Optimal control strategies for toxoplasmosis disease transmission dynamics via harmonic mean-type incident rate.

Toxoplasma infection in humans is considered due to direct contact with infected cats. Toxoplasma infection (an endemic disease) has the potential to affect various organs and systems (brain, eyes, heart, lungs, liver, and lymph nodes). Bilinear incidence rate and constant population (birth rate is equal to death rate) are used in the literature to explain the dynamics of Toxoplasmosis disease transmission in humans and cats. The goal of this study is to consider the mathematical model of Toxoplasma disease with harmonic mean type incident rate and also consider that the population of humans and cats is not equal (birth rate and the death rate are not equal). In examining Toxoplasma transmission dynamics in humans and cats, harmonic mean incidence rates are better than bilinear incidence rates. The disease dynamics are first schematically illustrated, and then the law of mass action is applied to obtain nonlinear ordinary differential equations (ODEs). Analysis of the boundedness, positivity, and equilibrium points of the system has been analyzed. The reproduction number is calculated using the next-generation matrix technique. The stability of disease-free and endemic equilibrium are analyzed. Sensitivity analysis is also done for reproduction number. Numerical simulation shows that the infection is spread in the population when the contact rate ß h and ß c increases while the infection is reduced when the recovery rate δ h increases. This study investigates the impact of various optimal control strategies, such as vaccinations for the control of disease and the awareness of disease awareness, on the management of disease.

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and assessment of existing guidelines.

BACKGROUND: The SAVVY project aims to improve the analyses of adverse events (AEs) in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). This paper summarizes key features and conclusions from the various SAVVY papers. METHODS: Summarizing several papers reporting theoretical investigations using simulations and an empirical study including randomized clinical trials from several sponsor organizations, biases from ignoring varying follow-up times or CEs are investigated. The bias of commonly used estimators of the absolute (incidence proportion and one minus Kaplan-Meier) and relative (risk and hazard ratio) AE risk is quantified. Furthermore, we provide a cursory assessment of how pertinent guidelines for the analysis of safety data deal with the features of varying follow-up time and CEs. RESULTS: SAVVY finds that for both, avoiding bias and categorization of evidence with respect to treatment effect on AE risk into categories, the choice of the estimator is key and more important than features of the underlying data such as percentage of censoring, CEs, amount of follow-up, or value of the gold-standard. CONCLUSIONS: The choice of the estimator of the cumulative AE probability and the definition of CEs are crucial. Whenever varying follow-up times and/or CEs are present in the assessment of AEs, SAVVY recommends using the Aalen-Johansen estimator (AJE) with an appropriate definition of CEs to quantify AE risk. There is an urgent need to improve pertinent clinical trial reporting guidelines for reporting AEs so that incidence proportions or one minus Kaplan-Meier estimators are finally replaced by the AJE with appropriate definition of CEs.

An Augmented Reality Visualization System for Simulated Multirotor Aerial Vehicles.

Multirotors Aerial Vehicles are special class of Unmanned Aerial Vehicles with many practical applications. The growing demand for this class of aircraft requires tools that speed up their development. Simulated environments have gained increasing importance, as they facilitate testing and prototyping solutions, where virtual environments allow real-time interaction with simulated models, with similar behavior to real systems. More recently, the use of Augmented Reality has allowed an increasing experience of immersion and integration between the virtual world and a real scenario. This work proposes the use of Augmented Reality technology and a simulated model of a multirotor to create an interactive flight environment, aiming to improve the user experience in the analysis of simulated models. For this purpose, a smartphone was adopted as a hardware platform, a game engine is used as a basis for the development of the Augmented Reality application, that represents a numerical simulation of the flight dynamics and the control system of a multirotor, and a game controller is adopted for user interaction. The resulting system demonstrates that Augmented Reality is a viable technology that can be used to increase the possibilities of evaluating simulated systems.

Preliminary evaluation of the toxicological, antioxidant and antitumor activities promoted by the compounds 2,4-dihydroxy-benzylidene-thiosemicarbazones an in silico, in vitro and in vivo study.

Thiosemicarbazones are promising classes of compounds with antitumor activity. For this study, six 2,4-dihydroxy-benzylidene-thiosemicarbazones compounds were synthesized. These compounds were submitted to different assays in silico, in vitro and in vivo to evaluate the toxicological, antioxidant and antitumor effects. The in silico results were evaluated by the SwissADME and pkCSM platforms and showed that all compounds had good oral bioavailability profiles. The in vitro and in vivo toxicity assays showed that the compounds showed low cytotoxicity against different normal cells and did not promote hemolytic effects. The single dose acute toxicity test (2000 mg/kg) showed that none of the compounds were toxic to mice. In in vitro antioxidant activity assays, the compounds showed moderate to low activity, with PB17 standing out for the ABTS radical capture assay. The in vivo antioxidant activity highlighted the compounds 1, 6 and 8 that promoted a significant increase in the concentration of liver antioxidant enzymes. Finally, all compounds showed promising antitumor activity against different cell lines, especially MCF-7 and DU145 lines, in addition, they inhibited the growth of sarcoma 180 at concentrations lower than 50 mg/kg. These results showed that the evaluated compounds can be considered as potential antitumor agents.

Improved algorithm for generating evenly-spaced streamlines from an orientation field on a triangulated surface.

BACKGROUND: Vector fields such as cardiac fiber orientation can be visualized on a surface using streamlines. The application of evenly-spaced streamline generation to the construction of interconnected cable structure for cardiac propagation models has more stringent requirements imperfectly fulfilled by current algorithms. METHOD: We developed an open-source C++/python package for the placement of evenly-spaced streamlines on a triangulated surface. The new algorithm improves upon previous works by more accurately handling streamline extremities, U-turns and limit cycles, by providing stronger geometrical guarantees on inter-streamline minimal distance, particularly when a high density of streamlines (up to 10µm spacing) is desired, and by making a more efficient parallel implementation available. The approach requires finding intersections between geometrical capsules and triangles to update an occupancy mask defined on the triangles. This enables fast streamline integration from thousands of seed points to identify optimal streamline placement. RESULTS: The algorithm was assessed qualitatively on different left atrial models of fiber orientation with varying mesh resolutions (up to 375k triangles) and quantitatively by measuring streamline lengths and distribution of inter-streamline minimal distance. The complexity and the computational performance of the algorithm were studied as a function of streamline spacing in relation to triangular mesh resolution. CONCLUSION: More accurate geometrical computations, attention to details and fine-tuning led to an algorithm more amenable to applications that require precise positioning of streamlines.

Making Predictions Using Poorly Identified Mathematical Models.

Many commonly used mathematical models in the field of mathematical biology involve challenges of parameter non-identifiability. Practical non-identifiability, where the quality and quantity of data does not provide sufficiently precise parameter estimates is often encountered, even with relatively simple models. In particular, the situation where some parameters are identifiable and others are not is often encountered. In this work we apply a recent likelihood-based workflow, called Profile-Wise Analysis (PWA), to non-identifiable models for the first time. The PWA workflow addresses identifiability, parameter estimation, and prediction in a unified framework that is simple to implement and interpret. Previous implementations of the workflow have dealt with idealised identifiable problems only. In this study we illustrate how the PWA workflow can be applied to both structurally non-identifiable and practically non-identifiable models in the context of simple population growth models. Dealing with simple mathematical models allows us to present the PWA workflow in a didactic, self-contained document that can be studied together with relatively straightforward Julia code provided on GitHub . Working with simple mathematical models allows the PWA workflow prediction intervals to be compared with gold standard full likelihood prediction intervals. Together, our examples illustrate how the PWA workflow provides us with a systematic way of dealing with non-identifiability, especially compared to other approaches, such as seeking ad hoc parameter combinations, or simply setting parameter values to some arbitrary default value. Importantly, we show that the PWA workflow provides insight into the commonly-encountered situation where some parameters are identifiable and others are not, allowing us to explore how uncertainty in some parameters, and combinations of parameters, regardless of their identifiability status, influences model predictions in a way that is insightful and interpretable.

A Pharmacogenomics-Based In Silico Investigation of Opioid Prescribing in Post-operative Spine Pain Management and Personalized Therapy.

Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP ( https://www.ncbi.nlm.nih.gov/snp/ ), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein-protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene-miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein-Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, L-Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein-Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information.

A two-timescale model of plankton-oxygen dynamics predicts formation of oxygen minimum zones and global anoxia.

Decline of the dissolved oxygen in the ocean is a growing concern, as it may eventually lead to global anoxia, an elevated mortality of marine fauna and even a mass extinction. Deoxygenation of the ocean often results in the formation of oxygen minimum zones (OMZ): large domains where the abundance of oxygen is much lower than that in the surrounding ocean environment. Factors and processes resulting in the OMZ formation remain controversial. We consider a conceptual model of coupled plankton-oxygen dynamics that, apart from the plankton growth and the oxygen production by phytoplankton, also accounts for the difference in the timescales for phyto- and zooplankton (making it a "slow-fast system") and for the implicit effect of upper trophic levels resulting in density dependent (nonlinear) zooplankton mortality. The model is investigated using a combination of analytical techniques and numerical simulations. The slow-fast system is decomposed into its slow and fast subsystems. The critical manifold of the slow-fast system and its stability is then studied by analyzing the bifurcation structure of the fast subsystem. We obtain the canard cycles of the slow-fast system for a range of parameter values. However, the system does not allow for persistent relaxation oscillations; instead, the blowup of the canard cycle results in plankton extinction and oxygen depletion. For the spatially explicit model, the earlier works in this direction did not take into account the density dependent mortality rate of the zooplankton, and thus could exhibit Turing pattern. However, the inclusion of the density dependent mortality into the system can lead to stationary Turing patterns. The dynamics of the system is then studied near the Turing bifurcation threshold. We further consider the effect of the self-movement of the zooplankton along with the turbulent mixing. We show that an initial non-uniform perturbation can lead to the formation of an OMZ, which then grows in size and spreads over space. For a sufficiently large timescale separation, the spread of the OMZ can result in global anoxia.

Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.

Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.

How do students reason about statistical sampling with computer simulations? An integrative review from a grounded cognition perspective.

Interactive computer simulations are commonly used as pedagogical tools to support students' statistical reasoning. This paper examines whether and how these simulations enable their intended effects. We begin by contrasting two theoretical frameworks-dual processes and grounded cognition-in the context of people's conceptions about statistical sampling, setting the stage for the potential benefits of simulations in learning such conceptions. Then, we continue with reviewing the educational literature on statistical sampling simulations. Our review tentatively suggests benefits of the simulations for building statistical habits of mind. However, challenges seem to persist when more specific concepts and skills are investigated. With and without simulations, students have difficulty forming an aggregate view of data, interpreting sampling distributions, showing a process-based understanding of the law of large numbers, making statistical inferences, and context-independent reasoning. We propose that grounded cognition offers a framework for understanding these findings, highlighting the bidirectional relationship between perception and conception, perceptual design features, and guided perceptual routines for supporting students' meaning making from simulations. Finally, we propose testable instructional strategies for using simulations in statistics education.

Virtual reality simulation training in laparoscopic surgery - does it really matter, what simulator to use? Results of a cross-sectional study.

BACKGROUND: Virtual reality simulation training plays a crucial role in modern surgical training, as it facilitates trainees to carry out surgical procedures or parts of it without the need for training "on the patient". However, there are no data comparing different commercially available high-end virtual reality simulators. METHODS: Trainees of an international gastrointestinal surgery workshop practiced in different sequences on LaparoS® (VirtaMed), LapSim® (Surgical Science) and LapMentor III® (Simbionix) eight comparable exercises, training the same basic laparoscopic skills. Simulator based metrics were compared between an entrance and exit examination. RESULTS: All trainees significantly improved their basic laparoscopic skills performance, regardless of the sequence in which they used the three simulators. Median path length was initially 830 cm and 463 cm on the exit examination (p < 0.001), median time taken improved from 305 to 167 s (p < 0.001). CONCLUSIONS: All Simulators trained efficiently the same basic surgery skills, regardless of the sequence or simulator used. Virtual reality simulation training, regardless of the simulator used, should be incorporated in all surgical training programs. To enhance comparability across different types of simulators, standardized outcome metrics should be implemented.

A haptic guidance system for simulated catheter navigation with different kinaesthetic feedback profiles.

BACKGROUND: This paper proposes a haptic guidance system to improve catheter navigation within a simulated environment. METHODS: Three force profiles were constructed to evaluate the system: collision prevention; centreline navigation; and a novel force profile of reinforcement learning (RL). All force profiles were evaluated from the left common iliac to the right atrium. RESULTS: Our findings show that providing haptic feedback improved surgical safety compared to visual-only feedback. If staying inside the vasculature is the priority, RL provides the safest option. It is also shown that the performance of each force profile varies in different anatomical regions. CONCLUSION: The implications of these findings are significant, as they hold the potential to improve how and when haptic feedback is applied for cardiovascular intervention.

A computational analysis of the role of integrins and Rho-GTPases in the emergence and disruption of apical-basal polarization in renal epithelial cells.

Apical-basal polarization in renal epithelial cells is crucial to renal function and an important trigger for tubule formation in kidney development. Loss of polarity can induce epithelial-to-mesenchymal transition (EMT), which can lead to kidney pathologies. Understanding the relative and combined roles of the involved proteins and their interactions that govern epithelial polarity may provide insights for controlling the process of polarization via chemical or mechanical manipulations in an in vitro or in vivo setting. Here, we developed a computational framework that integrates several known interactions between integrins, Rho-GTPases Rho, Rac and Cdc42, and polarity complexes Par and Scribble, to study their mutual roles in the emergence of polarization. The modeled protein interactions were shown to induce the emergence of polarized distributions of Rho-GTPases, which in turn led to the accumulation of apical and basal polarity complexes Par and Scribble at their respective poles, effectively recapitulating polarization. Our multiparametric sensitivity analysis suggested that polarization depends foremost on the mutual inhibition between Rac and Rho. Next, we used the computational framework to investigate the role of integrins and GTPases in the generation and disruption of polarization. We found that a minimum concentration of integrins is required to catalyze the process of polarization. Furthermore, loss of polarization was found to be only inducible via complete degradation of the Rho-GTPases Rho and Cdc42, suggesting that polarization is fairly stable once it is established. Comparison of our computational predictions against data from in vitro experiments in which we induced EMT in renal epithelial cells while quantifying the relative Rho-GTPase levels, displayed that EMT coincides with a large reduction in the Rho-GTPase Rho. Collectively, these results demonstrate the essential roles of integrins and Rho-GTPases in the establishment and disruption of apical-basal polarity and thereby provide handles for the in vitro or in vivo regulation of polarity.

Interactions-based classification of a single microbial sample.

To address the limitation of overlooking crucial ecological interactions due to relying on single time point samples, we developed a computational approach that analyzes individual samples based on the interspecific microbial relationships. We verify, using both numerical simulations as well as real and shuffled microbial profiles from the human oral cavity, that the method can classify single samples based on their interspecific interactions. By analyzing the gut microbiome of people with autistic spectrum disorder, we found that our interaction-based method can improve the classification of individual subjects based on a single microbial sample. These results demonstrate that the underlying ecological interactions can be practically utilized to facilitate microbiome-based diagnosis and precision medicine.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients.

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Application of fused graphical lasso to statistical inference for multiple sparse precision matrices.

In this paper, the fused graphical lasso (FGL) method is used to estimate multiple precision matrices from multiple populations simultaneously. The lasso penalty in the FGL model is a restraint on sparsity of precision matrices, and a moderate penalty on the two precision matrices from distinct groups restrains the similar structure across multiple groups. In high-dimensional settings, an oracle inequality is provided for FGL estimators, which is necessary to establish the central limit law. We not only focus on point estimation of a precision matrix, but also work on hypothesis testing for a linear combination of the entries of multiple precision matrices. We apply a de-biasing technology, which is used to obtain a new consistent estimator with known distribution for implementing the statistical inference, and extend the statistical inference problem to multiple populations. The corresponding de-biasing FGL estimator and its asymptotic theory are provided. A simulation study and an application of the diffuse large B-cell lymphoma data show that the proposed test works well in high-dimensional situation.

Experimental and computer study of the mechanism and identification of conditions for energy-efficient removal of moisture from materials under ultrasonic exposure.

The article is devoted to investigation of energy-efficient moisture removal from capillary-porous materials. Moisture is removed by dispersion at collapse of cylindrical cavitation bubbles, formed by ultrasonic vibrations in the capillaries of the material. Mathematical model, which allowed to investigate the mechanism of moisture dispersion, has been developed. Necessity of realization of cavitation bubble full life cycle in capillary (slow growth, rapid expansion with deformation, collapse) was found. An optimal range of sound pressure levels from 150 dB ("critical level" at which dispersion of water from capillary starts) up to 170 dB (dispersion productivity growth stops due to cavitation bubbles reaching maximum size equal to diameter of capillary) was determined. It is shown that the size of the dewatered sample for maximum drying efficiency should correspond to the ultrasonic wavelength in air. Ultrasonic dispersion of liquid during drying was confirmed experimentally. It is found that for significant reduction of drying time (up to 50% and more) it is necessary to affect in the range of 165-170 dB. And the materials to be dried must be placed as particles or layers having dimensions or thicknesses corresponding to the length of the ultrasonic wave in air. The implementation of ultrasonic drying, on the example of food products (beets) provided a reduction in drying time of 1.9 times, while reducing energy costs by 1.7 times in comparison with convective drying.

Finite Element Analysis of Pelvic Floor Biomechanical Models to Elucidate the Mechanism for Improving Urination and Defecation Dysfunction in Older Adults: Protocol for a Model Development and Validation Study.

BACKGROUND: The population is constantly aging, and most older adults will experience many potential physiological changes as they age, leading to functional decline. Urinary and bowel dysfunction is the most common obstacle in older people. At present, the analysis of pelvic floor histological changes related to aging has not been fully elucidated, and the mechanism of improving intestinal control ability in older people is still unclear. OBJECTIVE: The purpose of this study is to describe how the finite element method will be used to understand the mechanical characteristics of and physiological changes in the pelvic cavity during the rehabilitation process, providing theoretical support for the mechanism for improving urination and defecation dysfunction in older individuals. METHODS: We will collect magnetic resonance imaging (MRI) and computed tomography (CT) data of the pelvic cavity of one male and one female volunteer older than 60 years and use the finite element method to construct a 3D computer simulation model of the pelvic cavity. By simulating different physiological states, such as the Valsalva maneuver and bowel movement, we will verify the accuracy of the constructed model, investigate the effects of different neuromuscular functional changes, and quantify the impact proportions of the pelvic floor muscle group, core muscle group, and sacral nerve. RESULTS: At present, we have registered the study in the Chinese Clinical Trial Registry and collected MRI and CT data for an older male and an older female patient. Next, the construction and analysis of the finite element model will be accomplished according to the study plan. We expect to complete the construction and analysis of the finite element model by July 2024 and publish the research results by October 2025. CONCLUSIONS: Our study will build finite element models of the pelvic floor of older men and older women, and we shall elucidate the relationship between the muscles of the pelvic floor, back, abdomen, and hips and the ability of older adults to control bowel movements. The results of this study will provide theoretical support for elucidating the mechanism for improving urination and defecation dysfunction through rehabilitation. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2400080749; https://www.chictr.org.cn/showproj.html?proj=193428. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56333.

Quantification of the environmental impact of radiotherapy and associated secondary human health effects: a multi-institutional retrospective analysis and simulation.

BACKGROUND: The health-care industry is a substantial contributor to global greenhouse gas emissions, yet the specific environmental impact of radiotherapy, a cornerstone of cancer treatment, remains under-explored. We aimed to quantify the emissions associated with the delivery of radiotherapy in the USA and propose a framework for reducing the environmental impact of oncology care. METHODS: In this multi-institutional retrospective analysis and simulation study, we conducted a lifecycle assessment of external beam radiotherapy (EBRT) for ten anatomical disease sites, adhering to the International Organization for Standardization's standards ISO 14040 and ISO 14044. We analysed retrospective data from Jan 1, 2017, to Oct 1, 2023, encompassing patient and staff travel, medical supplies, and equipment and building energy use associated with the use of EBRT at four academic institutions in the USA. The primary objective was to measure the environmental impacts across ten categories: greenhouse gases (expressed as kg of carbon dioxide equivalents [CO2e]), ozone depletion, smog formation, acidification, eutrophication, carcinogenic and non-carcinogenic potential, respiratory effects, fossil fuel depletion, and ecotoxicity. Human health effects secondary to these environmental impacts were also estimated as disability-adjusted life years. We also assessed the potential benefits of hypofractionated regimens for breast and genitourinary (ie, prostate and bladder) cancers on US greenhouse gas emissions using an analytic model based on the 2014 US National Cancer Database for fractionation patterns and patient commute distances. FINDINGS: We estimated that the mean greenhouse gas emissions associated with a standard 25-fraction EBRT course were 4310 kg CO2e (SD 2910), which corresponded to 0·0035 disability-adjusted life years per treatment course. Transit and building energy usage accounted for 25·73% (1110 kg CO2e) and 73·95% of (3190 kg CO2e) of total greenhouse gas emissions, respectively, whereas supplies contributed only 0·32% (14 kg CO2e). Across the other environmental impact categories, most of the environmental impact also stemmed from patient transit and energy use within facilities, with little environmental impact contributed by supplies used. Hypofractionated treatment simulations suggested a substantial reduction in greenhouse gas emissions-by up to 42% for breast and 77% for genitourinary cancer-and environmental impacts more broadly. INTERPRETATION: This comprehensive lifecycle assessment of EBRT delineates the environmental and secondary health impacts of radiotherapy, and underscores the urgent need for sustainable practices in oncology. The findings serve as a reference for future decarbonisation efforts in cancer care and show the potential environmental benefits of modifying treatment protocols (when clinical equipoise exists). They also highlight strategic opportunities to mitigate the ecological footprint in an era of escalating climate change and increasing cancer prevalence. FUNDING: Mount Zion Health Fund.