The complexity of DNA damage by radiation follows a Gamma distribution: insights from the Microdosimetric Gamma Model

Review of the geometrical developments in GEANT4-DNA: From a biological perspective

GEANT4-DNA is an expansion of the widely utilised GEANT4 Monte Carlo toolkit. This extension focuses on modelling the physical, chemical, and biological stages of ionising radiation for radiobiological applications at cellular and DNA level interactions. To date, review papers on GEANT4-DNA focus solely on evaluating a selection of the latest developments with a greater focus on mechanistic developments rather than progress in biologically specific geometries. In this work, an overview of biological analysis and biological geometries that have been developed are discussed, highlighting the latest developments and future possible development avenues for GEANT4-DNA for this application. An overview of the biological organisation levels, namely DNA, cellular, and population levels, and how GEANT4-DNA models the physical, chemical, and biological processes are also described. This review emphasises the need for persistent development of specific biological geometry accompanied by personalised DNA damage analysis parameters dependent on the biological processes considered within a specific model. It also provides an in-depth understanding of the advances at all the biological organisation levels (DNA, cellular, and population) and the use of co-operative platforms developed to model alongside GEANT4 to provide further detailed geometries and or biological damage analysis. The developments presented have been analytically discussed along with their key findings and prospects for GEANT4-DNA. Finally, a perspective on future necessary developments is portrayed since many of the advancements in the biological analysis and biological geometries discussed have not been exploited to their full potential. The development of GEANT4-DNA, using the advances discussed in this review, provides a favourable method for the evaluation of biological damage comparable to radiobiological studies.

Effects of Radiation Dose on Lubricants: A Review of Experimental Studies

One of the key priorities in modern mechanical engineering is to ensure the safe, long-lasting operation of systems while maximizing energy efficiency and withstanding extreme and damaging conditions. This is especially relevant in environments subject to radiation, in which materials, depending on their chemical and elemental composition, exhibit specific limitations. In this review, we summarize the current state of the art in research and development regarding the performance of lubricants in radiation fields and the effectiveness of radiation-tolerant lubricated systems. We discuss the current understanding of radiation-damage mechanisms in dry-film lubricants, oils, and greases, and we summarize the experimental results of the irradiation studies performed to date. We compile and compare the established performance of various material types and chemistries in intense ionizing radiation fields. Finally, we provide an overview of future research directions, highlighting the challenges faced in irradiation studies, considerations relating to the selection of materials, new facilities and experiments aimed at advancing the field, and other issues related to the safe application and use of lubricant materials in radiation environments.

NRG Oncology White Paper on the Relative Biological Effectiveness in Proton Therapy

This position paper, led by the NRG Oncology Particle Therapy Work Group, focuses on the concept of relative biologic effect (RBE) in clinical proton therapy (PT), with the goal of providing recommendations for the next-generation clinical trials with PT on the best practice of investigating and using RBE, which could deviate from the current standard proton RBE value of 1.1 relative to photons. In part 1, current clinical utilization and practice are reviewed, giving the context and history of RBE. Evidence for variation in RBE is presented along with the concept of linear energy transfer (LET). The intertwined nature of tumor radiobiology, normal tissue constraints, and treatment planning with LET and RBE considerations is then reviewed. Part 2 summarizes current and past clinical data and then suggests the next steps to explore and employ tools for improved dynamic models for RBE. In part 3, approaches and methods for the next generation of prospective clinical trials are explored, with the goal of optimizing RBE to be both more reflective of clinical reality and also deployable in trials to allow clinical validation and interpatient comparisons. These concepts provide the foundation for personalized biologic treatments reviewed in part 4. Finally, we conclude with a summary including short- and long-term scientific focus points for clinical PT. The practicalities and capacity to use RBE in treatment planning are reviewed and considered with more biological data in hand. The intermediate step of LET optimization is summarized and proposed as a potential bridge to the ultimate goal of case-specific RBE planning that can be achieved as a hypothesis-generating tool in near-term proton trials.

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response

Computational models of tumor growth are valuable for simulating the dynamics of cancer progression and treatment responses. In particular, agent-based models (ABMs) tracking individual agents and their interactions are useful for their flexibility and ability to model complex behaviors. However, ABMs have often been confined to small domains or, when scaled up, have neglected crucial aspects like vasculature. Additionally, the integration into tumor ABMs of precise radiation dose calculations using gold-standard Monte Carlo (MC) methods, crucial in contemporary radiotherapy, has been lacking. Here, we introduce AMBER, an Agent-based fraMework for radioBiological Effects in Radiotherapy that computationally models tumor growth and radiation responses. AMBER is based on a voxelized geometry, enabling realistic simulations at relevant pre-clinical scales by tracking temporally discrete states stepwise. Its hybrid approach, combining traditional ABM techniques with continuous spatiotemporal fields of key microenvironmental factors such as oxygen and vascular endothelial growth factor, facilitates the generation of realistic tortuous vascular trees. Moreover, AMBER is integrated with TOPAS, an MC-based particle transport algorithm that simulates heterogeneous radiation doses. The impact of radiation on tumor dynamics considers the microenvironmental factors that alter radiosensitivity, such as oxygen availability, providing a full coupling between the biological and physical aspects. Our results show that simulations with AMBER yield accurate tumor evolution and radiation treatment outcomes, consistent with established volumetric growth laws and radiobiological understanding. Thus, AMBER emerges as a promising tool for replicating essential features of tumor growth and radiation response, offering a modular design for future expansions to incorporate specific biological traits.

Clustered DNA Damage and its Complexity: Tracking the History

The concept of radiation-induced clustered damage in DNA has grown over the past several decades to become a topic of considerable interest across the scientific disciplines involved in studies of the biological effects of ionizing radiation. This paper, prepared for the 70th anniversary issue of Radiation Research, traces historical development of the three main threads of physics, chemistry, and biochemical/cellular responses that led to the hypothesis and demonstration that a key component of the biological effectiveness of ionizing radiation is its characteristic of producing clustered DNA damage of varying complexities. The physics thread has roots that started as early as the 1920s, grew to identify critical nanometre-scale clusterings of ionizations relevant to biological effectiveness, and then, by the turn of the century, had produced an extensive array of quantitative predictions on the complexity of clustered DNA damage from different radiations. Monte Carlo track structure simulation techniques played a key role through these developments, and they are now incorporated into many recent and ongoing studies modelling the effects of radiation. The chemistry thread was seeded by water-radiolysis descriptions of events in water as radical-containing "spurs," demonstration of the important role of the hydroxyl radical in radiation-inactivation of cells and the difficulty of protection by radical scavengers. This led to the concept and description of locally multiply damaged sites (LMDS) for DNA double-strand breaks and other combinations of DNA base damage and strand breakage that could arise from a spur overlapping, or created in very close proximity to, the DNA. In these ways, both the physics and the chemistry threads, largely in parallel, put out the challenge to the experimental research community to verify these predictions of clustered DNA damage from ionizing radiations and to investigate their relevance to DNA repair and subsequent cellular effects. The third thread, biochemical and cell-based research, responded strongly to the challenge by demonstrating the existence and biological importance of clustered DNA damage. Investigations have included repair of a wide variety of defined constructs of clustered damage, evaluation of mutagenic consequences, identification of clustered base-damage within irradiated cells, and identification of co-localization of repair complexes indicative of complex clustered damage after high-LET irradiation, as well as extensive studies of the repair pathways involved in repair of simple double-strand breaks. There remains, however, a great deal more to be learned because of the diversity of clustered DNA damage and of the biological responses.