Characterization of accurate 3D collimator-detector response function for single- and multi-lofthole collimated SPECT cameras.

PURPOSE: Collimator-detector response function (CDRF) of a SPECT scanner refers to the image generated from a point source of activity. This research aims to characterize the CDRF of a breast-dedicated SPECT imager equipped with a lofthole collimator using GATE Monte Carlo simulation. MATERIALS AND METHODS: To do so, a cylindrical multi-lofthole collimation system with lofthole apertures dedicated to breast imaging was modeled using the GATE Monte Carlo simulator. The dependency of the CDRF on the source-to-collimator distance of a single-lofthole as well as 8-lofthole collimations was assessed and then compared. In addition, the 3D-sensitivity map of the 8-lofthole collimation was derived. Finally, fair comparisons were conducted between the response of the 8-lofthole collimator and that of an 8-pinhole and also existing analytical derivations. In all cases, a data acquisition period of 5.0 min with an in-air 99mTc point source was considered. RESULTS: For the single-lofthole collimator, 4.5 times increasing the magnification factor leads to a 16- and twofold improvement in the sensitivity and spatial resolution, respectively. In the single-lofthole collimator, the resolution and sensitivity are degraded as the source-to-aperture distance increases. For the cylindrical 8-lofthole collimator, the findings confirm that CDRF strongly depends on source-to-aperture distance and angle of photon incidence. For a 30 mm in-plane offset point, a 25% increase in sensitivity is observed compared to that of the center of the FOV. Increasing the angle from 0 ∘ to 34 ∘ results in a 50% reduction in sensitivity. Furthermore, the findings illustrate that spatial resolution follows a quadratic function as 10 - 3 d 2 + 2 × 10 - 4 d + R 0 where d is an offset along the x-, y-, and z-axis, and R0 is the spatial resolution at the center of the FOV. CONCLUSION: In conclusion, both spatial resolution and sensitivity of the lofthole collimation are considerably angle- and offset-dependent within the FOV of single- and multi-lofthole collimated SPECT imagers.

A unified framework to control estimation error in reinforcement learning.

In reinforcement learning, accurate estimation of the Q-value is crucial for acquiring an optimal policy. However, current successful Actor-Critic methods still suffer from underestimation bias. Additionally, there exists a significant estimation bias, regardless of the method used in the critic initialization phase. To address these challenges and reduce estimation errors, we propose CEILING, a simple and compatible framework that can be applied to any model-free Actor-Critic methods. The core idea of CEILING is to evaluate the superiority of different estimation methods by incorporating the true Q-value, calculated using Monte Carlo, during the training process. CEILING consists of two implementations: the Direct Picking Operation and the Exponential Softmax Weighting Operation. The first implementation selects the optimal method at each fixed step and applies it in subsequent interactions until the next selection. The other implementation utilizes a nonlinear weighting function that dynamically assigns larger weights to more accurate methods. Theoretically, we demonstrate that our methods provide a more accurate and stable Q-value estimation. Additionally, we analyze the upper bound of the estimation bias. Based on two implementations, we propose specific algorithms and their variants, and our methods achieve superior performance on several benchmark tasks.

Estimating Haplotype Structure and Frequencies: A Bayesian Approach to Unknown Design in Pooled Genomic Data.

The estimation of haplotype structure and frequencies provides crucial information about the composition of genomes. Techniques, such as single-individual haplotyping, aim to reconstruct individual haplotypes from diploid genome sequencing data. However, our focus is distinct. We address the challenge of reconstructing haplotype structure and frequencies from pooled sequencing samples where multiple individuals are sequenced simultaneously. A frequentist method to address this issue has recently been proposed. In contrast to this and other methods that compute point estimates, our proposed Bayesian hierarchical model delivers a posterior that permits us to also quantify uncertainty. Since matching permutations in both haplotype structure and corresponding frequency matrix lead to the same reconstruction of their product, we introduce an order-preserving shrinkage prior that ensures identifiability with respect to permutations. For inference, we introduce a blocked Gibbs sampler that enforces the required constraints. In a simulation study, we assessed the performance of our method. Furthermore, by using our approach on two distinct sets of real data, we demonstrate that our Bayesian approach can reconstruct the dominant haplotypes in a challenging, high-dimensional set-up.

Evaluation of adherence to abiraterone therapy in prostate cancer patients based on a population pharmacokinetic model.

AIMS: Abiraterone treatment requires regular drug intake under fasting conditions due to pronounced food effect, which may impact patient adherence. The aim of this prospective study was to evaluate adherence to abiraterone treatment in patients with prostate cancer. To achieve this aim, an abiraterone population pharmacokinetic model was developed and patients' adherence has been estimated by comparison of measured levels of abiraterone with population model-based simulations. METHODS: A total of 1469 abiraterone plasma levels from 83 healthy volunteers collected in a bioequivalence study were analysed using a nonlinear mixed-effects model. Monte Carlo simulation was used to describe the theoretical distribution of abiraterone pharmacokinetic profiles at a dose of 1000 mg once daily. Adherence of 36 prostate cancer patients treated with abiraterone was then evaluated by comparing the real abiraterone concentration measured in each patient during follow-up visit with the theoretical distribution of profiles based on simulations. Patients whose abiraterone levels were ˂5th or ˃95th percentile of the distribution of simulated profiles were considered to be non-adherent. RESULTS: Based on this evaluation, 13 patients (36%) have been classified as non-adherent. We observed significant association (P = .0361) between richness of the breakfast and rate of non-adherence. Adherent patients reported significantly better overall condition in self-assessments (P = .0384). A trend towards a higher occurrence of adverse effects in non-adherent patients was observed. CONCLUSIONS: We developed an abiraterone population pharmacokinetic model and proposed an advanced approach to medical adherence evaluation. Due to the need for administration under fasting conditions, abiraterone therapy is associated with a relatively high rate of non-adherence.

Learning from virtual experiments to assist users of Small Angle Neutron Scattering in model selection.

In this work, we combine the advantages of virtual Small Angle Neutron Scattering (SANS) experiments carried out by Monte Carlo simulations with the recent advances in computer vision to generate a tool that can assist SANS users in small angle scattering model selection. We generate a dataset of almost 260.000 SANS virtual experiments of the SANS beamline KWS-1 at FRM-II, Germany, intended for Machine Learning purposes. Then, we train a recommendation system based on an ensemble of Convolutional Neural Networks to predict the SANS model from the two-dimensional scattering pattern measured at the position-sensitive detector of the beamline. The results show that the CNNs can learn the model prediction task, and that this recommendation system has a high accuracy in the classification task on 46 different SANS models. We also test the network with real data and explore the outcome. Finally, we discuss the reach of counting with the set of virtual experimental data presented here, and of such a recommendation system in the SANS user data analysis procedure.

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks.

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data's abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.

Pesticide residues in vegetables from Gansu province, China and risk assessment by Monte Carlo simulation.

To assess the dietary exposure risks of pesticide residues in vegetables for the general population, the presence of 39 pesticides was determined in 70 samples, of which 13 were detected. The most frequently detected pesticide was bifenthrin, with a detection rate of 35.7%, mainly found in gingers, followed by isoprocarb and acephate (11.4%) and dimethoate (8.6%). In 17.1% of the samples two or more pesticide residues were found. Acute, chronic and chronic cumulative dietary exposure risk was assessed. Chronic exposure risks were determined by Monte Carlo Simulation (MCS). Estimated chronic exposure to carbofuran, omethoate, disulfoton and dimethoate of approximately 49%, 52%, 40% and 3%, respectively, were at non-carcinogenic human risk. Acute exposure risk to acephate was considered to be of concern due to the high acute hazard quotient (aHQ).

A bayesian spatio-temporal dynamic analysis of food security in Africa.

Exploring the factors influencing Food Security and Nutrition (FSN) and understanding its dynamics is crucial for planning and management. This understanding plays a pivotal role in supporting Africa's food security efforts to achieve various Sustainable Development Goals (SDGs). Utilizing Principal Component Analysis (PCA) on data from the FAO website, spanning from 2000 to 2019, informative components are derived for dynamic spatio-temporal modeling of Africa's FSN Given the dynamic and evolving nature of the factors impacting FSN, despite numerous efforts to understand and mitigate food insecurity, existing models often fail to capture this dynamic nature. This study employs a Bayesian dynamic spatio-temporal approach to explore the interconnected dynamics of food security and its components in Africa. The results reveal a consistent pattern of elevated FSN levels, showcasing notable stability in the initial and middle-to-late stages, followed by a significant acceleration in the late stage of the study period. The Democratic Republic of Congo and Ethiopia exhibited particularly noteworthy high levels of FSN dynamicity. In particular, child care factors and undernourishment factors showed significant dynamicity on FSN. This insight suggests establishing regional task forces or forums for coordinated responses to FSN challenges based on dynamicity patterns to prevent or mitigate the impact of potential food security crises.

Clinical application of a GPU-accelerated monte carlo dose verification for cyberknife M6 with Iris collimator.

PURPOSE: To apply an independent GPU-accelerated Monte Carlo (MC) dose verification for CyberKnife M6 with Iris collimator and evaluate the dose calculation accuracy of RayTracing (TPS-RT) algorithm and Monte Carlo (TPS-MC) algorithm in the Precision treatment planning system (TPS). METHODS: GPU-accelerated MC algorithm (ArcherQA-CK) was integrated into a commercial dose verification system, ArcherQA, to implement the patient-specific quality assurance in the CyberKnife M6 system. 30 clinical cases (10 cases in head, and 10 cases in chest, and 10 cases in abdomen) were collected in this study. For each case, three different dose calculation methods (TPS-MC, TPS-RT and ArcherQA-CK) were implemented based on the same treatment plan and compared with each other. For evaluation, the 3D global gamma analysis and dose parameters of the target volume and organs at risk (OARs) were analyzed comparatively. RESULTS: For gamma pass rates at the criterion of 2%/2 mm, the results were over 98.0% for TPS-MC vs.TPS-RT, TPS-MC vs. ArcherQA-CK and TPS-RT vs. ArcherQA-CK in head cases, 84.9% for TPS-MC vs.TPS-RT, 98.0% for TPS-MC vs. ArcherQA-CK and 83.3% for TPS-RT vs. ArcherQA-CK in chest cases, 98.2% for TPS-MC vs.TPS-RT, 99.4% for TPS-MC vs. ArcherQA-CK and 94.5% for TPS-RT vs. ArcherQA-CK in abdomen cases. For dose parameters of planning target volume (PTV) in chest cases, the deviations of TPS-RT vs. TPS-MC and ArcherQA-CK vs. TPS-MC had significant difference (P < 0.01), and the deviations of TPS-RT vs. TPS-MC and TPS-RT vs. ArcherQA-CK were similar (P > 0.05). ArcherQA-CK had less calculation time compared with TPS-MC (1.66 min vs. 65.11 min). CONCLUSIONS: Our proposed MC dose engine (ArcherQA-CK) has a high degree of consistency with the Precision TPS-MC algorithm, which can quickly identify the calculation errors of TPS-RT algorithm for some chest cases. ArcherQA-CK can provide accurate patient-specific quality assurance in clinical practice.

Trace elements in the Upper Indus River Basin (UIRB) of Western Himalayas: Quantification, sources modeling, and impacts.

This study conducted a comprehensive analysis of trace element concentrations in the Upper Indus River Basin (UIRB), a glacier-fed region in the Western Himalayas (WH), aiming to discern their environmental and anthropogenic sources and implications. Despite limited prior data, 69 samples were collected in 2019 from diverse sources within the UIRB, including mainstream, tributaries, and groundwater, to assess trace element concentrations. Enrichment factor (EF) results and comparisons with regional and global averages suggest that rising levels of Zn, Cd, and As may pose safety concerns for drinking water quality. Advanced multivariate statistical techniques such as principal component analysis (PCA), absolute principal component scores (APCS-MLR), Monte Carlo simulation (MCS), etc were applied to estimate the associated human health hazards and also identified key sources of trace elements. The 95th percentile of the MCS results indicates that the estimated total cancer risk for children is significantly greater than (>1000 times) the USEPA's acceptable risk threshold of 1.0 × 10-6. The results classified most of the trace elements into two distinct groups: Group A (Li, Rb, Sr, U, Cs, V, Ni, TI, Sb, Mo, Ge), linked to geogenic sources, showed lower concentrations in the lower-middle river reaches, including tributaries and downstream regions. Group B (Pb, Nb, Cr, Zn, Be, Al, Th, Ga, Cu, Co), influenced by both geogenic and anthropogenic activities, exhibited higher concentrations near urban centers and midstream areas, aligning with increased municipal waste and agricultural activities. Furthermore, APCS-MLR source apportionment indicated that trace elements originated from natural geogenic processes, including rock-water interactions and mineral dissolution, as well as anthropogenic activities. These findings underscore the need for targeted measures to mitigate anthropogenic impacts and safeguard water resources for communities along the IRB and WH.

Dosimetry and Monte Carlo modelling of the Papillon+ contact X-ray brachytherapy device.

PURPOSE: This study aimed to develop and validate a Monte Carlo (MC) model for the Papillon+ contact x-ray brachytherapy (CXB) device, producing 50 kilovolt (kV) X-rays, specifically focusing on its application with a 25 mm diameter rectal applicator for contact therapy. MATERIAL AND METHODS: The validation process involved depth dose and transverse dose profile measurements using EBT3 gafchromic films positioned in a plastic water low energy range phantom. The half-value layer (HVL) was further measured and derived from the simulated X-ray spectra. RESULTS: Excellent agreement within ±2% was achieved between the measured and simulated on-axis depth dose curves for the 25 mm rectal applicator. Transverse dose profile measurements showed a high level of agreement between the simulation and measurements, on average 3.1% in contact with the applicator at the surface of the phantom and on average 1.7% at 10 mm depth. A close agreement within 5.5% was noticed concerning the HVL between the measurement and simulation. The simulated gamma spectra and 2D-dose distribution demonstrated a soft X-ray energy spectrum and a uniform dose distribution in contact with the applicator. CONCLUSIONS: An MC model was successfully developed for the Papillon+ eBT device with a 25 mm diameter rectal applicator. The validated model, with its demonstrated accuracy in depth dose and transverse dose profile simulations, is a valuable tool for quality assurance and patient safety and, in a later phase, may be used for treatment planning, dose calculations and tissue inhomogeneity corrections.

Preclinical photon minibeam radiotherapy using a custom collimator: Dosimetry characterization and preliminary in-vivo results on a glioma model.

PURPOSE: The purpose of this study is to investigate the dosimetric characteristics of a collimator for minibeam radiotherapy (MBRT) with film dosimetry and Monte Carlo (MC) simulations. The outcome of MBRT with respect to conventional RT using a glioma preclinical model was also evaluated. METHODS: A multi-slit collimator was designed to be used with commercial small animal irradiator. The collimator was built by aligning 0.6 mm wide and 5 mm thick parallel lead leaves at 0.4 mm intervals. Dosimetry characteristics were evaluated by Gafchromic (CG) films and TOPAS Monte Carlo (MC) code. An in vivo experiment was performed using a glioma preclinical model by injecting two million GL261cells subcutaneously and treating with 25 Gy, single fraction, with MBRT and conventional RT. Survival curves and acute radiation damage were measured to compare both treatments. RESULTS: A satisfactory agreement between experimental results and MC simulations were obtained, the measured FWHM and distance between the peaks were respectively 0.431 and 1.098 mm. In vivo results show that MBRT can provide local tumor control for three weeks after RT treatment and a similar survival fraction of open beam radiotherapy. No severe acute effects were seen for the MBRT group. CONCLUSIONS: We developed a minibeam collimator and presented its dosimetric features. Satisfactory agreement between MC and GC films was found with differences consistent with uncertainties due to fabrication and set-up errors. The survival curves of MBRT and open field RT are similar while atoxicity is dramatically lower with MBRT, preliminarily confirming the expected effect.

Heavy metals in Ethiopian drinking water and public health risks: Insights from nationwide and regional analysis.

The ambitious sustainable development goal (SDG) 6 of the United Nations, which aims to achieve universal access to safe water and sanitation by 2030, remains elusive for many developing countries like Ethiopia. This is often due to a multitude of intricate factors, including the escalating degradation of water quality. Here, we present a comprehensive nationwide and regional analysis of heavy metal pollution in drinking water sources and the associated human health risks in Ethiopia based on a dataset of 11 heavy metal concentrations (n = 975) collated from available studies. Results indicate significant variations in heavy metal pollution in drinking water sources in Ethiopia, with 44 % of the total concentration exceeding maximum permissible limits. The mean concentrations were ranked as follows: Pb (1.92 mg/L) > Zn (1.25 mg/L) > Fe (0.56 mg/L) > Mn (0.43 mg/L) > Cu (0.40 mg/L) > Co (0.30 mg/L) > As (0.12 mg/L) > Ni (0.12 mg/L) > Cr (0.10 mg/L) > Cd (0.06 mg/L) > Hg (0.04 mg/L). We found that children are more vulnerable to non-carcinogenic health risks than adults, with the highest hazard quotient (HQ) exceedances of up to a factor of 1823 and 762, respectively. Furthermore, a Monte Carlo-based probabilistic risk assessment highlighted significant concerns regarding co-exposure to multiple heavy metals. The measured concentrations, ingestion rates, and exposure frequencies were identified as sensitive parameters. Overall, a higher risk was attributed to Pb and As, with river drinking water sources and the Tigray region requiring immediate mitigation measures. In conclusion, the findings emphasize the urgent need to test and purify water before consumption and to implement effective public health interventions. Furthermore, a multifaceted approach including regular monitoring, source protection, and proper waste management is recommended to expedite the achievement of SDGs and promote water sustainability in resource-limited Ethiopia and sub-Saharan Africa.

"dsbandrepair" - An updated Geant4-DNA simulation tool for evaluating the radiation-induced DNA damage and its repair.

PURPOSE: Interdisciplinary scientific communities have shown large interest to achieve a mechanistic description of radiation-induced biological damage, aiming to predict biological results produced by different radiation quality exposures. Monte Carlo track-structure simulations are suitable and reliable for the study of early DNA damage induction used as input for assessing DNA damage. This study presents the most recent improvements of a Geant4-DNA simulation tool named "dsbandrepair". METHODS: "dsbandrepair" is a Monte Carlo simulation tool based on a previous code (FullSim) that estimates the induction of early DNA single-strand breaks (SSBs) and double-strand breaks (DSBs). It uses DNA geometries generated by the DNAFabric computational tool for simulating the induction of early single-strand breaks (SSBs) and double-strand breaks (DSBs). Moreover, the new tool includes some published radiobiological models for survival fraction and un-rejoined DSB. Its application for a human fibroblast cell and human umbilical vein endothelial cell containing both heterochromatin and euchromatin was conducted. In addition, this new version offers the possibility of using the new IRT-syn method for computing the chemical stage. RESULTS: The direct and indirect strand breaks, SSBs, DSBs, and damage complexity obtained in this work are equivalent to those obtained with the previously published simulation tool when using the same configuration in the physical and chemical stages. Simulation results on survival fraction and un-rejoined DSB are in reasonable agreement with experimental data. CONCLUSIONS: "dsbandrepair" is a tool for simulating DNA damage and repair, benchmarked against experimental data. It has been released as an advanced example in Geant4.11.2.

Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM.

Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior information and can have poor statistical properties in small samples. We show how L-RM-ANOVA can be used with Bayesian estimation to resolve the aforementioned issues. We demonstrate how to place informative priors on model parameters that constitute main and interaction effects. We further show how to place weakly informative priors on standardized parameters which can be used when no prior information is available. We conclude that Bayesian estimation can lower Type 1 error and bias, and increase power and efficiency when priors are chosen adequately. We demonstrate the approach using a real empirical example and guide the readers through specification of the model. We argue that ANOVA tables and incomplete descriptive statistics are not sufficient information to specify informative priors, and we identify which parameter estimates should be reported in future research; thereby promoting cumulative research.

Artificial neural networks trained on simulated multispectral data for real-time imaging of skin microcirculatory blood oxygen saturation.

Significance: Imaging blood oxygen saturation ( SO 2 ) in the skin can be of clinical value when studying ischemic tissue. Emerging multispectral snapshot cameras enable real-time imaging but are limited by slow analysis when using inverse Monte Carlo (MC), the gold standard for analyzing multispectral data. Using artificial neural networks (ANNs) facilitates a significantly faster analysis but requires a large amount of high-quality training data from a wide range of tissue types for a precise estimation of SO 2 . Aim: We aim to develop a framework for training ANNs that estimates SO 2 in real time from multispectral data with a precision comparable to inverse MC. Approach: ANNs are trained using synthetic data from a model that includes MC simulations of light propagation in tissue and hardware characteristics. The model includes physiologically relevant variations in optical properties, unique sensor characteristics, variations in illumination spectrum, and detector noise. This approach enables a rapid way of generating high-quality training data that covers different tissue types and skin pigmentation. Results: The ANN implementation analyzes an image in 0.11 s, which is at least 10,000 times faster than inverse MC. The hardware modeling is significantly improved by an in-house calibration of the sensor spectral response. An in-vivo example shows that inverse MC and ANN give almost identical SO 2 values with a mean absolute deviation of 1.3%-units. Conclusions: ANN can replace inverse MC and enable real-time imaging of microcirculatory SO 2 in the skin if detailed and precise modeling of both tissue and hardware is used when generating training data.

Economic losses due to foot-and-mouth disease (FMD) in Ethiopian cattle.

Ethiopia's cattle population is among the largest in Africa and is burdened by frequent foot-and-mouth disease (FMD) outbreaks. FMD is caused by several distinct and highly contagious viral strains that can result in acute disease in cattle, causing losses in productivity and impeding international trade. This economic simulation study considered four main sources of losses due to FMD in cattle: reduced milk yield, draft power yield, fertility, and increased mortality. Economic losses were estimated per case across age-sex strata in 89 Ethiopian administrative zones for the years 2010-2021 using a wide range of data to estimate distributions for 30 input variables in a series of Monte Carlo simulations. It was estimated that an average case of FMD in Ethiopian cattle results in losses (mean values reported followed 95 % confidence intervals in brackets) of US dollars (USD) 11 (USD 7-USD 16) per case. Losses resulting from an average outbreak were estimated to be USD 2300 (USD 1400-USD 3300), while national annual losses were estimated to be USD 0.9 Mil. (USD 0.2 Mil.-USD 2.3 Mil.). Per cow-year, based on a national cow population of approximately 39 Mil. head, these estimated annual losses are equivalent to losses of only USD 0.02 (USD 0.01-USD 0.06). Nationally, these losses were significantly less than previously estimated in the literature, with currently estimated losses more accurately reflecting the economic burden of FMD in Ethiopian cattle over the past decade. The relatively small estimated losses suggest that control efforts based on widespread vaccination in countries with primarily extensive cattle production systems, such as Ethiopia, are unlikely to be economically sound. Sensitivity analyses suggested losses would be far greater in intensive systems, and that certainty surrounding incidence rates is paramount to the formulation of economically sound animal healthpolicy in regions with endemic FMD.

Application of Monte Carlo simulation to optimise the dosage regimen of meropenem in patients with augmented renal clearance for Pseudomonas aeruginosa infection.

Objective: To optimise the dosing regimen of meropenem for treating Pseudomonas aeruginosa (PA) infections in critically ill patients with augmented renal clearance (ARC) using pharmacokinetic/pharmacodynamic (PK/PD) principles and Monte Carlo simulation (MCS). Methods: This research involves an MCS based on PK data from patients with ARC and a minimum inhibitory concentration (MIC) distribution of PA. This study simplifies the methods section, focusing on the critical aspects of simulation and target values for effective treatment. Results: The study highlights key findings and emphasises that tailored dosing based on bacterial MIC values is essential for patients with ARC. It also notes that empirical treatment in patients with ARC should consider the MIC distribution, with 2 g every (q) 6 h administered to achieve the PK/PD target, while 3 g q 6 h is effective in inhibiting resistance. Conclusion: Tailored dosing based on bacterial MIC values is crucial for patients with ARC. Prolonged infusion time alone does not enhance efficacy. Empirical treatment in patients with ARC should consider MIC distribution; a dosage of 2 g q 6 h achieves the PK/PD target, while 3 g q 6 h (≥12 g daily) inhibits resistance.

Preliminary results of classifying otosclerosis and disarticulation using a convolutional neural network trained with simulated wideband acoustic immittance data.

Current noninvasive methods of clinical practice often do not identify the causes of conductive hearing loss due to pathologic changes in the middle ear with sufficient certainty. Wideband acoustic immittance (WAI) measurement is noninvasive, inexpensive and objective. It is very sensitive to pathologic changes in the middle ear and therefore promising for diagnosis. However, evaluation of the data is difficult because of large interindividual variations. Machine learning methods like Convolutional neural networks (CNN) which might be able to deal with this overlaying pattern require a large amount of labeled measurement data for training and validation. This is difficult to provide given the low prevalence of many middle-ear pathologies. Therefore, this study proposes an approach in which the WAI training data of the CNN are simulated with a finite-element ear model and the Monte-Carlo method. With this approach, virtual populations of normal, otosclerotic, and disarticulated ears were generated, consistent with the averaged data of measured populations and well representing the qualitative characteristics of individuals. The CNN trained with the virtual data achieved for otosclerosis an AUC of 91.1 %, a sensitivity of 85.7 %, and a specificity of 85.2 %. For disarticulation, an AUC of 99.5 %, sensitivity of 100 %, and specificity of 93.1 % was achieved. Furthermore, it was estimated that specificity could potentially be increased to about 99 % in both pathological cases if stapes reflex threshold measurements were used to confirm the diagnosis. Thus, the procedures' performance is comparable to classifiers from other studies trained with real measurement data, and therefore the procedure offers great potential for the diagnosis of rare pathologies or early-stages pathologies. The clinical potential of these preliminary results remains to be evaluated on more measurement data and additional pathologies.

Monte Carlo simulations of microdosimetry and radiolytic species production at long time post proton irradiation using GATE and Geant4-DNA.

BACKGROUND: Radiobiological effectiveness of radiation in cancer treatment can be studied at different scales (molecular till organ scale) and different time post irradiation. The production of free radicals and reactive oxygen species during water radiolysis is particularly relevant to understand the fundamental mechanisms playing a role in observed biological outcomes. The development and validation of Monte Carlo tools integrating the simulation of physical, physico-chemical and chemical stages after radiation is very important to maintain with experiments. PURPOSE: Therefore, in this study, we propose to validate a new Geant4-DNA chemistry module through the simulation of water radiolysis and Fricke dosimetry experiments on a proton preclinical beam line. MATERIAL AND METHODS: In this study, we used the GATE Monte Carlo simulation platform (version 9.3) to simulate a 67.5 MeV proton beam produced with the ARRONAX isochronous cyclotron (IBA Cyclone 70XP) at conventional dose rate (0.2 Gy/s) to simulate the irradiation of ultra-pure liquid water samples and Fricke dosimeter. We compared the depth dose profile with measurements performed with a plane parallel Advanced PTW 34045 Markus ionization chamber. Then, a new Geant4-DNA chemistry application proposed from Geant4 version 11.2 has been used to assess the evolution of HO • ${\mathrm{HO}}^ \bullet $ , e aq - ${\mathrm{e}}_{{\mathrm{aq}}}^ - $ , H 3 O + ${{\mathrm{H}}}_3{{\mathrm{O}}}^ + $ , H 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ , H 2 ${{\mathrm{H}}}_2$ , HO 2 • ${\mathrm{HO}}_2^ \bullet $ , HO 2 - , O 2 • - ${\mathrm{HO}}_2^ - ,{\mathrm{\ O}}_2^{ \bullet - }$ and HO - ${\mathrm{HO}}^ - $ reactive species along time until 1-h post-irradiation. In particular, the effect of oxygen and pH has been investigated through comparisons with experimental measurements of radiolytic yields for H 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ and Fe3+. RESULTS: GATE simulations reproduced, within 4%, the depth dose profile in liquid water. With Geant4-DNA, we were able to reproduce experimental H 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ radiolytic yields 1-h post-irradiation in aerated and deaerated conditions, showing the impact of small changes in oxygen concentrations on species evolution along time. For the Fricke dosimeter, simulated G(Fe3+) is 15.97 ± 0.2 molecules/100 eV which is 11% higher than the measured value (14.4 ± 04 molecules/100 eV). CONCLUSIONS: These results aim to be consolidated by new comparisons involving other radiolytic species, such as e aq - ${\mathrm{e}}_{{\mathrm{aq}}}^ - $ or , O 2 • - $,{\mathrm{\ O}}_2^{ \bullet - }$ to further study the mechanisms underlying the FLASH effect observed at ultra-high dose rates (UHDR).