Interface Focus

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.

Computational analysis of physiological and biomedical systems necessitate efficient geometry representations for high fidelity model predictions, including patient or device specificity. Particle-based Lagrangian computational approaches comprise a valuable approach to gain insights from quantitative velocity and pressure data from computational models. Examples include particle dynamics and transport in human vasculature for diseases such as stroke, thrombosis, and embolisms; and modern targeted drug delivery systems in the vascular network and respiratory airways. However, current particle simulation approaches can bear significant computational expense that scales with both number of particles and background fluid mesh resolution. A significant determinant of this computational expense is the contact resolution between particles and anatomically realistic vessel wall. Here, we develop an efficient particle dynamics model that leverages an implicit representation of real anatomical features using a signed distance field to efficiently resolve particle-wall contact. We outline the underlying algorithmic details, followed by a systematic illustration of performance and accuracy using simplified and analytically defined geometries and flow fields. Subsequently, we present a representative simulation of embolic particles along a human vascular segment where we compare our distance field-based approach against classical wall-contact checks based on assessing particle boundary intersection with triangulated surface mesh. Our approach transforms the underlying Lagrangian contact detection operation into an equivalent Eulerian operation, significantly speeding up bulk particle dynamics computations without significantly impacting accuracy or geometric fidelity.

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.

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.

Hemodynamic forces play a key role in early cardiac morphogenesis, yet many computational studies assume Newtonian blood behavior. Here, we evaluate the impact of nonNewtonian shearthinning rheology on flow patterns, pressure distributions, and wall shear stress (WSS) during cardiac looping using idealized threedimensional models of the embryonic heart tube. Five geometries representing progressive looping stages, from a linear tube to an Sshaped configuration with ventricular ballooning, were analyzed under pulsatile flow using both Newtonian and powerlaw viscosity models. Across all stages, Reynolds numbers (Re {approx} 1-7) and Womersley numbers (Wo {approx} 0.3) indicated laminar, quasisteady flow consistent with embryonic conditions. Incorporating shearthinning rheology produced substantial deviations from Newtonian predictions, with peak systolic WSS differing by up to [~]40% and pressure drops by up to [~]20%. These effects were most pronounced in regions of increased curvature and geometric complexity. These findings demonstrate that nonNewtonian rheology significantly influences predicted hemodynamic environments during cardiac looping and should be incorporated into computational models aimed at understanding mechanobiological regulation of early heart development.

The commercial maturation of microfluidics remains bottlenecked by empirical prototyping and an absence of predictive digital design capabilities. Because optimizing advanced technologies such as passive particle separation fundamentally hinges on the precise coupling of fluid dynamics and particle mechanics, conventional two-dimensional or decoupled fluid simulations inherently fail to capture authentic multiscale behaviors. To bridge this gap, we establish a high-fidelity three-dimensional fluid-structure interaction framework combining a high-order Arbitrary Lagrangian-Eulerian mapping-based finite element method with a localized hierarchical dynamic mesh strategy. Engineered to accurately resolve complex multiscale hydrodynamics, this architecture utilizes deterministic lateral displacement structures as a stringent test case. Validated against experimental data for rigid microspheres and tumor cells, the framework predicts transport trajectories and critical separation diameters with sub-micron precision. Crucially, the simulation explicitly resolves the M-shaped spatial fluctuation of local size thresholds alongside the dynamic vertical migration of particles. Unveiling these hidden physical mechanisms provides a deterministic explanation for highly debated phenomena such as mixed-mode transport. By enabling the rigorous in silico evaluation of complex non-periodic architectures, this framework serves as a powerful instrument for predictive structural optimization. Such capabilities establish the essential infrastructure for microfluidic digital design, accelerating the transition from empirical trial-and-error to precision simulation-driven engineering.

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.

Quantitative biophysical signatures of nuclear spatial reorganisation across breast carcinoma progression remain insufficiently characterised. We apply two complementary fractal descriptors, Correlation dimension (Dc) and Minkowski dimension (Dm), to 4276 regions of interest across seven breast tissue subtypes from the BRACS dataset, validating observed dimensions against systematically constructed null spatial models to distinguish genuine structural organisation from geometric irregularity. All subtypes significantly exceed the complete spatial randomness baseline, confirming universal departure from random nuclear arrangement. The observed scaling is characterised as statistically monofractal within a bounded pre-fractal range. Invasive carcinoma uniquely fails to exceed the clustered null in Dc while simultaneously showing the weakest Dm null deviation, a dual convergence toward stochastic baselines consistent with the progressive removal of architectural constraints. Flat epithelial atypia exhibits a unique directional dissociation with the lowest Dc across all subtypes combined with high Dm null deviation, a co-occurrence not observed in any other subtype and geometrically consistent with decoupled nuclear spatial organisation at the centroid distribution and boundary morphology scales. Interpreted within a percolation-theoretic framework, the non-monotonic null deviation trajectory maps onto qualitative regime transitions, providing a physically grounded explanation for the observed discrimination profile across pathological transitions. These findings position fractal-like nuclear architecture as a potential descriptor for pre-malignant transitional states.

Control of fluids is a hallmark of microfluidic systems and fundamental for the successful application of microfluidic devices. Trigger valves use geometric features to autonomously control the release of fluids in microfluidic devices. Our previous work has adapted geometries used in closed trigger valve systems to enable use in open systems, allowing for open microfluidic devices with up to three trigger valves. Here, we focus on the parallel co-flows produced by sequential release of trigger valves and present a model that predicts their layer widths as a function of the geometric characteristics of the different side channels of each trigger valve. We show layered co-flows with widths as low as 50 microns. Additionally, we expand the use of trigger valves in open microfluidic devices by incorporating 1) varied step heights, 2) devices with up to seven trigger valves, and 3) use of varied fluids and plastics. To validate the implementation and use of these trigger valves in open systems, we have developed a theoretical framework to compare predicted outcomes (i.e., fluid travel distance, velocity, and layering width) with our experimental values. This theoretical work offers applications in various fields, including hydrogel patterning for 3D cell culture, organ-on-a-chip models, at-home sample preparation, and autonomous microfluidic systems for biosensing.

Vertebrate gastrulating mesoderm is a prototypic example of a mesenchymal-like tissue undergoing extensive remodelling. While the tissue may be globally represented as a viscoelastic material, the actual biological material is intrinsically complex. To get to a real understanding of its properties, one needs to move to the mesoscale, linking cellular properties to collective phenomena. Vertebrate embryos also display a remarkable variability in mechanical properties, despite which they robustly complete gastrulation. This study attempts to explore these aspects by dissecting Xenopus mesoderm cell behaviour in a minimal system, using aspiration through a microfluidic system to impose controlled stress to a mesoderm aggregate. We show that beyond estimating global rheology at the tissue scale, it is possible to infer a wealth of information based on cell morphology and dynamics. Our data are consistent with collective behaviour being mostly dictated by the balance between the capacity of cells to stretch and the resistance to cell-cell contacts, which limits cell-cell intercalation and thus tissue remodelling. Importantly, tissues are not only able to transmit stress over a distance, they also clearly react to it through actively reinforcing cell-cell mechanical coupling. This adaptative property is found through a broad range of tissue stiffness, and adhesion strength appears to scale with the elastic modulus, suggesting that cell stiffness may ultimately be the key parameter setting mesoderm rheology and accounting for the large differences observed between embryo batches.

Pulsatile milk transport through the lactating mammary ductal tree involves complex interactions between pressure gradients, wall compliance, and non-Newtonian rheology across spatial scales that span nearly two orders of magnitude in lumen radius. Direct experimental characterisation of flow in distal ductal generations remains infeasible due to their sub-millimetre calibre, leaving the haemodynamic environment of the secretory ductules largely unknown. We present a two-stage physicsinformed operator-learning framework that extends validated flow predictions from three instrumented duct generations to twenty generations of a bifurcated mammary network. A Physics-Informed Neural Network (PINN) trained against particle image velocimetry measurements across seven ducts achieved R2 = 0.924-0.997. A Deep Operator Network (DeepONet) distilled from the PINN and refined through physics-constrained training on the governing one-dimensional fluid-structure interaction equations achieved R2(u) = 0.857-0.985 across all validated ducts, with predictions for Generations 4-20 obtained by supplying Murrays Law geometry and mass-conservation-scaled boundary conditions to the frozen operator. Three biophysically significant findings emerge: a mean velocity plateau of 0.14-0.18 m/s across Generations 4-13 produced by Cross shear-thinning compensation offsetting Murray-branching deceleration; a non-monotonic pulsatility index that declines from 0.048 at Generation 1 to a minimum of 0.039 at Generation 5 before rising monotonically to 1.37 at Generation 20 as progressive wall stiffening drives the most distal ductules into a microcirculation-like haemodynamic regime; and a brief elastic-recoil transition zone at Generations 4-5 where mean axial pressure drop reverses sign. To the authors knowledge, these results provide the first quantitative characterisation of pulsatile milk flow across the full hierarchy of a bifurcated mammary ductal tree using a physics-informed operator-learning framework with implications for ductal mechanobiology, milk ejection mechanics, and mastitis pathogenesis.

De novo vessel formation (vasculogenesis) in vitro is a key step in tissue engineering to preserve tissue viability for long-term assays and testing therapeutic agents. However, in vitro vasculogenesis is often unreliable due to differences in vascular-supporting cells, including endothelial cells and stromal cells such as smooth muscle cells (SMCs) and fibroblasts. Here, we developed a robust co-culture system of HUVECs and SMCs to generate stable vascular networks capable of maintaining tissue viability over extended periods. Given that SMC plasticity is a major limitation in supporting endothelial network formation, we systematically evaluated the effects of passage number, confluency, and freezing on primary SMC function. To overcome this limitation, we generated immortalized supportive SMCs, which preserved their vasculogenic gene program and functional capacity even at high passage. In addition, we identified and validated key genes associated with endothelial support, including CD248, C3, and FBLN1, all essential for vasculogenesis. Immortalized SMCs consistently maintained expression of these genes and supported robust vessel formation under variable culture conditions. Collectively, this study demonstrates that immortalized SMCs provide a stable, reproducible platform for endothelial-SMC co-cultures, enabling long-term vascularized tumor models suitable for functional studies and therapeutic screening.

As the duration of space flights increases, so does the need to optimize off-planet microbial growth. Microbes can both be unintentionally brought into space and cause human disease or be intentionally harnessed for on-site bioengineering functions. However, optimizing microbial growth is challenging due to an insufficient understanding of how microbial communities are affected by the extraterrestrial environment. To address this gap, we have modified a previously developed model for cell growth in microgravity. By improving the functional form used for cell growth as well as the code usability, we enable further research into how microbial communities are influenced by gravity. Applying this model to isolate individual effects of gravity on cell growth indicates that a lack of gravity-driven flow decreases cell growth in microgravity, while the absence of sedimentation increases cell growth in microgravity. These opposite effects likely contribute to the system-dependent effects of microgravity observed experimentally.

Tracking gastrointestinal (GI) transit in preclinical models is essential for assessing gut motility and drug delivery. Current preclinical methods rely on end-to-end transit measurements or emptying studies that require terminal endpoints and organ explanation. Clinically, radiopaque "Sitz" markers are administered orally and their position in the GI tract is assessed through radiography. Sitz markers have been in use since 1969 and are typically mass-produced using industrial molding or extrusion, resulting in a single, fixed geometry with limited tunability. We present a stereolithography (SLA)-based method to fabricate customizable radiopaque markers using additive manufacturing with a barium sulfate (BaSO4)-doped resin. We demonstrate precise control over marker geometry, a key advantage over existing markers. Furthermore, we apply this method in vivo, tracking markers in a live rat model from ingestion to excretion using serial CT imaging. We systematically investigate how changes in marker geometry impact GI residency and transit time. Our results show that 3D printed markers provide a flexible and tunable platform for radiopaque marker fabrication and enable investigation of the fundamental relationship between a markers physical properties and its performance in a dynamic biological environment. This work establishes a novel, tunable platform for GI motility evaluation and drug delivery studies.

Nano- and microplastics (NMPs), by-products of the fragmentation and degradation of plastic products, are ubiquitous environmental contaminants, yet their burden in pediatric immune tissues and functional consequences for developing immunity remain unknown. Here we report the first comprehensive characterization of NMPs in surgically excised pediatric tonsils (n = 30) using pyrolysis gas chromatography- mass spectrometry (Py-GC/MS), Nile Red fluorescence microscopy, and optical photothermal infrared (O-PTIR) spectroscopy. NMPs were detected in all specimens, with polystyrene, polyethylene, polyethylene terephthalate, and acrylonitrile butadiene styrene present in >90% of samples. To bridge clinical exposure data with mechanistic insight, we formulated a cryo-milled multi-polymer mixture reflecting the patient-derived polymer profile and challenged human lymphoid aggregate culture (HLAC) tonsil organoids at environmentally relevant concentrations. Multiplexed cytokine profiling of culture supernatants revealed a robust early inflammatory response at day 3, with significant upregulation of IL-6 (p = 0.011) and MIP-1{beta}/CCL4 (p = 0.011), followed by convergence toward control levels by day 14. Functional cytokine modules spanning immune, metabolic, structural, and growth factor pathways showed coordinated deviation from controls at day 3 post-exposure with subsequent normalization. Fluorescence-guided depth profiling demonstrated time-dependent penetration of 100 nm particles into organoid aggregates (70% tissue depth at day 3 versus 95% at day 14), and transmission electron microscopy revealed intracellular polyethylene within lymphocyte lysosomes. These findings establish pediatric tonsils as a sentinel tissue for NMP bioaccumulation and demonstrate that environmentally relevant polymer mixtures elicit transient but significant immunomodulatory responses in human lymphoid tissue, with implications for mucosal and systemic immune health in children. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/728317v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@19395f4org.highwire.dtl.DTLVardef@5a0380org.highwire.dtl.DTLVardef@19c0741org.highwire.dtl.DTLVardef@a052c5_HPS_FORMAT_FIGEXP M_FIG Structure: Translational pipeline from clinical tissue characterization to patient-informed preclinical modeling of nano-microplastic (NMP) exposure in pediatric lymphoid tissue. Pediatric tonsil tissue collected from clinically indicated tonsillectomies underwent tissue digestion for NMP characterization to identify NMP type and size distributions. In parallel, tonsil tissue was used to generate human lymphoid aggregate culture (HLAC) organoids that recapitulate the cellular complexity of the native tissue. These patient-derived organoids were then exposed to environmentally relevant compositions and concentrations of NMPs over time-course experiments, with longitudinal assessment of immunomodulatory responses including cytokine profiling and functional readouts. This bedside-to-bench approach establishes a physiologically relevant human system for investigating NMP-immune interactions, bridging clinical tissue analysis with mechanistic preclinical modeling to inform understanding of pediatric environmental exposures and their potential health impacts. C_FIG

BackgroundMetabolic reprogramming is a common occurrence in tumor cells, where enhanced glycolysis promotes cell growth, invasion and migration. NF1 is tumor suppressor gene that downregulates the encoded neurofibromin protein. However, the effects of NF1 on energy metabolism and epithelial-mesenchymal transition (EMT) in glioblastoma multiforme (GBM), as well as the underlying molecular mechanisms, remain unclear. MethodsCRISPR/Cas9 gene editing technology was employed to construct GBM cell lines with NF1 gene mutations. Metabolomics was utilized to examine the impact of NF1 on metabolic remodeling in GBM. The Seahorse XF24 extracellular flux analyzer was used to detect the effect of NF1 knockdown on glycolysis and mitochondrial oxidative phosphorylation in GBM cells. Wound healing assay and Transwell chamber assay were utilized to detect the effect of NF1 on GBM cell invasion. Orthotopic tumor model in nude mice was established to explore the role of NF1 in vivo. In addition, Co-IP, western blotting, and immunofluorescence were used to explore the changes of key enzymes in glycolysis and mitochondrial oxidative phosphorylation and the relationship between NF1 and MFN1. ResultsThe expression of NF1 is decreased in glioma tissues and is significantly correlated with patient prognosis. NF1 knockdown may promote the invasion, migration, and EMT of GBM cells. At the same time, the activation of the AKT/mTOR signaling pathway promotes aerobic glycolysis in GBM cells, promotes mitochondrial division through targeted regulation of MFN1, and inhibits mitochondrial oxidative phosphorylation. NF1 deficiency promotes EMT in GBM cells by enhancing aerobic glycolysis and mitochondrial division. ConclusionNF1 deficiency promotes GBM glycolysis by activating the AKT/mTOR signaling pathway and inhibits the mitochondrial oxidative phosphorylation by regulating MFN1; NF1 deletion promotes GBM EMT by remodeling the pattern of energy metabolism.

T cell receptors (TCRs) are critical for immune surveillance and successful adaptive immune response against foreign antigens. TCRs drive this key arm of the immune system through recognition of peptide epitopes presented on MHC complexes. However, they are limited due to their stochastic nature and generation via genetic recombination. In silico design of functional TCRs that target defined peptide epitopes would be of considerable utility but has up until now been unsuccessful. Here, we develop an artificial intelligence (AI)-powered approach using a hybrid physics-based simulation and generative AI that successfully engineers TCRs against defined epitopes presented by MHC-I. We use this approach to design TCRs against two cancer antigens, a HERC1 neoantigen and an immunogenic neoepitope in mutant EGFR. We engineer multiple TCRs against the HERC1 neoantigen which activate T cells in response to exposure to peptide-MHC I and kill cancer cells more effectively than a patient-derived TCR. In addition, we used generative AI to design functional TCRs that target the EGFR T790M neoantigen, engineering greater specificity against the mutant sequence. We present an AI-based approach to TCR design with broad utility for efforts to engineer TCRs and for the development of new cell therapies. One sentence summaryArtificial intelligence-based approach enables the directed engineering of functional TCRs with enhanced features that target cancer neoantigens.

Abstract Background Prostate cancer (PCa) diagnosis remains challenged by the limited specificity of prostate-specific antigen (PSA) testing, which cannot reliably distinguish malignancy from benign prostatic hyperplasia (BPH). MicroRNAs (miRNAs) are emerging candidates for liquid biopsy-based diagnostics, but most studies assess expression in isolation within a single compartment (biological source - Tissue, blood, serum, urine etc.), overlooking both compartment-specific behavior and the coordinated relationships among miRNAs. Methods We profiled four candidate miRNAs --- miR-19b-3p, miR-21-5p, miR-101-3p and miR-375-3p, across four biological compartments (prostate tumor tissue, urine, serum, and blood) in 179 patients undergoing prostate biopsy for clinical suspicion of PCa (104 PCa, 75 BPH) using qRT-PCR. Urinary exosomal RNA was isolated with a commercial exosome isolation kit so from here onwards this compartment will be referred to as urine. Differential expression was quantified using Cohen's d; inter-miRNA coordination was assessed via Spearman correlation and differential correlation ({delta} r) analysis; and a compartment-level network rewiring score was derived as the sum of {delta} r| across miRNA pairs. Cross-compartment structural alignment was evaluated by comparing correlation patterns at the population level. Diagnostic models combining PSA, age, and urinary exosomal-miRNA features were evaluated using Logistic Regression, Elastic Net Logistic Regression and Naive Bayes classifiers under leave-one-out cross-validation (LOOCV). Results Effect sizes were largest and most consistent in urine, with miR-101-3p showing the strongest separation between PCa and BPH (d = -1.01), followed by miR-21-5p (d {approx}-0.72$) and miR-19b-3p (d {approx}-0.64). Two markers (miR-19b-3p, miR-375-3p) showed directional reversals across compartments, indicating that disease-associated signals are compartment-specific rather than uniformly conserved. In tumor tissue, PCa was associated with substantial reorganization of inter-miRNA coordination (network rewiring score = 2.46), including the emergence of a strong miR-21-5p--miR-375-3p co-regulatory axis ({delta} r = +0.87$) and decoupling of the miR-21-5p--miR-19b-3p relationship ({delta}r = -0.64$). Urine showed a structurally distinct coordination pattern (rewiring score = 1.77), dominated by a miR-101-3p--miR-19b-3p axis (r = +0.56) absent from tissue; cross-compartment comparison showed concordance in only 1 of 5 miRNA pairs, indicating that urine's architecture is largely independent of tissue's. For diagnostic translation, the conventional PSA cutoff (4 ng/mL) achieved 100% sensitivity but only 23.5% specificity. In urine, miR-101-3p performs better than other miRNAs, with AUC of 0.77 (95% CI: 0.62--0.90). Adding PSA and age to the urinary miR-101-3p further improved discrimination to an AUC of 0.91 (95% CI: 0.82--0.99), with 70% specificity at 92% sensitivity; this pattern was consistent across Elastic Net and Logistic Regression classifiers. Expanding the model to include all urinary miRNAs, age, and pair-derived coordination features did not improve on this result (AUC = 0.88), indicating that population-level coordination changes did not translate into additional individual-level diagnostic value in this cohort. Conclusions miRNA signals in extracellular compartments do not represent direct surrogates of tumor-level molecular architecture; each compartment harbors a distinct, transformed coordination structure reflecting its biological context. While these coordination-level changes are mechanistically informative, the most direct translational gain in this study came from a parsimonious model combining PSA, age with a single urinary marker, miR-101-3p, which improved AUC from 0.77 to 0.91, with specificity 70.5% at 90% sensitivity criteria. This combination represents a promising, interpretable candidate for reducing unnecessary prostate biopsies, pending validation in larger, independent cohorts. Keywords: MicroRNA, Compartment-Specific Biomarkers, Urinary Exosomes, Differential Correlation, Liquid Biopsy, Machine learning, PSA, Early diagnosis

The global coral reef crisis has prompted restoration initiatives worldwide. Targeting the coral larval stage is among the most scalable approaches as recruitment operates over large spatial scales. It thus represents one of the best levers for coral population recovery. Active coral larval seeding has shown considerable success, and passive substrate engineering has emerged as a promising complementary strategy. Coral settlement modules featuring helix recesses have increased settlement and survival by up to 80-fold on small experimental units, but whether these results translate to tools deployable at the scale of thousands of units, remains yet an open question. Here, we transferred structural features from successful experimental coral settlement designs into production-ready concrete modules to (i) evaluate coral recruitment on five designs at four reef sites differing in flow regime and coral cover over one year; (ii) compare production-scale performance against experimental clay modules and natural reef substrate; and (iii) identify key parameters for large-scale production. The helix recess geometry of coral settlement modules outperformed the featureless control design approximately 20-fold and exceeded natural reef recruitment at least 3- to 32-fold. The helix features were successfully transferred from experimental clay to production-scale concrete modules, yielding comparable settlement densities when standardized to crevice length, which proved to be the biologically relevant unit of available habitat. Production feasibility was demonstrated by producing 690 modules for deployment on a hybrid reef on the west side of Oahu, Hawaii. The passive coral larval recruitment approach presented here could substantially improve the logistical and economic feasibility of large-scale coral reef restoration. This approach requires neither coral larval rearing, handling, nor coral fragmenting, and is compatible with active larval seeding where genetic diversity or larvae supply are limiting factors. The coral settlement modules can be cast in standardized concrete molds at precast facilities. Modules have demonstrated consistent coral recruitment enhancement across reef environments with contrasting flow and coral cover. Deploying mixed arrays of helix-recess structures with designs offering multi-level complexity and three-dimensional rugosity maximizes outcomes for coral, fish, and invertebrate communities simultaneously. Site selection is the most critical deployment decision and should consider larval supply, hydrodynamics, and substrate stability which drive recruitment outcomes more than design choice alone. The modules offer a range of application potential, ranging from integration into existing coastal infrastructure over stand-alone reef restoration approaches, to substrate-consolidating interconnected arrangements.

Targeted drug delivery offers a promising approach for personalized medicine in treating vascular stenosis. However, biomechanical constraints, such as drug washout by high-velocity central blood flow and unintended absorption by healthy vascular walls, complicate the determination of optimal dosing locations. Conventional three-dimensional computational fluid dynamics (CFD) provides precise flow analysis but incurs prohibitive computational costs, making long-term tracking of plaque growth and reverse-engineering of optimal delivery highly inefficient. In this study, we propose a pseudo-3D stochastic growth model that dramatically reduces computational load while capturing the essential dynamics of plaque progression and regression. By modeling the advection-diffusion of lipid and drug particles as a discrete Markov process within a Stokes flow field, we simulate the morphological evolution of plaques under continuous and interrupted targeted therapies. Furthermore, by formulating the drug transport process as an absorbing Markov chain with boundaries at the healthy walls and vessel outlet, we calculate the exact reaching probability and mean first passage time (MFPT) to the plaque. Based on these probability distributions, we discover continuous "Optimal Dosing Curves", which indicate the most effective spatial coordinates for catheter-based drug release to maximize therapeutic efficacy. This mathematical framework not only elucidates the stochastic nature of vascular plaque dynamics but also provides a scalable, computationally efficient foundation for optimizing targeted drug delivery in personalized medicine.