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.

Numerical optimization of longitudinal collimator geometry for novel x-ray field.

Objective. A novel x-ray field produced by an ultrathin conical target is described in the literature. However, the optimal design for an associated collimator remains ambiguous. Current optimization methods using Monte Carlo calculations restrict the efficiency and robustness of the design process. A more generic optimization method that reduces parameter constraints while minimizing computational load is necessary. A numerical method for optimizing the longitudinal collimator hole geometry for a cylindrically-symmetrical x-ray tube is demonstrated and compared to Monte Carlo calculations.Approach. The x-ray phase space was modelled as a four-dimensional histogram differential in photon initial position, final position, and photon energy. The collimator was modeled as a stack of thin washers with varying inner radii. Simulated annealing was employed to optimize this set of inner radii according to various objective functions calculated on the photon flux at a specified plane.Main results. The analytical transport model used for optimization was validated against Monte Carlo calculations using Geant4 via its wrapper, TOPAS. Optimized collimators and the resulting photon flux profiles are presented for three focal spot sizes and five positions of the source. Optimizations were performed with multiple objective functions based on various weightings of precision, intensity, and field flatness metrics. Finally, a select set of these optimized collimators, plus a parallel-hole collimator for comparison, were modeled in TOPAS. The evolution of the radiation field profiles are presented for various positions of the source for each collimator.Significance. This novel optimization strategy proved consistent and robust across the range of x-ray tube settings regardless of the optimization starting point. Common collimator geometries were re-derived using this algorithm while simultaneously optimizing geometry-specific parameters. The advantages of this strategy over iterative Monte Carlo-based techniques, including computational efficiency, radiation source-specificity, and solution flexibility, make it a desirable optimization method for complex irradiation geometries.

Deconvolution and Analysis of the 1H NMR Spectra of Crude Reaction Mixtures.

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity of manually analyzing the NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with the experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C-H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with a mean absolute error as low as 1%.

Dosimetric feasibility study ("proof of concept") of refractory ventricular tachycardia radioablation using proton minibeams.

PURPOSE: Preclinical data demonstrated that the use of proton minibeam radiotherapy reduces the risk of toxicity in healthy tissue. Ventricular tachycardia radioablation is an area under clinical investigation in proton beam therapy. We sought to simulate a ventricular tachycardia radioablation with proton minibeams and to demonstrate that it was possible to obtain a homogeneous coverage of an arrhythmogenic cardiac zone with this technique. MATERIAL AND METHODS: An arrhythmogenic target volume was defined on the simulation CT scan of a patient, localized in the lateral wall of the left ventricle. A dose of 25Gy was planned to be delivered by proton minibeam radiotherapy, simulated using a Monte Carlo code (TOPAS v.3.7) with a collimator of 19 0.4 mm-wide slits spaced 3mm apart. The main objective of the study was to obtain a plan ensuring at least 93% of the prescription dose in 93% of the planning target volume without exceeding 110% of the prescribed dose in the planning target volume. RESULTS: The average dose in the planning treatment volume in proton minibeam radiotherapy was 25.12Gy. The percentage of the planning target volume receiving 93% (V93%), 110% (V110%), and 95% (V95%) of the prescribed dose was 94.25%, 0%, and 92.6% respectively. The lateral penumbra was 6.6mm. The mean value of the peak-to-valley-dose ratio in the planning target volume was 1.06. The mean heart dose was 2.54Gy versus 5.95Gy with stereotactic photon beam irradiation. CONCLUSION: This proof-of-concept study shows that proton minibeam radiotherapy can achieve a homogeneous coverage of an arrhythmogenic cardiac zone, reducing the dose at the normal tissues. This technique, ensuring could theoretically reduce the risk of late pulmonary and breast fibrosis, as well as cardiac toxicity as seen in previous biological studies in proton minibeam radiotherapy.

Bayesian modeling of quantiles of body mass index among under-five children in Ethiopia.

BACKGROUND: Body Mass Index (BMI) is a measurement of nutritional status, which is a vital pre-condition for good health. The prevalence of childhood malnutrition and the potential long-term health risks associated with obesity in Ethiopia have recently increased globally. The main objective of this study was to investigate the factors associated with the quantiles of under-five children's BMI in Ethiopia. METHODS: Data on 5,323 children, aged between 0-59 months from March 21, 2019, to June 28, 2019, were obtained from the Ethiopian Mini Demographic Health Survey (EMDHS, 2019), based on the standards set by the World Health Organization. The study used a Bayesian quantile regression model to investigate the association of factors with the quantiles of under-five children's body mass index. Markov Chain Monte Carlo (MCMC) with Gibbs sampling was used to estimate the country-specific marginal posterior distribution estimates of model parameters, using the Brq R package. RESULTS: Out of a total of 5323 children included in this study, 5.09% were underweight (less than 12.92 BMI), 10.05% were overweight (BMI: 17.06 - 18.27), and 5.02% were obese (greater than or equal to 18.27 BMI) children's. The result of the Bayesian quantile regression model, including marginal posterior credible intervals (CIs), showed that for the prediction of the 0.05 quantile of BMI, the current age of children [ ß = -0.007, 95% CI :(-0.01, -0.004)], the region Afar [ ß = - 0.32, 95% CI: (-0.57, -0.08)] and Somalia[ ß = -0.72, 95% CI: (-0.96, -0.49)] were negatively associated with body mass index while maternal age [ ß = 0.01, 95% CI: (0.005, 0.02)], mothers primary education [ ß = 0.19, 95% CI: (0.08, 0.29)], secondary and above [ ß = 0.44, 95% CI: (0.29, 0.58)], and family follows protestant [ ß = 0.22, 95% CI: (0.07, 0.37)] were positively associated with body mass index. In the prediction of the 0.95 (or 0.85?) quantile of BMI, in the upper quantile, still breastfeeding [ ß = -0.25, 95% CI: (-0.41, -0.10)], being female [ ß = -0.13, 95% CI: (-0.23, -0.03)] were negatively related while wealth index [ ß = 0.436, 95% CI: (0.25, 0.62)] was positively associated with under-five children's BMI. CONCLUSIONS: In conclusion, the research findings indicate that the percentage of lower and higher BMI for under-five children in Ethiopia is high. Factors such as the current age of children, sex of children, maternal age, religion of the family, region and wealth index were found to have a significant impact on the BMI of under-five children both at lower and upper quantile levels. Thus, these findings highlight the need for administrators and policymakers to devise and implement strategies aimed at enhancing the normal or healthy weight status among under-five children in Ethiopia.

A Mathematical Model for the Impact of 3HP and Social Programme Implementation on the Incidence and Mortality of Tuberculosis: Study in Brazil.

In this paper, we presented a mathematical model for tuberculosis with treatment for latent tuberculosis cases and incorporated social implementations based on the impact they will have on tuberculosis incidence, cure, and recovery. We incorporated two variables containing the accumulated deaths and active cases into the model in order to study the incidence and mortality rate per year with the data reported by the model. Our objective is to study the impact of social program implementations and therapies on latent tuberculosis in particular the use of once-weekly isoniazid-rifapentine for 12 weeks (3HP). The computational experimentation was performed with data from Brazil and for model calibration, we used the Markov Chain Monte Carlo method (MCMC) with a Bayesian approach. We studied the effect of increasing the coverage of social programs, the Bolsa Familia Programme (BFP) and the Family Health Strategy (FHS) and the implementation of the 3HP as a substitution therapy for two rates of diagnosis and treatment of latent at 1% and 5%. Based of the data obtained by the model in the period 2023-2035, the FHS reported better results than BFP in the case of social implementations and 3HP with a higher rate of diagnosis and treatment of latent in the reduction of incidence and mortality rate and in cases and deaths avoided. With the objective of linking the social and biomedical implementations, we constructed two different scenarios with the rate of diagnosis and treatment. We verified with results reported by the model that with the social implementations studied and the 3HP with the highest rate of diagnosis and treatment of latent, the best results were obtained in comparison with the other independent and joint implementations. A reduction of the incidence by 36.54% with respect to the model with the current strategies and coverage was achieved, and a greater number of cases and deaths from tuberculosis was avoided.

Tuning spatially fractionated radiotherapy dose profiles using the moiré effect.

Spatially Fractionated Radiotherapy (SFRT) has demonstrated promising potential in cancer treatment, combining the advantages of reduced post-radiation effects and enhanced local control rates. Within this paradigm, proton minibeam radiotherapy (pMBRT) was suggested as a new treatment modality, possibly producing superior normal tissue sparing to conventional proton therapy, leading to improvements in patient outcomes. However, an effective and convenient beam generation method for pMBRT, capable of implementing various optimum dose profiles, is essential for its real-world application. Our study investigates the potential of utilizing the moiré effect in a dual collimator system (DCS) to generate pMBRT dose profiles with the flexibility to modify the center-to-center distance (CTC) of the dose distribution in a technically simple way.We employ the Geant4 Monte Carlo simulations tool to demonstrate that the angle between the two collimators of a DCS can significantly impact the dose profile. Varying the DCS angle from 10 ∘ to 50 ∘ we could cover CTC ranging from 11.8 mm to 2.4 mm, respectively. Further investigations reveal the substantial influence of the multi-slit collimator's (MSC) physical parameters on the spatially fractionated dose profile, such as period (CTC), throughput, and spacing between MSCs. These findings highlight opportunities for precision dose profile adjustments tailored to specific clinical scenarios.The DCS capacity for rapid angle adjustments during the energy transition stages of a spot scanning system can facilitate dynamic alterations in the irradiation profile, enhancing dose contrast in normal tissues. Furthermore, its unique attribute of spatially fractionated doses in both lateral directions could potentially improve normal tissue sparing by minimizing irradiated volume. Beyond the realm of pMBRT, the dual MSC system exhibits remarkable versatility, showing compatibility with different types of beams (X-rays and electrons) and applicability across various SFRT modalities.Our study illuminates the dual MSC system's potential as an efficient and adaptable tool in the refinement of pMBRT techniques. By enabling meticulous control over irradiation profiles, this system may expedite advancements in clinical and experimental applications, thereby contributing to the evolution of SFRT strategies.

Concentrations, compositional profiles, and health risks of benzophenones among the Taiwanese population based on analysis of 23 daily consumed foods.

In this study, we analyzed the occurrence and distribution of 11 benzophenone-type ultraviolet filters (BPs) in 893 food samples spanning 7 food categories in Taiwan. We conducted a Monte Carlo simulation to determine the carcinogenic and noncarcinogenic risks of BPs. The results indicated that cornflakes had the highest mean level of BPs (103 ng/g), followed by bread (101 ng/g) and pastries (59 ng/g). BP was the most prevalent category, followed by 4-methylbenzophenone (4-MBP), 2-hydroxybenzophenone, and benzophenone-3. Estimation of the lifetime cancer risk (LTCR) of BP (average life expectancy of 80 years) placed them in the 50th and 97.5th percentiles [P50 (P97.5)] LTCR of 1.9 × 10-7 (5.7 × 10-6), indicating that BP in food poses a low renal hazard to the Taiwanese population. The noncarcinogenic risk of BPs was evaluated using a hazard quotient and combined margin of exposure (MOET), revealing a P50 (P97.5) hazard index of < 1 for BP, 4-MBP, and methyl-2-benzoylbenzoate. Although the P50 MOET values for all age groups were within the moderate range of concern, with a more conservative extreme (P2.5), the MOET values for the 0-3, 3-6, and 6-12 age groups fell below 100, indicating a high concern for renal degeneration and hyperplasia.

Monitoring multistage healthcare processes using state space models and a machine learning based framework.

Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are often characterized by a multistage structure in which several components, states or stages form the final products or outcomes. In such complex scenarios, Multistage Process Monitoring (MPM) techniques become invaluable for monitoring distinct states of the process over time. However, the healthcare sector has seen limited studies employing MPM. This study aims to fill this gap by developing an MPM control chart tailored for healthcare data to promote early detection, confirmation, and patient safety. As it is important to detect unnatural conditions in healthcare processes at an early stage, the statistical control charts are combined with machine learning techniques (i.e., we deal with Intelligent Control Charting, ICC) to enhance detection ability. Through Monte Carlo simulations, our method demonstrates better performance compared to its statistical counterparts. To underline the practical application of the proposed ICC framework, real data from a two-stage thyroid cancer surgery is utilized. This real-world case serves as a compelling illustration of the effectiveness of the developed MPM control chart in a healthcare setting.

[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 simulation of the effect of melanin concentration on light-tissue interactions in transmittance and reflectance finger photoplethysmography.

Photoplethysmography (PPG) uses light to detect volumetric changes in blood, and is integrated into many healthcare devices to monitor various physiological measurements. However, an unresolved limitation of PPG is the effect of skin pigmentation on the signal and its impact on PPG based applications such as pulse oximetry. Hence, an in-silico model of the human finger was developed using the Monte Carlo (MC) technique to simulate light interactions with different melanin concentrations in a human finger, as it is the primary determinant of skin pigmentation. The AC/DC ratio in reflectance PPG mode was evaluated at source-detector separations of 1 mm and 3 mm as the convergence rate (Q), a parameter that quantifies the accuracy of the simulation, exceeded a threshold of 0.001. At a source-detector separation of 3 mm, the AC/DC ratio of light skin was 0.472 times more than moderate skin and 6.39 than dark skin at 660 nm, and 0.114 and 0.141 respectively at 940 nm. These findings are significant for the development of PPG-based sensors given the ongoing concerns regarding the impact of skin pigmentation on healthcare devices.

A Bayesian model-based reduced major axis regression.

Reduced major axis (RMA) regression, widely used in the fields of zoology, botany, ecology, biology, spectroscopy, and among others, outweighs the ordinary least square regression by relaxing the assumption that the covariates are without measurement errors. A Bayesian implementation of the RMA regression is presented in this paper, and the equivalence of the estimates of the parameters under the Bayesian and the frequentist frameworks is proved. This model-based Bayesian RMA method is advantageous since the posterior estimates, the standard deviations, as well as the credible intervals of the estimates can be obtained through Markov chain Monte Carlo methods directly. In addition, it is straightforward to extend to the multivariate RMA case. The performance of the Bayesian RMA approach is evaluated in the simulation study, and, finally, the proposed method is applied to analyze a dataset in the plantation.

Evaluating the lifetime performance index of omega distribution based on progressive type-II censored samples.

Besides achieving high quality products, statistical techniques are applied in many fields associated with health such as medicine, biology and etc. Adhering to the quality performance of an item to the desired level is a very important issue in various fields. Process capability indices play a vital role in evaluating the performance of an item. In this paper, the larger-the-better process capability index for the three-parameter Omega model based on progressive type-II censoring sample is calculated. On the basis of progressive type-II censoring the statistical inference about process capability index is carried out through the maximum likelihood. Also, the confidence interval is proposed and the hypothesis test for estimating the lifetime performance of products. Gibbs within Metropolis-Hasting samplers procedure is used for performing Markov Chain Monte Carlo (MCMC) technique to achieve Bayes estimation for unknown parameters. Simulation study is calculated to show that Omega distribution's performance is more effective. At the end of this paper, there are two real-life applications, one of them is about high-performance liquid chromatography (HPLC) data of blood samples from organ transplant recipients. The other application is about real-life data of ball bearing data. These applications are used to illustrate the importance of Omega distribution in lifetime data analysis.

[Health Risk Assessment for an Arsenic-contaminated Site Based on Monte Carlo Simulation and Parameters Optimization].

Risk assessment is a critical part of risk management for contaminated sites. However， in the specific management practice of As-contaminated sites， it is difficult to obtain realistic health risks for contaminated sites based on the total amount of pollutants and determined values of the model， thus preventing the control requirements of later remediation to be met. An increasing number of studies have recently been conducting risk assessments by considering bioavailability， modification parameters， and combined probabilistic models. To improve the accuracy of risk assessment results， taking a large As-contaminated site as a case， 432 sampling sites were set up and collected at different depths to analyze the level and distribution characteristics of As pollution， and probabilistic risk assessment was conducted with the modification of model parameters through literature research and Monte Carlo simulation. Then， the impact of traditional methods and probabilistic methods on health risk assessment was explored in comparison. The results indicated that ω（As） in the top soil of the study area ranged from 2.70-97.0 mg·kg-1， with a spatial variation coefficient of 0.61 and weaker spatial continuity. The carcinogenic risk and hazard index obtained by the traditional risk assessment method were 2.12E-4 and 8.36， respectively， which obviously overestimated the actual risk level and were not conductive to the refined management of As-contaminated sites. Combined with modification of model parameters and probabilistic risk assessment， the non-carcinogenic risk for adults and children was found to be at an acceptable level， and the carcinogenic risk was reduced by nearly an order of magnitude compared to that in the conventional method. Considering the relative biological effectiveness （RBA） of As， the 95% quantile of the total carcinogenic risk was 1.24E-5， a reduction of up to 36.41% compared to the uncorrected corresponding risk value of 1.95E-5. The carcinogenic risk of soil As for adults and children in the study area exceeded acceptable risk levels 1E-6， with oral ingestion of soil being the primary route of exposure. In addition， the results of the sensitivity analysis of the parameters showed that As concentration， daily oral ingestion rate of soils， and exposure duration of children had relatively larger effects for health risks. This work will provide a methodological and theoretical basis for achieving accurate risk assessment of As-contaminated sites and provide concepts for refined risk management.

On the reliable estimation of sequential Monod kinetic parameters.

While dozens of studies have attempted to estimate the Monod kinetic parameters of microbial reductive dechlorination, published values in the literature vary by 2-6 orders of magnitude. This lack of consensus can be attributed in part to limitations of both experimental design and parameter estimation techniques. To address these issues, Hamiltonian Monte Carlo was used to produce more than one million sets of realistic simulated microcosm data under a variety of experimental conditions. These data were then employed in model fitting experiments using a number of parameter estimation algorithms for determining Monod kinetic parameters. Analysis of data from conventional triplicate microcosms yielded parameter estimates characterized by high collinearity, resulting in poor estimation accuracy and precision. Additionally, confidence intervals computed by commonly used classical regression analysis techniques contained true parameter values much less frequently than their nominal confidence levels. Use of an alternative experimental design, requiring the same number of analyses as conventional experiments but comprised of microcosms with varying initial chlorinated ethene concentrations, is shown to result in order-of-magnitude decreases in parameter uncertainty. A Metropolis algorithm which can be run on a typical personal computer is demonstrated to return more reliable parameter interval estimates.

Optimization of Ganciclovir and Valganciclovir Starting Dose in Children by Machine Learning.

BACKGROUND AND OBJECTIVES: Ganciclovir (GCV) and valganciclovir (VGCV) show large interindividual pharmacokinetic variability, particularly in children. The objectives of this study were (1) to develop machine learning (ML) algorithms trained on simulated pharmacokinetics profiles obtained by Monte Carlo simulations to estimate the best ganciclovir or valganciclovir starting dose in children and (2) to compare its performances on real-world profiles to previously published equation derived from literature population pharmacokinetic (POPPK) models achieving about 20% of profiles within the target. MATERIALS AND METHODS: The pharmacokinetic parameters of four literature POPPK models in addition to the World Health Organization (WHO) growth curve for children were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles. ML algorithms were developed and benchmarked to predict the probability to reach the steady-state, area-under-the-curve target (AUC0-24 within 40-60 mg × h/L) based on demographic characteristics only. The best ML algorithm was then used to calculate the starting dose maximizing the target attainment. Performances were evaluated for ML and literature formula in a test set and in an external set of 32 and 31 actual patients (GCV and VGCV, respectively). RESULTS: A combination of Xgboost, neural network, and random forest algorithms yielded the best performances and highest target attainment in the test set (36.8% for GCV and 35.3% for the VGCV). In actual patients, the best GCV ML starting dose yielded the highest target attainment rate (25.8%) and performed equally for VGCV with the Franck model formula (35.3% for both). CONCLUSION: The ML algorithms exhibit good performances in comparison with previously validated models and should be evaluated prospectively.

Dosimetric validation of SmART-RAD Monte Carlo modelling for x-ray cabinet radiobiology irradiators.

Objective. Accuracy and reproducibility in the measurement of radiation dose and associated reporting are critically important for the validity of basic and preclinical radiobiological studies performed with kilovolt x-ray radiation cabinets. This is essential to enable results of radiobiological studies to be repeated, as well as enable valid comparisons between laboratories. In addition, the commonly used single point dose value hides the 3D dose heterogeneity across the irradiated sample. This is particularly true for preclinical rodent models, and is generally difficult to measure directly. Radiation transport simulations integrated in an easy to use application could help researchers improve quality of dosimetry and reporting.Approach. This paper describes the use and dosimetric validation of a newly-developed Monte Carlo (MC) tool, SmART-RAD, to simulate the x-ray field in a range of standard commercial x-ray cabinet irradiators used for preclinical irradiations. Comparisons are made between simulated and experimentally determined dose distributions for a range of configurations to assess the potential use of this tool in determining dose distributions through samples, based on more readily available air-kerma calibration point measurements.Main results. Simulations gave very good dosimetric agreement with measured depth dose distributions in phantoms containing both water and bone equivalent materials. Good spatial and dosimetric agreement between simulated and measured dose distributions was obtained when using beam-shaping shielding.Significance. The MC simulations provided by SmART-RAD provide a useful tool to go from a limited number of dosimetry measurements to detailed 3D dose distributions through a non-homogeneous irradiated sample. This is particularly important when trying to determine the dose distribution in more complex geometries. The use of such a tool can improve reproducibility and dosimetry reporting in preclinical radiobiological research.

Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors.

The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose. To evaluate the models under near real-world conditions, Monte Carlo sampling was implemented for the first time. This technique enables the use of probabilistic fingerprint features derived from the experimental HRMS data with SIRIUS+CSI:FingerID as an input for models trained on true binary fingerprint features. Depending on the bioassay, the lowest false-positive rate at 90% recall ranged from 0.251 (sr.mmp, mitochondrial membrane potential) to 0.824 (nr.ar, androgen receptor), which is consistent with the trends observed in the models' performances submitted for the Tox21 Data Challenge. These findings underscore the informativeness of fingerprint features that can be compiled from HRMS in predicting the endocrine-disrupting activity. Moreover, an in-depth SHapley Additive exPlanations analysis unveiled the models' ability to pinpoint structural patterns linked to the modes of action of active chemicals. Despite the superior performance of the single-output models compared to that of the multi-output models, the latter's potential cannot be disregarded for similar tasks in the field of in silico toxicology. This study presents a significant advancement in identifying potentially toxic chemicals within complex mixtures without unambiguous identification and effectively reducing the workload for postprocessing by up to 75% in nontarget HRMS.

Dosing of Convalescent Plasma and Hyperimmune Anti-SARS-CoV-2 Immunoglobulins: A Phase I/II Dose-Finding Study.

BACKGROUND AND OBJECTIVE: During the COVID-19 pandemic, trials on convalescent plasma (ConvP) were performed without preceding dose-finding studies. This study aimed to assess potential protective dosing regimens by constructing a population pharmacokinetic (popPK) model describing anti-SARS-CoV-2 antibody titers following the administration of ConvP or hyperimmune globulins (COVIg). METHODS: Immunocompromised patients, testing negative for anti-SARS-CoV-2 spike antibodies despite vaccination, received a range of anti-SARS-CoV-2 antibodies in the form of COVIg or ConvP infusion. The popPK analysis was performed using NONMEM v7.4. Monte Carlo simulations were performed to assess potential COVIg and ConvP dosing regimens for prevention of COVID-19. RESULTS: Forty-four patients were enrolled, and data from 42 were used for constructing the popPK model. A two-compartment elimination model with mixed residual error best described the Nab-titers after administration. Inter-individual variation was associated to CL (44.3%), V1 (27.3%), and V2 (29.2%). Lean body weight and type of treatment (ConvP/COVIg) were associated with V1 and V2, respectively. Median elimination half-life was 20 days (interquartile range: 17-25 days). Simulations demonstrated that even monthly infusions of 600 mL of the ConvP or COVIg used in this trial would not achieve potentially protective serum antibody titers for > 90% of the time. However, as a result of hybrid immunity and/or repeated vaccination, plasma donors with extremely high antibody titers are now readily available, and a > 90% target attainment should be possible. CONCLUSION: The results of this study may inform future intervention studies on the prophylactic and therapeutic use of antiviral antibodies in the form of ConvP or COVIg. CLINICAL TRIAL REGISTRATION NUMBER: NL9379 (The Netherlands Trial Register).

Estimating of radiation output of X-RAY tube using mathematical model: Case of high-frequency machines.

PURPOSE: During any radiological procedure, it is important to know the dose to be-administered to the patient and this can be done by estimating the output of the X-ray tube either with a dosimeter or with a mathematical equation or Monte Carlo simulations. The aim of this work is to develop a new mathematical model equation (NMME) for estimating the output of high-frequency X-ray tubes. METHODS: To achieve this, data collected from ten machines in many regions of Cameroon were used (for nine machines) to build an initial model that does not take into account the anode angle and the tenth machine was used to test the model. Using the SpekCalc software, some simulations were carried out to evaluate the influence of the anode angle. This allowed the NMME to be proposed. RESULTS: The deviations frequencies between 0.65% and 19.61% were obtained by comparing the output values obtained using initial model with the measured values. The statistical hypothesis test showed that the estimated values using initial model and NMME are in agreement with those measured unlike the Kothan and Tungjai model. For the tenth machine, the percentage difference between estimated and measured values is less than 8 %. CONCLUSION: These results show that the proposed model performed better than the previous models. In the absence of a dosimeter, the NMME could be used to estimate the output of high frequency X-ray machines and therefore the radiation doses received by patients during diagnostic X-ray examinations.