Dose and DNA damage modelling of diffusing alpha-emitters radiation therapy using Geant4.

PURPOSE: Diffusing alpha-emitters radiation therapy (DaRT) is a brachytherapy technique using α-particles to treat solid tumours. The high linear energy transfer (LET) and short range of α-particles make them good candidates for the targeted treatment of cancer. Treatment planning of DaRT requires a good understanding of the dose from α-particles and the other particles released in the 224Ra decay chain. METHODS: The Geant4 Monte Carlo toolkit has been used to simulate a DaRT seed to better understand the dose contribution from all particles and simulate the DNA damage due to this treatment. RESULTS: Close to the seed α-particles deliver the majority of dose, however at radial distances greater than 4 mm, the contribution of ß-particles is greater. The RBE has been estimated as a function of number of double strand breaks (DSBs) and complex DSBs. A maximum seed spacing of 5.5 mm and 6.5 mm was found to deliver at least 20 Gy RBE weighted dose between the seeds for RBEDSB and RBEcDSB respectively. CONCLUSIONS: The DNA damage changes with radial distance from the seed and has been found to become less complex with distance, which is potentially easier for the cell to repair. Close to the seed α-particles contribute the majority of dose, however the contribution from other particles cannot be neglected and may influence the choice of seed spacing.

In situ pulmonary mucus hydration assay using rotational and translational diffusion of gold nanorods with polarization-sensitive optical coherence tomography.

Significance: Assessing the nanostructure of polymer solutions and biofluids is broadly useful for understanding drug delivery and disease progression and for monitoring therapy. Aim: Our objective is to quantify bronchial mucus solids concentration (wt. %) during hypertonic saline (HTS) treatment in vitro via nanostructurally constrained diffusion of gold nanorods (GNRs) monitored by polarization-sensitive optical coherence tomography (PS-OCT). Approach: Using PS-OCT, we quantified GNR translational (DT) and rotational (DR) diffusion coefficients within polyethylene oxide solutions (0 to 3 wt. %) and human bronchial epithelial cell (hBEC) mucus (0 to 6.4 wt. %). Interpolation of DT and DR data is used to develop an assay to quantify mucus concentration. The assay is demonstrated on the mucus layer of an air-liquid interface hBEC culture during HTS treatment. Results: In polymer solutions and mucus, DT and DR monotonically decrease with increasing concentration. DR is more sensitive than DT to changes above 1.5 wt. % of mucus and exhibits less intrasample variability. Mucus on HTS-treated hBEC cultures exhibits dynamic mixing from cilia. A region of hard-packed mucus is revealed by DR measurements. Conclusions: The extended dynamic range afforded by simultaneous measurement of DT and DR of GNRs using PS-OCT enables resolving concentration of the bronchial mucus layer over a range from healthy to disease in depth and time during HTS treatment in vitro.

Investigations on the Diffusion Behavior of Smoke-Associated Aroma Compounds into the Sausage Matrix as Affected by Casing, Caliber, and Packaging.

The aim of the study was to investigate how smoke-associated flavoring substances behave during storage in Frankfurter-type sausages. The diffusion behavior of seven selected aroma substances in the sausage matrix and the influence of the packaging and the casing were examined over a storage period of 28 days. The sausages were cut into uniformly thick layers at defined time intervals and examined by headspace-solid phase microextraction-gas chromatography-mass spectrometry. In general, three different groups could be distinguished: (1) even distribution over the entire product on the first day after smoking; (2) clear concentration gradient from outside to inside on the first day of storage, which leveled out until day 28 of storage; and (3) a clear concentration gradient that remained present throughout the storage period. In addition, only small effects were found in the distribution of flavorings between two types of packaging, selected casing, or different calibers.

Development of a novel DGT passive sampler for measuring polycyclic aromatic hydrocarbons in aquatic systems.

Polycyclic aromatic hydrocarbons (PAHs) are priority pollutants and need to be measured reliably in waters and other media, to understand their sources, fate, behaviour and to meet regulatory monitoring requirements. Conventional water sampling requires large water volumes, time-consuming pre-concentration and clean-up and is prone to analyte loss or contamination. Here, for the first time, we developed and validated a novel diffusive gradients in thin-films (DGT) passive sampler for PAHs. Based on the well-known DGT principles, the sampler pre-concentrates PAHs with typical deployment times of days/weeks, with minimal sample handling. For the first time, DGT holding devices made of metal and suitable for sampling hydrophobic organic compounds were designed and tested. They minimize sorption and sampling lag times. Following tests on different binding layer resins, a MIP-DGT was preferred - the first time applying MIP for PAHs. It samples PAHs independent of pH (3.9 -8.1), ionic strength (0.01 -0.5 M) and dissolved organic matter < 20 mg L-1, making it suitable for applications across a wide range of environments. Field trials in river water and wastewater demonstrated that DGT is a convenient and reliable tool for monitoring labile PAHs, readily achieving quantitative detection of environmental levels (sub-ng and ng/L range) when coupled with conventional GC-MS or HPLC. ENVIRONMENTAL IMPLICATIONS: PAHs are carcinogenic and genotoxic compounds. They are environmentally ubiquitous and must be monitored in waters and other media. This study successfully developed a new DGT passive sampler for reliable in situ time-integrated measurements of PAHs in waters at the ng/L level. This is the first time to use passive samplers for accurate measurements of hydrophobic organic contaminants in aquatic systems without calibration, a big step forward in monitoring PAHs. The application of this new sampler will enhance our understanding of the sources, fate, behavior and ecotoxicology of PAHs, enabling improved environmental risk assessment and management of these compounds.

Radiopharmaceutical transport in solid tumors via a 3-dimensional image-based spatiotemporal model.

Lutetium-177 prostate-specific membrane antigen (177Lu-PSMA)-targeted radiopharmaceutical therapy is a clinically approved treatment for patients with metastatic castration-resistant prostate cancer (mCRPC). Even though common practice reluctantly follows "one size fits all" approach, medical community believes there is significant room for deeper understanding and personalization of radiopharmaceutical therapies. To pursue this aim, we present a 3-dimensional spatiotemporal radiopharmaceutical delivery model based on clinical imaging data to simulate pharmacokinetic of 177Lu-PSMA within the prostate tumors. The model includes interstitial flow, radiopharmaceutical transport in tissues, receptor cycles, association/dissociation with ligands, synthesis of PSMA receptors, receptor recycling, internalization of radiopharmaceuticals, and degradation of receptors and drugs. The model was studied for a range of values for injection amount (100-1000 nmol), receptor density (10-500 nmol•l-1), and recycling rate of receptors (10-4 to 10-1 min-1). Furthermore, injection type, different convection-diffusion-reaction mechanisms, characteristic time scales, and length scales are discussed. The study found that increasing receptor density, ligand amount, and labeled ligands improved radiopharmaceutical uptake in the tumor. A high receptor recycling rate (0.1 min-1) increased radiopharmaceutical concentration by promoting repeated binding to tumor cell receptors. Continuous infusion results in higher radiopharmaceutical concentrations within tumors compared to bolus administration. These insights are crucial for advancing targeted therapy for prostate cancer by understanding the mechanism of radiopharmaceutical distribution in tumors. Furthermore, measures of characteristic length and advection time scale were computed. The presented spatiotemporal tumor transport model can analyze different physiological parameters affecting 177Lu-PSMA delivery.

Modelling non-local cell-cell adhesion: a multiscale approach.

Cell-cell adhesion plays a vital role in the development and maintenance of multicellular organisms. One of its functions is regulation of cell migration, such as occurs, e.g. during embryogenesis or in cancer. In this work, we develop a versatile multiscale approach to modelling a moving self-adhesive cell population that combines a careful microscopic description of a deterministic adhesion-driven motion component with an efficient mesoscopic representation of a stochastic velocity-jump process. This approach gives rise to mesoscopic models in the form of kinetic transport equations featuring multiple non-localities. Subsequent parabolic and hyperbolic scalings produce general classes of equations with non-local adhesion and myopic diffusion, a special case being the classical macroscopic model proposed in Armstrong et al. (J Theoret Biol 243(1): 98-113, 2006). Our simulations show how the combination of the two motion effects can unfold. Cell-cell adhesion relies on the subcellular cell adhesion molecule binding. Our approach lends itself conveniently to capturing this microscopic effect. On the macroscale, this results in an additional non-linear integral equation of a novel type that is coupled to the cell density equation.

Transport Properties in Multicomponent Systems Containing Cyclodextrins and Nickel Ions.

In this work, we propose a comprehensive experimental study of the diffusion of nickel ions in combination with different cyclodextrins as carrier molecules for enhanced solubility and facilitated transport. For this, ternary mutual diffusion coefficients measured by Taylor dispersion method are reported for aqueous solutions containing nickel salts and different cyclodextrins (that is, α-CD, ß-CD, and γ-CD) at 298.15 K. A combination of Taylor dispersion and other methods, such as UV-vis spectroscopy, will be used to obtain complementary information on these systems. The determination of the physicochemical properties of these salts with CDs in aqueous solution provides information that allows us to understand solute-solvent interactions, and gives a significant contribution to understanding the mechanisms underlying diffusional transport in aqueous solutions, and, consequently, to mitigating the potential toxicity associated with these metal ions. For example, using mutual diffusion data, it is possible to estimate the number of moles of each ion transported per mole of the cyclodextrin driven by its own concentration gradient.

Coarse-grained modeling and dynamics tracking of nanoparticles diffusion in human gut mucus.

The gastrointestinal (GI) tract's mucus layer serves as a critical barrier and a mediator in drug nanoparticle delivery. The mucus layer's diverse molecular structures and spatial complexity complicates the mechanistic study of the diffusion dynamics of particulate materials. In response, we developed a bi-component coarse-grained mucus model, specifically tailored for the colorectal cancer environment, that contained the two most abundant glycoproteins in GI mucus: Muc2 and Muc5AC. This model demonstrated the effects of molecular composition and concentration on mucus pore size, a key determinant in the permeability of nanoparticles. Using this computational model, we investigated the diffusion rate of polyethylene glycol (PEG) coated nanoparticles, a widely used muco-penetrating nanoparticle. We validated our model with experimentally characterized mucus pore sizes and the diffusional coefficients of PEG-coated nanoparticles in the mucus collected from cultured human colorectal goblet cells. Machine learning fingerprints were then employed to provide a mechanistic understanding of nanoparticle diffusional behavior. We found that larger nanoparticles tended to be trapped in mucus over longer durations but exhibited more ballistic diffusion over shorter time spans. Through these discoveries, our model provides a promising platform to study pharmacokinetics in the GI mucus layer.

Label-Free Light Scattering Imaging with Purified Brownian Motion Differentiates Small Extracellular Vesicles in Cell Microenvironments.

Small extracellular vesicles (sEVs) are heterogeneous biological nanoparticles (NPs) with wide biomedicine applications. Tracking individual nanoscale sEVs can reveal information that conventional microscopic methods may lack, especially in cellular microenvironments. This usually requires biolabeling to identify single sEVs. Here, we developed a light scattering imaging method based on dark-field technology for label-free nanoparticle diffusion analysis (NDA). Compared with nanoparticle tracking analysis (NTA), our method was shown to determine the diffusion probabilities of a single NP. It was demonstrated that accurate size determination of NPs of 41 and 120 nm in diameter is achieved by purified Brownian motion (pBM), without or within the cell microenvironments. Our pBM method was also shown to obtain a consistent size estimation of the normal and cancerous plasma-derived sEVs without and within cell microenvironments, while cancerous plasma-derived sEVs are statistically smaller than normal ones. Moreover, we showed that the velocity and diffusion coefficient are key parameters for determining the diffusion types of the NPs and sEVs in a cancerous cell microenvironment. Our light scattering-based NDA and pBM methods can be used for size determination of NPs, even in cell microenvironments, and also provide a tool that may be used to analyze sEVs for many biomedical applications.

ERSegDiff: a diffusion-based model for edge reshaping in medical image segmentation.

Medical image segmentation is a crucial field of computer vision. Obtaining correct pathological areas can help clinicians analyze patient conditions more precisely. We have observed that both CNN-based and attention-based neural networks often produce rough segmentation results around the edges of the regions of interest. This significantly impacts the accuracy of obtaining the pathological areas. Without altering the original data and model architecture, further refining the initial segmentation outcomes can effectively address this issue and lead to more satisfactory results. Recently, diffusion models have demonstrated outstanding results in image generation, showcasing their powerful ability to model distributions. We believe that this ability can greatly enhance the accuracy of the reshaping results. This research proposes ERSegDiff, a neural network based on the diffusion model for reshaping segmentation borders. The diffusion model is trained to fit the distribution of the target edge area and is then used to modify the segmentation edge to produce more accurate segmentation results. By incorporating prior knowledge into the diffusion model, we can help it more accurately simulate the edge probability distribution of the samples. Moreover, we introduce the edge concern module, which leverages attention mechanisms to produce feature weights and further refine the segmentation outcomes. To validate our approach, we employed the COVID-19 and ISIC-2018 datasets for lung segmentation and skin cancer segmentation tasks, respectively. Compared with the baseline model, ERSegDiff improved the dice score by 3%-4% and 2%-4%, respectively, and achieved state-of-the-art scores compared to several mainstream neural networks, such as swinUNETR.

Spatially controlled diffusion range of tumor-associated angiogenic factors to develop a tumor model using a microfluidic resistive circuit.

Developing a tumor model with vessels has been a challenge in microfluidics. This difficulty is because cancer cells can overgrow in a co-culture system. The up-regulation of anti-angiogenic factors during the initial tumor development can hinder neovascularization. The standard method is to develop a quiescent vessel network before loading a tumor construct in an adjacent chamber, which simulates the interaction between a tumor and its surrounding vessels. Here, we present a new method that allows a vessel network and a tumor to develop simultaneously in two linked chambers. The physiological environment of these two chambers is controlled by a microfluidic resistive circuit using two symmetric long microchannels. Applying the resistive circuit, a diffusion-dominated environment with a small 2-D pressure gradient is created across the two chambers with velocity <10.9 nm s-1 and Péclet number <6.3 × 10-5. This 2-D pressure gradient creates a V-shaped velocity clamp to confine the tumor-associated angiogenic factors at pores between the two chambers, and it has two functions. At the early stage, vasculogenesis is stimulated to grow a vessel network in the vessel chamber with minimal influence from the tumor that is still developed in the adjacent chamber. At the post-tumor-development stage, the induced steep concentration gradient at pores mimics vessel-tumor interactions to stimulate angiogenesis to grow vessels toward the tumor. Applying this method, we demonstrate that vasculogenic vessels can grow first, followed by stimulating angiogenesis. Angiogenic vessels can grow into stroma tissue up to 1.3 mm long, and vessels can also grow into or wrap around a 625 µm tumor spheroid or a tumor tissue developed from a cell suspension. In summary, our study suggests that the interactions between a developing vasculature and a growing tumor must be controlled differently throughout the tissue development process, including at the early stage when vessels are still forming and at the later stage when the tumor needs to interact with the vessels.

X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis.

Objective. Digital breast tomosynthesis (DBT) has significantly improved the diagnosis of breast cancer due to its high sensitivity and specificity in detecting breast lesions compared to two-dimensional mammography. However, one of the primary challenges in DBT is the image blur resulting from x-ray source motion, particularly in DBT systems with a source in continuous-motion mode. This motion-induced blur can degrade the spatial resolution of DBT images, potentially affecting the visibility of subtle lesions such as microcalcifications.Approach. We addressed this issue by deriving an analytical in-plane source blur kernel for DBT images based on imaging geometry and proposing a post-processing image deblurring method with a generative diffusion model as an image prior.Main results. We showed that the source blur could be approximated by a shift-invariant kernel over the DBT slice at a given height above the detector, and we validated the accuracy of our blur kernel modeling through simulation. We also demonstrated the ability of the diffusion model to generate realistic DBT images. The proposed deblurring method successfully enhanced spatial resolution when applied to DBT images reconstructed with detector blur and correlated noise modeling.Significance. Our study demonstrated the advantages of modeling the imaging system components such as source motion blur for improving DBT image quality.

Rocking- and diffusion-based culture of tumor spheroids-on-a-chip.

Tumor spheroids are now intensively investigated toward preclinical and clinical applications, necessitating the establishment of accessible and cost-effective methods for routine operations. Without losing the advantage of organ-chip technologies, we developed a rocking system for facile formation and culture of tumor spheroids in hydrogel microwells of a suspended membrane under microfluidic conditions. While the rocking is controlled with a step motor, the microfluidic device is made of two plastic plates, allowing plugging directly syringe tubes with Luer connectors. Upon injection of the culture medium into the tubes and subsequent rocking of the chip, the medium flows back and forth in the channel underneath the membrane, ensuring a diffusion-based culture. Our results showed that such a rocking- and diffusion-based culture method significantly improved the quality of the tumor spheroids when compared to the static culture, particularly in terms of growth rate, roundness, junction formation and compactness of the spheroids. Notably, dynamically cultured tumor spheroids showed increased drug resistance, suggesting alternative assay conditions. Overall, the present method is pumpless, connectionless, and user-friendly, thereby facilitating the advancement of tumor-spheroid-based applications.

MAM-E: Mammographic Synthetic Image Generation with Diffusion Models.

Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approach, the high quality of the generated images, and their relatively less complex training process compared with Generative Adversarial Networks. Still, the implementation of such models in the medical domain remains at an early stage. In this work, we propose exploring the use of diffusion models for the generation of high-quality, full-field digital mammograms using state-of-the-art conditional diffusion pipelines. Additionally, we propose using stable diffusion models for the inpainting of synthetic mass-like lesions on healthy mammograms. We introduce MAM-E, a pipeline of generative models for high-quality mammography synthesis controlled by a text prompt and capable of generating synthetic mass-like lesions on specific regions of the breast. Finally, we provide quantitative and qualitative assessment of the generated images and easy-to-use graphical user interfaces for mammography synthesis.

A multifunctional CaCO3 bioreactor coated with coordination polymers enhances cancer immunotherapy.

Designing effective nanomedicines to induce durable anti-tumor immunity represents a promising strategy for improving moderate immune stimulation. In this study, we engineered a multifunctional nanoreactor (named SCGFP NPs) for remodeling the tumor microenvironment (TME) to improve the therapeutic efficacy of immunotherapy. The core of SCGFP NPs consists of CaCO3 loaded with SN38, prepared by the gas diffusion method, and coated with a significant amount of gallic acid-Fe3+-PEG coordination polymer on the surface. In the acidic TME, SCGFP NPs explosively release exogenous Ca2+ and SN38. The SN38-induced intracellular Ca2+ accumulation and exogenous Ca2+ synergistically trigger immunogenic cell death (ICD) through sustained Ca2+ overload. The ablation of tumors with high-intensity photothermal therapy (PTT) by near-infrared (NIR) irradiation of GA-Fe3+ induces tumor cell necrosis, further enhancing ICD activation. Additionally, SN38 upregulates PD-L1, amplifying tumor responsiveness to immune checkpoint inhibitors (ICIs). This study indicates that SCGFP NPs, through the integration of a trimodal therapeutic strategy, hold enormous potential for various types of tumor immunotherapy through distinct mechanisms or synergistic effects.

Self-organization of PIP3 waves is controlled by the topology and curvature of cell membranes.

Phosphatidylinositol (3,4,5)-trisphosphate (PIP3) is a signaling lipid on the plasma membrane that plays a fundamental role in cell signaling with a strong impact on cell physiology and diseases. It is responsible for the protruding edge formation, cell polarization, macropinocytosis, and other membrane remodeling dynamics in cells. It has been shown that the membrane confinement and curvature affects the wave formation of PIP3 and F-actin. But, even in the absence of F-actin, a complex self-organization of the spatiotemporal PIP3 waves is observed. In recent findings, we have shown that these waves can be guided and pinned on strongly bended Dictyostelium membranes caused by molecular crowding and curvature-limited diffusion. Based on these experimental findings, we investigate the spatiotemporal PIP3 wave dynamics on realistic three-dimensional cell-like membranes to explore the effect of curvature-limited diffusion, as observed experimentally. We use an established stochastic reaction-diffusion model with enzymatic Michaelis-Menten-type reactions that mimics the dynamics of Dictyostelium cells. As these cells mimic the three-dimensional shape and size observed experimentally, we found that the PIP3 wave directionality can be explained by a Hopf-like and a reverse periodic-doubling bifurcation for uniform diffusion and curvature-limited diffusion properties. Finally, we compare the results with recent experimental findings and discuss the discrepancy between the biological and numerical results.

Convection-Enhanced Drug Delivery: Experimental and Analytical Studies of Infusion Behavior in an In Vitro Brain Surrogate.

Convection-enhanced drug delivery (CED) directly infuses drugs with a large molecular weight toward target cells as a therapeutic strategy for neurodegenerative diseases and brain cancers. Despite the success of many previous in vitro experiments on CED, challenges still remain. In particular, a theoretical predictive model is needed to form a basis for treatment planning, and developing such a model requires well-controlled injection tests that can rigorously capture the convective (advective) and diffusive transport of an infusate. For this purpose, we investigated the advection-diffusion transport of an infusate (bromophenol blue solution) in the brain surrogate (0.2% w/w agarose gel) at different injection rates, ranging from 0.25 to 4 µL/min, by closely monitoring changes in the color intensity, propagation distance, and injection pressures. One dimensional closed-form solution was examined with two variable sets, such as the mathematically calculated coefficient of molecular diffusion and average velocity, and the hydraulic dispersion coefficient and seepage velocity by the least squared method. As a result, the seepage velocity was greater than the average velocity to some extent, particularly for the later infusion times. The poroelastic deformation in the brain surrogate might lead to changes in porosity, and consequently, slight increases in the actual flow velocity as infusion continues. The limitation of efficiency of the single catheter was analyzed by dimensionless analysis. Lastly, this study suggests a simple but robust approach that can properly capture the convective (advective) and diffusive transport of an infusate in an in vitro brain surrogate via well-controlled injection tests.

Numerical study of doxorubicin transdermal delivery for breast cancer treatment using microneedles.

The lack of in vivo studies on the delivery of doxorubicin within human skin, especially the absence of data on the doxorubicin diffusion coefficient, has made understanding its transdermal delivery kinetics challenging. In this study, as a first step, governing equations and finite element methods were employed to reproduce Franz diffusion cell experiment in human cadaver skin. The application of this experiment representative model with a fitting method resulted in approximate values for the diffusivity of doxorubicin across various skin layers. The estimated values were used later to conduct a comprehensive examination of doxorubicin administration for breast tumor treatments. In a 2D axisymmetric model using Fick's Law and then a microneedles array 3D model, crucial parameters effects on delivery efficiency were examined, such as the microneedle tip diameter, tip-to-tip distance, and tumor depth. As highlighted by the findings of this study, these parameters have an impact on the effectiveness of doxorubicin delivery for treating breast tumors. The focus of this research is on the potential of numerical methods in biomedical engineering, which addresses the urgent need for data on doxorubicin diffusion in human skin and offers valuable insights into optimizing drug delivery strategies for enhanced therapeutic outcomes.

Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac.

BACKGROUND: Longitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac. METHODS: Eleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution ("highRes") and signal-to-noise ratio ("highSNR"), were investigated. A test-retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers' brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers. RESULTS: Both DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations. CONCLUSIONS: High repeatability of ADC measurements in a diffusion phantom and volunteers' brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.

Using a probabilistic approach to derive a two-phase model of flow-induced cell migration.

Interstitial fluid flow is a feature of many solid tumors. In vitro experiments have shown that such fluid flow can direct tumor cell movement upstream or downstream depending on the balance between the competing mechanisms of tensotaxis (cell migration up stress gradients) and autologous chemotaxis (downstream cell movement in response to flow-induced gradients of self-secreted chemoattractants). In this work we develop a probabilistic-continuum, two-phase model for cell migration in response to interstitial flow. We use a kinetic description for the cell velocity probability density function, and model the flow-dependent mechanical and chemical stimuli as forcing terms that bias cell migration upstream and downstream. Using velocity-space averaging, we reformulate the model as a system of continuum equations for the spatiotemporal evolution of the cell volume fraction and flux in response to forcing terms that depend on the local direction and magnitude of the mechanochemical cues. We specialize our model to describe a one-dimensional cell layer subject to fluid flow. Using a combination of numerical simulations and asymptotic analysis, we delineate the parameter regime where transitions from downstream to upstream cell migration occur. As has been observed experimentally, the model predicts downstream-oriented chemotactic migration at low cell volume fractions, and upstream-oriented tensotactic migration at larger volume fractions. We show that the locus of the critical volume fraction, at which the system transitions from downstream to upstream migration, is dominated by the ratio of the rate of chemokine secretion and advection. Our model also predicts that, because the tensotactic stimulus depends strongly on the cell volume fraction, upstream, tensotaxis-dominated migration occurs only transiently when the cells are initially seeded, and transitions to downstream, chemotaxis-dominated migration occur at later times due to the dispersive effect of cell diffusion.