Comparison of 3DCRT and IMRT out-of-field doses in pediatric patients using Monte Carlo simulations with treatment planning system calculations and measurements

Purpose: Out-of-field doses are given to healthy tissues, which may allow the development of second tumors. The use of IMRT in pediatric patients has been discussed, as it leads to a "bath" of low doses to large volumes of out-of-field organs and tissues. This study aims to compare out-of-field doses in pediatric patients comparing IMRT and 3DCRT techniques using measurements, Monte Carlo (MC) simulations, and treatment planning system (TPS) calculations. Materials and methods: A total dose of 54 Gy was prescribed to a PTV in the brain of a pediatric anthropomorphic phantom, for both techniques. To assess the out-of-field organ doses for both techniques, two treatment plans were performed with the 3DCRT and IMRT techniques in TPS. Measurements were carried out in a LINAC using a pediatric anthropomorphic phantom and thermoluminescent dosimeters to recreate the treatment plans, previously performed in the TPS. A computational model of a LINAC, the associated multileaf collimators, and a voxelized pediatric phantom implemented in the Monte Carlo N-Particle 6.1 computer program were also used to perform MC simulations of the out-of-field organ doses, for both techniques. Results: The results obtained by measurements and MC simulations indicate a significant increase in dose using the IMRT technique when compared to the 3DCRT technique. More specifically, measurements show higher doses with IMRT, namely, in the right eye (13,041 vs. 593 mGy), left eye (6,525 vs. 475 mGy), thyroid (79 vs. 70 mGy), right lung (37 vs. 28 mGy), left lung (27 vs. 20 mGy), and heart (31 vs. 25 mGy). The obtained results indicate that out-of-field doses can be seriously underestimated by TPS. Discussion: This study presents, for the first time, out-of-field dose measurements in a realistic scenario and calculations for IMRT, centered on a voxelized pediatric phantom and an MC model of a medical LINAC, including MLC with log file-based simulations. The results pinpoint significant discrepancies in out-of-field doses for the two techniques and are a cause of concern because TPS calculations cannot accurately predict such doses. The obtained doses may presumably increase the risk of the development of second tumors.info:eu-repo/semantics/publishedVersio

Optimizing time-in-target-range assessment for blood pressure: insights from a large-scale study with continual cuffless monitoring

IntroductionBlood pressure (BP) time-in-target-range (TTR) is an emerging predictor of cardiovascular risk. Conventional BP methods are fundamentally unable to provide an optimal assessment of TTR, using irregular measurements separated by lengthy intervals. We investigated the optimal duration and frequency for reliable, practical TTR assessment in clinical settings using continual monitoring.MethodsThis retrospective study analyzed 2.3 million BP readings from 5,189 European home users (55 ± 11 years, 82% male, BMI 28.0 ± 5.8) using a cuffless BP monitor (Aktiia SA). Systolic BP (SBP) data over 15 consecutive days were assessed (29 ± 11 readings/subject/24-h; 434 + 132 readings/subject/15-day). Subjects were classified into risk-related TTR groups based on 15-day SBP data (24-h, target 90–125 mmHg; ≥6 daytime readings). Various measurement frequencies and durations (1–14 days; 24-h/daytime; 2, 4 or ≥ 6 readings/day) were compared to this reference. Two specific configurations paralleling ambulatory (“One-Day-24 h”) and home (“One-Week-Daytime”) BP monitoring were selected for detailed analysis.ResultsThe reference TTR classified 63.0% of the subjects as high risk, 19.0% intermediate, and 18.0% low. “One-Day-24 h” schedule inaccurately classified 26% of subjects compared to the reference TTR, and “One-Week-Daytime” schedule inaccurately classified 45%. Classification accuracy with both schedules was high for subjects with very low or very high reference TTR, but poor otherwise. Accuracy of ≥90% in TTR classification only occurred with 7 days of continual 24-h monitoring.DiscussionFor the first time, with the benefit of a cuffless device that measures BP with sufficient frequency and duration, practical use of TTR is enabled as a potentially enhanced metric to manage hypertension

Description of New Morphological Variation of Culex (Culex) coronator Dyar and Knab, 1906 and First Report of Culex (Carrollia) bonnei Dyar, 1921 Found in the Central Region of Peru

Publisher Copyright: © The Author(s) 2024.Mosquitoes (Diptera: Culicidae) pose a significant threat to public health worldwide, especially in tropical and subtropical regions, where they act as primary vectors in transmission of infectious agents. In Peru, 182 culicid species have been identified and several species of the genus Culex are known to transmit arboviruses. However, knowledge of mosquito diversity and distribution remains limited, with many studies focusing on specific regions only. Here, we describe a new morphological variation of Cx. (Culex) coronator Dyar and Knab, 1906, and report the presence of Culex (Carrollia) bonnei Dyar, 1921 in the central region of Peru, Huanuco. Specimens were obtained through larvae collections and identified through morphologic characterization, including dissection of male genitalia, and molecular analyses. In total, 17 mosquitoes were analyzed, and the genitalia of the male specimens allowed the identification of Cx. coronator and Cx. bonnei. Partial sequences of the CoxI gene corresponding to these two species were obtained (N = 10). Phylogenetic analysis revealed that the sequences of Cx. coronator grouped in a monophyletic clade with sequences ascribed to other species corresponding to the subgenus Carrollia, while Cx. bonnei specimens formed a monophyletic clade with homologous sequences from GenBank. This study underscores the importance of continued efforts to study the diversity and distribution of mosquitoes in Peru, including their potential role as vectors of human pathogens, to underpin effective disease control and prevention strategies, highlighting the importance of a complemented morphological and molecular analysis.publishersversionpublishe

TH\"OR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Robot Interaction

We present a new large dataset of indoor human and robot navigation and interaction, called TH\"OR-MAGNI, that is designed to facilitate research on social navigation: e.g., modelling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. TH\"OR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, TH\"OR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human-human and human-robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. TH\"OR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human-robot interactions.Comment: Submitted to The International Journal of Robotics Research (IJRR) on 28 of February 202

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Background: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. Objectives: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. Methods: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). Results: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. Conclusion: Machine learning–based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI