Interface Focus

Metastatic dissemination in lung cancer (LC) and other solid tumors is influenced not only by tumor-intrinsic biology and immune-inflammatory responses, but also by the physical properties of the vascular system through which circulating tumor cells (CTCs) migrate. Peripheral arterial disease (PAD), particularly when manifesting as aneurysmal dilation, is frequent among long-term smokers and is associated with chronic vascular inflammation and altered hemodynamics. We hypothesized that PAD-related vascular remodeling and rheological alterations may influence tumor metastatic capacity. Through a retrospective analysis of 976 patients diagnosed with both cancer and arteriopathy between 2018 and 2024, a cohort of 120 individuals with concomitant aneurysmal and neoplastic disease was identified. Demographic, biochemical, and pathological variables were examined, and metastatic burden at diagnosis was compared with that of an unselected LC population from the same institution and with literature-reported data. We focused on non-small cell lung cancer (NSCLC) as a well-characterized biological model and developed a phenomenological biophysical framework linking inflammation-driven changes in blood viscosity to metastatic competence. A Monte Carlo simulation approach was used to estimate metastasis probability under control and PAD-like rheological conditions. Despite marked male predominance and high smoking exposure, the study cohort exhibited an unexpectedly low metastatic burden, with 13.3% of patients presenting metastatic disease at diagnosis and only 7.6% showing extrathoracic dissemination, compared with an expected rate of approximately 30%. Partition analysis identified arteriopathy as the strongest predictor associated with reduced metastatic dissemination. The rheological model indicated that once inflammation exceeds a critical threshold, increased blood viscosity and disturbed flow patterns may act as a mechanical filter impairing CTC extravasation. Monte Carlo simulations supported this threshold-dependent mechanism, showing an approximately 50% reduction in predicted metastatic rates in PAD-like conditions compared with controls. Collectively, these findings suggest that chronic PAD and aneurysmal vasculopathy may reshape the circulatory microenvironment, with NSCLC providing a mechanistically interpretable framework for a transition from a metastasis-permissive to a metastasis-restrictive rheological regime.

T cells are immune cells that are known to be effective at killing cancer cells, however, an individual patients tumour-specific T cell counts are often insufficient to control cancer growths. Adoptive T cell therapy aims to address this by activating and expanding highly effective T cells ex vivo before injecting them into the patient to employ their cancer-killing functions. Recent experimental setups using activating micro-rod scaffolds have significantly improved T cell expansion over conventional methods, but there is still much to understand regarding the factors that maximise the expansion of functional T cells in these scaffolds. We present a stochastic agent-based model of T cell expansion alongside its continuum limit to simulate the average interactions between T cells and micro-rods, which enable us to explore several behaviours of the experimental system. Stochastic simulations demonstrate that T cell expansion is driven by activated cell clusters around micro-rods. Using our spatial models and a mean-field approximation, we discover that this cluster-driven expansion is most supported by scaffolds with initially homogeneous micro-rod concentrations. Our simulations also reveal that loading the T cell growth factor, interleukin-2 (IL-2), into micro-rod pores for secretion significantly prolongs expansion compared to the more conventional method of IL-2 supplementation.

BackgroundAstrocyte endfoot processes are believed to cover all micro-vessels in the brain cortex and may play a significant role in fluid and substance transport into and out of the brain parenchyma. Detailed fluid mechanical models of diffusive and advective transport in the brain are promising tools to investigate theories of transport. MethodsWe derive theoretical estimates of astrocyte endfoot sheath permeability for advective and diffusive transport and its variation in microvascular networks from mouse brain cortex. The networks are based on recently published experimental data and generated endfoot patterns are based on Voronoi tessellations of the perivascular surface. We estimate corrections for projection errors in previously published data. ResultsWe provide structural-functional relationships between vessel radius and resistance that can be directly used in flow and transport simulations. We estimate endfoot sheath filtration coefficients in the range Lp = 0.2 x 10-10 m Pa-1 s-1 to 2.7 x 10-10 m Pa-1 s-1, diffusion membrane coefficients in the range CM = 0.5 x 103 m-1 to 6 x 103 m-1, and gap area fractions in the range 0.2 % to 0.6 %. ConclusionsThe astrocyte endfoot sheath surrounding microvessels forms a secondary barrier to extra-cellular transport, separating the extra-cellular space of the parenchyma and the perivascular space outside the endothelial layer. The filtration and membrane diffusion coefficients of the endfoot sheath are estimated to be an order of magnitude lower than the extra-cellular matrix while being two orders of magnitude higher than the vessel wall.

Arterial thrombosis is initiated when mechanical forces in flowing blood exceed the activation thresholds of platelets and von Willebrand factor (vWF). Despite extensive experimental characterization of shear-induced platelet aggregation, a unified theoretical framework that maps hemodynamic forcing onto clot nucleation is lacking. Here we present Force-Gated Thrombosis (FGT), a non-equilibrium mechanical theory that treats thrombus formation as a continuous phase transition driven by an effective mechanical forcing {Sigma} ={sigma} + |{nabla}{sigma}| + {beta}{varepsilon}, which combines local wall shear stress{sigma} , shear gradient |{nabla}{sigma}|, and extensional strain rate{varepsilon} . We introduce a dimensionless Thrombosis Number {Theta} = ({Sigma}/{Sigma}c)(P/P0)m(C/C0)n, which incorporates platelet concentration P and coagulation factor concentration C, and governs the transition between stable flow ({Theta} < 1) and active clot growth ({Theta} > 1). The thrombus density is represented by a scalar order parameter{varphi} whose dynamics follow a Ginzburg- Landau free energy functional. For a simplified stenosed artery we derive an analytic closed-form thrombosis onset criterion and a critical flow rate [Formula], where{delta} is stenosis severity. Linear stability analysis shows that perturbations grow at rate{omega} (k) = {Lambda}({Theta}) - D{varphi}k2, becoming unstable when {Theta} > 1. Near threshold the clot volume fraction scales as{varphi} [~] ({Theta} - 1)1/2, a mean-field critical exponent consistent with Ginzburg- Landau theory. Systematic comparison with fifteen published experimental and computational datasets spanning shear rates from 100 to 15,000 s-1 confirms that FGT correctly predicts the existence, location, and approximate severity of pathological thrombus formation across diverse vascular geometries. The theory provides a quantitative bridge between single-molecule mechanobiology and macroscale clinical thrombosis, and yields experimentally testable predictions distinguishing FGT from purely biochemical models.

Understanding how biological shape and movement interact with surrounding fluids represents a fundamental challenge at the intersection of biology, physics, and engineering. Fish locomotion exemplifies this challenge: body morphology and swimming kinematics together determine the hydrodynamic forces and flow structures that enable efficient propulsion and maneuverability. Whereas biologists have long sought to connect morphological variation to swimming performance, traditional morphometric approaches provide limited insight into the fluid mechanical consequences of shape differences. Similarly, although computational fluid dynamics can reveal detailed flow physics, simulating hydrodynamics across diverse and dynamic morphologies remains prohibitively expensive for systematic investigation. To bridge this gap, we introduce a data-driven framework that connects fish body shape dynamics to hydro-dynamic performance through compact morphospace parameterization and reduced-order modeling. Using CFD simulations of 15 fish species from the Digital Life Project database (www.digitallife3d.org/3d-model), we generate hydrodynamic datasets capturing the shape-flow relationship. Principal Component Analysis (PCA) extracts four dominant shape parameters from dorsal body profiles, which are then integrated into an Inverse-Design with Dynamic Mode Decomposition (ID-DMD) framework to model the resulting fluid dynamics. The resulting modal analysis suggests that locomotion strategies emerge from specific shape-flow interactions. We further demonstrate the frameworks utility through single- and multi-objective shape optimization, showing how it enables efficient exploration of the morphology-hydrodynamics relationship. This approach offers a novel analysis and design tool for understanding how biological form and motion interact with fluid mechanics, with applications ranging from bio-inspired vehicle development to evolutionary biomechanics.

The formation of body axes and apical termini is crucial for animal development. In Hydra, nuclear {beta}-catenin and Wnt3 play key roles and were previously thought to be part of a single mechanism for axis and head formation. This study challenges this view by combining mathematical modeling with experimental data. We show that {beta}-catenin and Wnt3 patterning in Hydra operate at two different scales, requiring distinct inhibitory mechanisms. {beta}-catenin, possibly interacting with other Wnts, co-ordinates axis formation, whereas Wnt3 is involved in small-scale head patterning. A double-loop reaction-diffusion model was developed, demonstrating the ability to describe patterns with divergent shapes, which single-loop models could not achieve. The previously proposed threshold mechanism for Wnt3 expression based on {beta}-catenin prepatterns could not explain the data. Our results suggest a more complex patterning mechanism in other animals, where axis and head formation may not be controlled by a single process.

Background: Lung cancer in never-smokers is rising, with a substantial proportion harboring the EGFR mutation. While fine particulate matter (PM2.5) is a recognized risk factor, other intervenable pollutants and built environmental factors remain unknown. Objectives: To identify urban characteristics associated with EGFR-mutant (vs. wild-type) lung cancer using high-resolution spatiotemporal data. Methods: We analyzed 2,699 lung cancer patients with documented EGFR status treated at a high-volume academic medical center in New York City. Patient residential addresses were linked to high-resolution (300m x 300m) 5-year cumulative exposures to 3 air pollutants and 26 urban features. We developed Light Gradient Boosting Machine (LightGBM) models to classify EGFR status, comparing a basic clinical model with established predictors (Asian, female, never-smoking status, and adenocarcinoma histology) to an extended model with additional urban factors. Predictive performance was assessed based on discrimination (AUC). Results: We included 2,699 patients, of whom 54.1% were female and 25.8% self-identified as Asian, 11.2% as Black, and 7.4% as Hispanic; and 29% had EGFR-mutated cancer. The extended model showed modest improvements in discrimination (AUC: 0.775 [95% CI, 0.739-0.809] vs. 0.768 [0.723-0.811]), compared to the clinical model. Newly identified factors for EGFR-mutant status included black carbon (BC), nitrogen dioxide (NO2), proximity to airports, reduced access to public transportation, elevated noise levels, and lead exposure. Conclusions: Traffic-related pollutants (BC, NO2) from diesel engines and motor vehicles, and proximity to airports, were among the novel spatiotemporal features associated with EGFR-mutant lung cancer. These results may inform policy interventions.

Deciphering the lactate transport within the uteroplacental unit should be aided by a theoretical model in light of the insurmountable difficulties involved with in-vivo relevant measurements. Here we formulate a boundary value problem that predicts the direction and extent of lactate fluxes within the human placenta under various physiological conditions. It accounts for metabolic processes within the placenta and transporters activity at the two membranes that confine the terminal villi. Lactate concentration inside the terminal villi and its fluxes at the membranes are being computed. Under normal conditions lactate flux from fetal arterioles to the placenta surpasses the flux to the fetus via the umbilical vein. Within the placenta, it adds to the lactate that originates in the glycolysis, some of it degrades to pyruvate and surpluses are delivered to the maternal circulation. The apparent permeabilities of the placental membranes with respect to lactate as well as the specific characterizations of the placental lactate production, hitherto unknown, are being estimated. We determine the range of parameter values that induce sustainable, healthy fetal lactate levels and demonstrate the versatility of lactate exchange between the placenta and the fetus by computing the effect of extreme conditions (e.g., cesarean section, intrauterine growth restriction) on lactate fluxes.

Cancer cells in hypoxic environments often proliferate less but exhibit enhanced migration relative to their normoxic counterparts. Recent in vitro and in silico studies have characterized the role of hypoxic memory - the ability of cancer cells to retain their hypoxic phenotype even when reoxygenated - in tumor invasion. However, the observations have been limited either to exposing cancer cells to hypoxia for a fixed duration or by assuming a fixed-time persistence of the hypoxic state upon reoxygenation independent of the duration of hypoxia exposure. Thus, time-dependent cell-state changes during hypoxia and their impact on hypoxic memory remains unclear. Here, we first analyze transcriptomic data from breast cancer samples to show that the genes upregulated at transcriptional level and hypomethylated at epigenetic level are enriched in cell invasion, indicating hypoxic memory-driven process of tumor invasion. Next, we used a computational model to investigate how the spatial-temporal dynamics of oxygen levels in a tumor drive time-dependent changes in hypoxic memory and influence tumor invasion dynamics. Our simulation results show that such dynamic hypoxic memory can drive enhanced tumor invasion over a fixed hypoxic memory by a) enriching hypoxic cell density at the tumor front, b) reducing sensitivity of hypoxic cell state to fluctuations in oxygen supply, and c) enhancing effective diffusion of hypoxic cells. Our results highlight the crucial role of dynamic hypoxic memory in shaping tumor invasion dynamics, underscoring the need to elucidate its underlying mechanisms in future studies.

Coral, as a bioreactor, has to continuously interact with surrounding environment to maintain a healthy state. A multi-physics reaction engineering model has been developed to capture this interaction. The coral interior is modeled as interconnected reaction units respectively for photosynthesis, respiration, and calcification, whose reaction kinetics are influenced by environmental fluctuations. Coupling between coral and environment is realized by bi-directional mass transfer at the coral-seawater interface, with consideration of the unique flow fields induced by ciliary beating. By resorting to this comprehensive model, we discover that ciliary beating demonstrates distinctively different diurnal and nocturnal functions. During daytime, beating can help reduce photosynthetic oxygen accumulation to prevent hyperoxia-induced mortality, while enhancing carbon dioxide uptake efficiency to promote nutrient production. At night, however, beating promotes oxygen acquisition for adequate respiration, while expelling carbon dioxide to inhibit symbiotic destruction under acidic stress. The model further enables mechanistic analysis of the detrimental impact of climate change on coral health, where the influences from two key factors (i.e., temperature and CO2 level) can be decoupled. Its interesting to find out that the elevated temperature plays a dominant role during daytime, while at night the coral is dominantly influenced by rising CO2 level.

The development of large-scale, three-dimensional human tissues is crucial for various applications in therapeutic tissue engineering, disease modeling, and drug testing. However, due to the diffusion limit of oxygen, the lack of functional vascular networks is a significant limitation in maintaining these engineered tissues in the laboratory. To address this challenge, we present a systematic, model-based design process for artificial supply networks that can ensure a sufficient supply of oxygen and nutrients to engineered human tissue. Our approach combines mathematical models of fluid dynamics, cell metabolism, and network properties to identify key parameters influencing the supply performance. We demonstrate the applicability and possibilities of this design process by simulating different network structures, including cuboid and rhombic do-decahedral honeycombs, under various conditions. Our results show that the structure of the artificial supply network, oxygen concentration, and solute flow within the network strongly influence cellular metabolic activity and viability. We also examine the effects of non-uniform cell density, channel blockage, and long channel length on the oxygen distribution inside the cell-containing tissue compartment. Our findings highlight the importance of considering these factors in the design of artificial supply networks for large-scale engineered human tissues. This study provides a promising approach for quickly exploring the vast design space of possible network structures under different conditions for desired cell and tissue states, ultimately contributing to the development of more efficient and effective tissue engineering strategies.

ObjectiveFLASH-RT can potentially improve the sparing of normal tissues while preserving the tumoricidal efficiency, owing to the radiation with ultra-high dose rate. However, the FLASH mechanism remains to be solved. A popular FLASH model is based on radiolytic oxygen depletion (ROD), which explains for radiation protection of normal tissues under FLASH-RT. However, ROD does not explain the preservation of tumoricidal efficiency for tumors. This work will develop a ROS+ROD FLASH model that can explain the differential tumor and normal-tissue response. ApproachThe new FLASH model utilizes reactive oxygen species (ROS) in addition to ROD, and takes into account that ROS level decreases during FLASH-RT. Specifically, the differential-equation model takes into account that the basic ROS level is lower during FLASH-RT and the degeneration rates of ROS are different in tumor cells and healthy cells. Based on this ROS+ROD FLASH model, the surviving fractions of tumor and normal cells are respectively compared between conventional radiotherapy (CONV-RT) and FLASH-RT. Main resultsWhile ROD alone does not distinguish the response of tumors and normal tissues to FLASH-RT, the proposed new FLASH model based on ROD and ROS successfully explained the differential response of tumors and normal tissues to FLASH-RT, i.e., the preserved tumoricidal capability, which cannot be explained by ROD alone, and the extra normal-tissue protection owing to the ultra-high dose rate. SignificanceSince the ROS level decreases slower in tumors than in normal tissues, during FLASH-RT, ROS decreases more in normal tissue, thus can get more protection. By incorporating ROS in addition to ROD, the new FLASH model can not only recover all results by previous FLASH model with ROD alone, but also explain the differential response: preserved lethality of FLASH-RT to tumors and improved protection to normal tissues.

Computational models coupling one-dimensional vascular networks with three-dimensional tissue domains are widely used for predicting blood flow distribution in tumor perfusion, drug delivery, and therapeutic planning. Two prominent coupling paradigms have emerged: the Lateral Average Model (LAM) which implements distributed transmural exchange via a vessel wall conductivity parameter{gamma} (m Pa-1 s-1), and the Sphere of Influence (SOI) model, which employs localized terminal coupling via a source sphere radius{varepsilon} (m). Despite their broad application, systematic quantitative comparisons of their parametric behavior and predictive equivalence remain lacking. We compare LAM and SOI in 3D-1D simulations on a benchmark vascular network and a porcine liver study with a hepatic arterial network reconstructed from CT arteriography. Across a benchmark vascular network under three sink configurations, the LAM net flow rate rose smoothly with{gamma} and saturated at a plateau, while the SOI net flow rate increased with{varepsilon} without saturating; as a result, global-flow equivalence between the two formulations exists only for particular boundary geometries, and not at all within the tested parameter range for one of the three configurations examined. Despite this partial agreement in total flow, the two models diverged substantially in regional perfusion: in a porcine hepatic arterial network reconstructed from CT arteriography, SOI predicted stable perfusion fractions to two regions of interest across its full tested parameter range, whereas LAM predictions for the same regions varied several-fold with vessel wall permeability and, at low permeability, could invert which region received more flow. These results indicate that the choice of coupling model has limited consequence for predicted total organ flow but substantial consequence for predicted local drug delivery, and we provide guidance for selecting between the two formulations depending on the clinical or research question being asked. Author SummaryWhen doctors plan treatments for liver cancer, they often rely on computer simulations to predict how blood flows through the liver and how well a drug will reach the tumor. These simulations depend on mathematical models that describe how blood moves from vessels into surrounding tissue. Two commonly used approaches exist for building these models, but researchers have generally chosen between them based on habit or convenience rather than on a principled understanding of how their predictions differ. In this work, we directly compared these two approaches, one that spreads blood exchange continuously along the vessel wall, and one that delivers blood from the vessel tips into a surrounding spherical zone, using both a simple test network and a realistic pig liver reconstructed from medical imaging. We found that the two approaches can agree on the total amount of blood reaching the liver, but disagree substantially on where that blood goes within the tissue. This distinction matters enormously for treatment planning: a model that predicts the right total blood flow but delivers it to the wrong region of the liver could lead to an inaccurate forecast of drug concentration at the tumor site. Our results provide practical guidance for researchers on which approach to use depending on what information is available and what question is being asked.

PurposeBreast cancer is the leading cause of cancer-related death among women worldwide. Conventional chemotherapy is considered a clinical state of the art treatment; however, resistance or recurrence occurs among a considerable portion of these patients. Besides understanding the genomic alterations pattern of tumor cells and their association with drug resistance or response, the development of a reliable tumor models that reflect the major cellular and molecular features of tumors may aid with screening of candidate drugs and identification of appropriate treatment regimens. Here, we developed a simple and low-cost tumor model of breast cancer to screen library of chemotherapy agents in a pre-clinical setting. Methodswe generated and cultured ex-vivo 3D culture of patient-derived tumor spheroids from both pre-treated primary and metastatic tumors using a partial digestion approach in a microfluidic device. We assessed chemotherapy response of the seven patient-derived breast tumor spheroids and expanded evaluation of drug sensitivity through molecular analysis of a small panel of genes. ResultsWe observed various chemotherapy responses across primary and metastasis tumor samples. Interestingly, we demonstrated response to paclitaxel and doxorubicin and resistance to cisplatin in 2/3 metastatic tumor samples while most of the primary tumor were responsive to chemotherapy. Additionally, the expression of PIK3CA and loss of PTEN were associated to treatment resistance. ConclusionOur study suggests potential application of microfluidic-based cell culture technology coupled with patient derived tumor spheroids in prediction of treatment response in a personalized manner.

Exchange of molecules between cerebrospinal fluid (CSF) and brain cells contributes to brain function and protection from dementia. Despite widespread acceptance of the glymphatic theory, the route by which CSF is brought close enough to the neural tissue for solutes to be exchanged by extracellular diffusion is not entirely clear. Exogenous molecules injected into CSF are observed to reach the basement lamina that surrounds the dense capillary network. Transport of solutes by diffusion along the basement lamina, a gel of macromolcules about 100 nm thick, would be too slow; bulk flow in a static geometry would require unphysiologically high pressures. However, it is known that the pulsation of blood aids transport of CSF, and we hypothesized that this is because the pulsation intermittently squeezes the pericapillary lamina. In a primitive mimicry, we have tested whether intermittent squeezing increases flow through an agar gel. In all but one of 216 tests, pulsation caused a reversible increase, sometimes by a factor of 100 or more. The enhancement was greatest for frequencies 5-11 Hz and, over the tested range of pressure heads (20 - 50 cmH2O), was greatest for the lowest pressure. The results suggest a reason why exercise slows the aging of the brain.

The antagonistic interplay between canonical Wnt signalling and Dickkopf (Dkk) proteins is fundamental to tissue organisation, including stem cell differentiation and body-axis formation. Disruptions in this interaction are linked to various human diseases, yet the mechanisms enabling robust body-axis formation through {beta}-catenin/Wnt-Dkk interactions remain unclear. A key model system for Wnt-driven pattern formation is the pre-bilaterian organism Hydra, where two ancestral Dkk proteins interact with Wnt signalling to self-organise the body axis. While Hydra patterning has been extensively studied using the activator-inhibitor framework, a model integrating experimentally identified molecules has been lacking. Here, we introduce a mathematical model that incorporates both Dkks and their experimentally observed interactions with Wnt signalling. Numerical and analytical studies show that this network alone is sufficient to drive de novo body-axis formation across a broad parameter range. Our mutual inhibition model provides a biologically grounded realization of the general local-activation/long-range-inhibition (LALI) principle of de novo pattern formation, offering a mechanistic explanation for the observed Dkk and Wnt expression patterns under various conditions. Unlike previous models, it is directly grounded in experimental data, links injury response to pattern formation, and remains robust against perturbations. Author SummaryHow organisms form and regenerate complex body structures is a fundamental question in biology. In the freshwater animal Hydra, which can regenerate its entire body from a small tissue fragment, a molecular signalling system involving Wnt proteins and their inhibitors, the Dickkopf (Dkk) family, plays a central role in organising the body axis. While these molecules are known to interact, their exact roles and how they collectively shape large-scale patterns have remained unclear--especially since their activity does not fully align with established pattern formation models. In this study, we present a new mathematical model that captures the observed interactions between Wnt and two Dkk molecules in Hydra. We show that a mechanism based on mutual inhibition--rather than the traditional interplay between activator and inhibitor molecules--can explain the emergence of a stable body axis and the results of various perturbation experiments. Our work offers new insights into the design principles of biological pattern formation and emphasizes the importance of exploring alternative mechanisms beyond classical theories.

Trillions of chemical reactions occur in the human body every second, where the generated products are not only consumed locally but also transported to various locations in a systematic manner to sustain homeostasis. Current solutions to model these biological phenomena are restricted in computability and scalability due to the use of continuum approaches where it is practically impossible to encapsulate the complexity of the physiological processes occurring at diverse scales. Here we present a discrete modeling framework defined on an interacting graph that offers the flexibility to model multiscale systems by translating the physical space into a metamodel. We discretize the graph-based metamodel into functional units composed of well-mixed volumes with vascular and cellular subdomains; the operators defined over these volumes define the transport dynamics. We predict glucose drift governed by advective-dispersive transport in the vascular subdomains of an islet vasculature and cross-validate the flow and concentration fields with finite-element based COMSOL simulations. Vascular and cellular subdomains are coupled to model the nutrient exchange occurring in response to the gradient arising out of reaction and perfusion dynamics. The application of our framework for modeling biologically relevant test systems shows how our approach can assimilate both multi-omics data from in vitro - in vivo studies and vascular topology from imaging studies for examining the structure-function relationship of complex vasculatures. The framework can advance simulation of whole-body networks at user-defined levels and is expected to find major use in personalized medicine and drug discovery. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/460337v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1b872b4org.highwire.dtl.DTLVardef@72b98borg.highwire.dtl.DTLVardef@1f35f29org.highwire.dtl.DTLVardef@ecba45_HPS_FORMAT_FIGEXP M_FIG C_FIG

Active fluid circulation and transport are key functions of living organisms, which drive efficient delivery of oxygen and nutrients to various physiological compartments. Because fluid circulation occurs in a network, the systemic flux and pressure are not simple outcomes of any given component. Rather, they are emergent properties of network elements and network topology. Moreover, consistent pressure and osmolarity gradients across compartments such as the kidney, interstitium, and vessels are known. How these gradients and network properties are established and maintained is an unanswered question in systems physiology. Previous studies have shown that epithelial cells are fluid pumps that actively generate pressure and osmolarity gradients. Polarization and activity of ion exchangers that drive fluid flux in epithelial cells are affected by pressure and osmolarity gradients. Therefore, there is an unexplored coupling between the pressure and osmolarity in the circulating network. Here we develop a mathematical theory that integrates the influence of pressure and osmolarity on solute transport and explores both cell fluid transport and systemic circulation. This model naturally generates pressure and osmolarity gradients across physiological compartments, and demonstrates how systemic transport properties can depend on cell properties, and how the cell state can depend on systemic properties. When epithelial and en-dothelial pumps are considered together, we predict how pressures at various points in the network depend on the overall osmolarity of the system. The model can be improved by including physiological geometries and expanding solute species, and highlights the interplay of fluid properties with cell function in living organisms.

Chimeric antigen receptor (CAR)-T cell therapy marks a significant advance for patients with B-cell acute lymphoblastic leukemia. Clinically, CAR-T cell doses are precisely calculated based on the patients weight, especially for paediatric patients. Patients with higher tumour burdens at the start of CAR-T therapy are less likely to both attain and maintain a deep response compared to those with lower tumour burdens. To quantitatively investigate how CAR-T cell dose, dosing regimen and tumour burden jointly determine therapy outcomes, we developed a family of mathematical models. We first analysed flow cytometry-based killing assay data testing RAJI-19 cells against CAR-T cells, and found that CAR-T cell lysing efficiency increases but saturates with further increases of both RAJI-19 cells and CAR-T cells. This interaction leads to bistable RAJI-19 cell kinetics; specifically, low tumour burdens are effectively inhibited, while high tumour burdens remain refractory. Our models predict that high CAR-T cell proliferation inhibit RAJI-19 cell kinetics independent of dosing regimens. However, with fixed total dose, single-dose infusion provides superior outcomes when proliferation is low. The predicted bistable CAR-T cell concentration interval matches with observed post-infusion CAR-T cell concentrations. Our findings offer a potential mechanistic explanation for observed variations in therapy outcomes and inform personalized CAR-T cell therapy.

Metastatic dissemination remains the leading cause of mortality in malignant tumors, yet the processes of intravasation and extravasation of circulating tumor cells (CTC) are still not fully understood. Existing microfluidic experimental systems possess a number of limitations that prevent them from reproducing physiological conditions. Here we propose a perfused biotechnological system designed to model key stages of the metastatic cascade under controlled flow. The construction includes a parallel-plate flow chamber formed between polyethylene terephthalate plates and surrounded by a collagen gel containing life human dermal fibroblasts. Matrix parameters were optimized, and it was established that a collagen concentration of 20 mg/mL provides mechanical stability, sustained cell viability, and gel robustness under flow. The system also supports the formation of a two-component cellular microenvironment: fibroblasts embedded within the matrix and endothelial cells forming a layer on its surface. An integrated open reservoir effectively eliminated air bubbles and stabilized hydrodynamics, representing a major advantage over conventional microfluidic systems. 48 hour long perfusion of full medium with cells demonstrated long-term cell viability and preservation of channel geometry under continuous perfusion. The developed system combines the benefits of 3D hydrogels and dynamic models while overcoming critical limitations of classical microfluidic devices, and it may serve as a reproducible platform for studying mechanisms of metastasis.