and optimize the stability-indicating method using the quality by design (QbD) approach and in-silico tools for application in samples of oral contraceptives sold in Brazil. The analysis samples were initially subjected to a forced degradation study through 7 days of exposure under acid and alkali hydrolysis, oxidative condition, and oxidation by metal ions. In addition to the chemical exposure, the sample was subjected to physical stress through 10 days of exposure under dry heat, moisture, and photolytic degradation. These exposure samples were analyzed in the development and optimization of chromatographic conditions. As a result, the developed method was able to separate 20 known substances, including the two APIs and their respective 18 degradation products, as well as unknown degradation products obtained by the forced degradation study. Finally, this stability-indicating method was successfully applied for comparative analysis of contraceptive drugs marketed in Brazil, newly purchased and subjected to accelerated stability condition at 40 °C and 75% RH over the 6-month period.

Flexible stretchable low-energy X-ray (30-80 keV) radiation shielding material: Low-melting-point Ga(1)In(1)Sn(7)Bi(1) alloy/thermoplastic polyurethane composite

A highly flexible stretchable thermoplastic polyurethane (TPU) composite loaded with a low-melting-point Ga₁In₁Sn₇Bi₁ multiprincipal element alloy (LMPEA) was prepared, and its radiation shielding performance was evaluated. The fluid characteristic of LMPEA and the flexibility of TPU enable good interface compatibility. Ga₁In₁Sn₇Bi₁ LMPEA consists of two eutectic structures, and the liquid gallium-rich phases are distributed at the boundary of the InBi intermetallic compound and Sn solid solution. In the low-photon energy range of 30-80 keV, LMPEA has a theoretical specific lead equivalent of 0.803 mmPb/mm and a theoretical weight reduction of 17.27% compared with lead. To evaluate the photon attenuation capability for the LMPEA/TPU composites, the Phy-X procedure and Monte Carlo simulations were used to determine the shielding parameters, such as the mass attenuation coefficient, linear attenuation coefficient, half-value layer, tenth-value layer, mean free path, effective atomic number, and fast neutron removal cross section. The attenuation performance test of X-ray protective materials measured the actual lead equivalent. At the same thickness, the LMPEA/TPU composite (66.667, 50.000 wt% LMPEA loading) has a higher measured lead equivalent than the in-service medical shielding materials, which meets the lead equivalent requirements of X-ray protective clothing. LMPEA/TPU composites are nontoxic, lightweight, and have excellent low-energy X-ray shielding ability, offering great potential for application in medical wearable materials.