In the present study, ninety-five halogenated dioxins and related chemicals (dibenzo-p-dioxins, dibenzofurans, biphenyls, and naphthalene) with endpoint pEC50 were used to develop twelve quantitative structure toxicity relationship (QSTR) models using inbuilt Monte Carlo algorithm of CORAL software. The hybrid optimal descriptor of correlation weights (DCW) using a combination of SMILES and HSG (hydrogen suppressed graph) was employed to generate QSTR models. Three target functions i.e. TF1 (WIIC=WCII=0), TF2 (WIIC= 0.3 & WCII=0) and TF3 (WIIC= 0.0 &WCII=0.3) were employed to develop robust QSTR models and the statistical outcomes of each target function were compared with each other. The correlation intensity index (CII) was found a reliable benchmark of the predictive potential for QSTR models. The numerical value of the determination coefficient of the validation set of split 1 computed by TF3 was found highest (RValid2=0.8438). The fragments responsible for the toxicity of dioxins and related chemicals were also identified in terms of the promoter of increase/decrease for pEC50. Three random splits (Split 1, Split 2 and Split 4) were selected for the extraction of the promoter of increase/decrease for pEC50. In the last, consensus modelling was performed using the intelligent consensus tool of DTC lab (https://dtclab.webs.com/software-tools). The original consensus model, which was created by combining four distinct models employing the split 4 arrangement, was more predictive for the validation set and the numerical value of the determination coefficient of the test set (validation set) was increased from 0.8133 to 0.9725. For the validation set of split 4, the mean absolute error (MAE 100%) was also lowered from 0.513 to 0.2739.

OBJECTIVE: Proton minibeam radiation therapy (pMBRT) is a new radiotherapy approach that has shown a significant increase in the therapeutic window in glioma-bearing rats compared to conventional proton therapy. The dosimetry of pMBRT is challenging and error prone due to the submillimetric beamlet sizes used. The aim of this study was to perform a robustness analysis on the setup parameters utilized in current preclinical trials and provide guidelines for reproducible dosimetry. The results of this work are intended to guide upcoming implementations of pMBRT worldwide, as well as pave the way for future clinical implementations.
METHODS: Monte Carlo simulations and experimental data were used to evaluate the impact of variations in setup parameters and uncertainties in collimator specifications on lateral pMBRT dose distributions. The value of each parameter was modified individually to evaluate their effect on dose distributions. Experimental dosimetry was performed by means of high-resolution detectors, that is, radiochromic films, the IBA Razor and the Microdiamond detector. New guidelines were proposed to optimize the experimental setup in pMBRT studies and perform reproducible dosimetry.
RESULTS: The sensitivity of dose distributions to uncertainties and variations in setup parameters was quantified. Quantities that define pMBRT lateral profiles (i.e., the peak-to-valley dose ratio [PVDR], peak and valley doses, and peak width) are significantly influenced by small-scale fluctuations in several of those parameters. The setup implemented at the Orsay proton therapy center for pMBRT irradiation was optimized to increase PVDRs and peak symmetry. In addition, we proposed guidelines to perform accurate and reproducible dosimetry in preclinical studies.
CONCLUSIONS: This study revealed the importance of adopting guidelines and protocols tailored to the distinct dose delivery method and dose distributions in pMBRT. This new methodology leads to reproducible dosimetry, which is imperative in preclinical trials. The results and guidelines presented in this manuscript can ease the initiation of pMBRT investigations in other centers.

We propose methods to estimate a suitable number of patients for implementing selective safety data collection (SSDC) in clinical investigations based on a confidence interval of the incidence rate or risk difference using Monte Carlo simulation.
The incidence rates and risk differences of adverse events (AEs) were based on the safety outcome measures. A suitable number of patients for implementing SSDC was estimated based on the probability that the half-width of the two-sided 95% confidence interval of incidence rate or risk difference was equal to or less than a pre-specified cut-off value (0.5-3.0%). Monte Carlo simulation was used to estimate the suitable number of patients at probabilities of 70%, 80%, and 90%. The applicability of our proposed method for estimating a suitable number of patients for SSDC implementation was confirmed based on the incidence rates or risk differences from actual clinical trial data for panitumumab.
We demonstrated the performance of our proposed method in estimating a suitable number of patients to implement SSDC in several situations. Furthermore, according to the safety datasets of three phase III clinical trials, the number of suitable patients for implementing SSDC using incidence rates or risk differences of common AEs with panitumumab could confirm the applicability of our proposed method.
A suitable number of patients estimated based on our proposed method may be one of the foundations for implementing SSDC, as additional data accrual may not impact the precision of the estimates of the frequency of common AEs.

Meropenem is increasingly used to treat neonatal sepsis. There are several guidelines recommending different dosing regimens of meropenem in neonates. Furthermore, deviations from these guidelines regularly occur in daily clinical practice. Therefore, the current study aimed to evaluate the variations of meropenem dosing guidelines and compare the difference between guideline and clinical practice in terms of the probability of target attainment.
This study is based on a population pharmacokinetic model. After defining the predictive performance of the model, Monte Carlo simulations were used to calculate the probability of target attainment of the currently existing dosing guidelines of meropenem and their use in daily clinical practice.
Two guidelines and two labels were included in the Monte Carlo simulations. For 70% fT>MIC (fraction of time when the free meropenem concentration exceeded the minimum inhibitory concentration during the dosing interval), the probability of target attainment of four recommended doses ranged from 59% to 88% (MIC = 2 mg·L-1 ) and from 17% to 47% (MIC = 8 mg·L-1 ). At the clinical practice evaluation, only 20% of patients attained target exposure for the MIC of 8 mg·L-1 with 70% fT>MIC , which was much less than those found in the Food and Drug Administration labels (40%).
This model-based population pharmacokinetics simulation showed that improper guidelines and/or clinical practice deviations will result in low probability of target attainment for patients infected with resistant bacteria and critically ill patients. It is important to develop and adhere to evidence-based and clinically pragmatic guidelines.

To promote consistency in clinical trials by recommending a uniform framework as it relates to radiation transport and dose calculation in water versus in medium.
The Global Quality Assurance of Radiation Therapy Clinical Trials Harmonisation Group (GHG; www.rtqaharmonization.org) compared the differences between dose to water in water (Dw,w), dose to water in medium (Dw,m), and dose to medium in medium (Dm,m). This was done based on a review of historical frameworks, existing literature and standards, clinical issues in the context of clinical trials, and the trajectory of radiation dose calculations. Based on these factors, recommendations were developed.
No framework was found to be ideal or perfect given the history, complexity, and current status of radiation therapy. Nevertheless, based on the evidence available, the GHG established a recommendation preferring dose to medium in medium (Dm,m).
Dose to medium in medium (Dm,m) is the preferred dose calculation and reporting framework. If an institution\'s planning system can only calculate dose to water in water (Dw,w), this is acceptable.

Numerous guidelines exist for the management of pancreatic cysts. We sought to compare the guideline-directed management strategies for pancreatic cysts by comparing 2 approaches (2017 International Consensus Guidelines and 2015 American Gastroenterological Association Guidelines) that differ significantly in their thresholds for imaging, surveillance, and surgery.
We developed a Monte Carlo model to evaluate the outcomes for a cohort of 10,000 patients managed per each guideline. The primary outcome was mortality related to pancreatic cyst management. Secondary outcomes included all-cause mortality, missed cancers, number of surgeries, number of imaging studies, cumulative cost, and quality-adjusted life years.
Deaths because of pancreatic cyst management and quality-adjusted life years were similar in both guidelines at a significantly higher cost of $3.6 million per additional cancer detected in the Consensus Guidelines. Deaths from \"unrelated\" causes (1,422) vastly outnumbered deaths related to pancreatic cysts (125). Secondary outcomes included more missed cancers in the American Gastroenterological Association guideline (71 vs 49), more surgeries and imaging studies in the Consensus guideline (711 vs 163; 116,997 vs 68,912), and higher cost in the Consensus guideline ($168.3 million vs $89.4 million). As the rate of malignant transformation increases, a more-intensive guideline resulted in fewer deaths related to pancreatic cyst management.
Our study demonstrates trade-offs between more- and less-intensive management strategies for pancreatic cysts. Although deaths related to pancreatic cyst management were similar in each strategy, fewer missed cancers in the more-intensive surveillance strategy is offset by a greater number of surgical deaths and higher cost. In conclusion, our study identifies that if the rate malignant transformation of pancreatic cysts is low (0.12% annually), a less-intensive guideline will result in similar deaths to a more-intensive guideline at a much lower cost.

Systematic, automated methods for monitoring physician performance are necessary if outlying behavior is to be detected promptly and acted on. In the Michigan Urological Surgery Improvement Collaborative (MUSIC), we evaluated several statistical process control (SPC) methods to determine the sensitivity and ease of interpretation for assessing adherence to imaging guidelines for patients with newly diagnosed prostate cancer.
Following dissemination of imaging guidelines within the Michigan Urological Surgery Improvement Collaborative (MUSIC) for men with newly diagnosed prostate cancer, MUSIC set a target of imaging < 10% of patients for which bone scan is not indicated. We compared four SPC methods using Monte Carlo simulation: p-chart, weighted binomial CUSUM, Bernoulli cumulative sum (CUSUM), and exponentially weighted moving average (EWMA). We simulated non-indicated bone scan rates ranging from 5.9% (within target) to 11.4% (above target) for a representative MUSIC practice. Sensitivity was determined using the average run length (ARL), the time taken to signal a change. We then plotted actual non-indicated bone scan rates for a representative MUSIC practice using each SPC method to qualitatively assess graphical interpretation.
EWMA had the lowest ARL and was able to detect changes significantly earlier than the other SPC methodologies (p < 0.001). The p-chart had the highest ARL and thus detected changes slowest (p < 0.001). EWMA and p-charts were easier to interpret graphically than CUSUM methods due to their ability to display historical imaging rates.
SPC methods can be used to provide informative and timely feedback regarding adherence to healthcare performance target rates in quality improvement collaboratives. We found the EWMA method most suited for detecting changes in imaging utilization.

The IAEA is currently coordinating a multi-year project to update the TRS-398 Code of Practice for the dosimetry of external beam radiotherapy based on standards of absorbed dose to water. One major aspect of the project is the determination of new beam quality correction factors, k Q , for megavoltage photon beams consistent with developments in radiotherapy dosimetry and technology since the publication of TRS-398 in 2000. Specifically, all values must be based on, or consistent with, the key data of ICRU Report 90. Data sets obtained from Monte Carlo (MC) calculations by advanced users and measurements at primary standards laboratories have been compiled for 23 cylindrical ionization chamber types, consisting of 725 MC-calculated and 179 experimental data points. These have been used to derive consensus k Q values as a function of the beam quality index TPR20,10 with a combined standard uncertainty of 0.6%. Mean values of MC-derived chamber-specific [Formula: see text] factors for cylindrical and plane-parallel chamber types in 60Co beams have also been obtained with an estimated uncertainty of 0.4%.

To address the situation where the complete consistency is unnecessary, a stepwise optimization model-based method for testing the acceptably additive consistency (AAC) of hesitant fuzzy preference relations (HFPRs) is introduced. Then, an AAC concept for HFPRs is defined. Meanwhile, incomplete HFPRs (iHFPRs) are discussed and a series of optimization models to acquire complete HFPRs is constructed. If the consistency is unacceptable, an optimization model for revising unacceptably consistent HFPRs under the conditions of the AAC and maximizing the ordinal consistency (OC) is offered. Subsequently, a model for minimizing the number of adjusted variables is presented. Considering the weighting information and the consensus for group decision making (GDM), the weights of fuzzy preference relations (FPRs) obtained from each individual HFPR and the decision makers (DMs) are determined using the distance measure. With regard to the consensus, two models for reaching the consensus requirement and minimizing the amount of revised variables are separately constructed, which are both based on the analysis of maximizing the OC. Furthermore, the thresholds of the additive consistency and the consensus are studied using the Monte Carlo simulation method. A GDM algorithm with HFPRs is offered. Finally, an example and comparison are provided to show the efficiency of the new procedure.

Intensified viral load monitoring for pregnant and breastfeeding women has been proposed to help address concerns around antiretroviral therapy (ART) adherence, viraemia and transmission risk, but there have been no systematic evaluations of existing policies.
We used an individual Monte Carlo simulation to describe longitudinal ART adherence and viral load from conception until 2 years\' postpartum. We applied national and international guidelines for viral load monitoring to the simulated data. We compared guidelines on the percentage of women receiving viral load monitoring and the percentage of women monitored at the time of elevated viral load.
Coverage of viral load monitoring in pregnancy and breastfeeding varied markedly, with between 14% and 100% of women monitored antenatally and 38-98% monitored during breastfeeding. Specific recommendations for testing at either a fixed gestation or a short, fixed period after ART initiation achieved more than 95% testing in pregnancy but this was much lower (14-83%) among guidelines with no special stipulations. By the end of breastfeeding, only a small proportion of simulated episodes of elevated viral load more than 1000 copies/ml were successfully detected by monitoring (range, 20-50%).
Although further research is needed to understand optimal viral load frequency and timing in this population, these results suggest that current policies yield suboptimal detection of elevated viral load in pregnant and breastfeeding women.