Interface Focus

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.

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.

Maternal thermal strain is associated with adverse pregnancy outcomes, yet fetal temperatures cannot currently be directly measured, limiting quantification of fetal thermal strain. Here, we develop two steady-state models for estimating internal temperatures in a near-term fetus. First, we improve the only previously published human fetal thermoregulation model, deriving a closed-form solution within its simplified uniform-cylinder representation. Second, we introduce a multilayer, anatomically segmented model that resolves tissue-specific temperatures. Both couple the fetal body to central blood pool and amniotic fluid compartments and incorporate a new placenta-umbilical cord heat-exchanger representation. Predictions agree with available intrauterine scalp measurements, with fetal core and head-center temperatures approximately 0.5{degrees}C and 0.8{degrees}C above maternal core, respectively. Physiologically plausible changes in umbilical cord heat-exchanger effectiveness or blood flow increased fetal temperatures by approximately 0.3{degrees}C. These models enable estimation of otherwise inaccessible temperatures, while the multilayer formulation lays a foundation for transient, coupled maternal-fetal thermoregulation modeling.

Venous thrombosis commonly develops in the vicinity of venous valve pockets, where disturbed haemodynamics, xreduced washout, and endothelial dysfunction promote thrombus initiation. While previous studies have focused on conventional flow descriptors such as velocity, shear stress, recirculation, and residence time, the influence of valve mechanics on flow organisation remains poorly understood. Here, we combine a biomimetic vein-on-a-chip platform with computational fluid dynamics, fluid-structure interaction simulations, Ghost Particle Velocimetry (GPV), whole-blood flow measurements, and particle transport experiments to investigate the interplay between valve biomechanics and venous haemodynamics. Movable venous valve leaflets fabricated by in situ photopolymerisation of poly(ethylene glycol) diacrylate (PEGDA) enabled independent control of leaflet stiffness under physiologically relevant steady and pulsatile flow conditions. Previous experiments demonstrated that leaflet flexibility governs thrombus localisation, with symmetric leaflet stiffness promoting clot formation at the valve tips, while asymmetric stiffness shifts thrombus formation towards the valve sinus. In addition, we identify a previously undescribed Reynolds-number-dependent symmetry-breaking transition in post-valve flow. Above a critical flow condition, an initially symmetric jet spontaneously develops into a stable asymmetric flow pattern. This behaviour was consistently reproduced experimentally using GPV and confirmed by Fluid-structure simulations. Valve compliance delayed the onset of the transition by increasing the effective leaflet opening, whereas valves with a small geometric offset promoted earlier asymmetry through higher local flow velocities within the valve gap. The resulting asymmetric flow generated persistent lateral bias in the transport of red blood cell-sized particles, suggesting enhanced platelet accumulation and prolonged residence within one valve sinus. These findings demonstrate that venous valve mechanics regulate not only local flow fields but also particle transport relevant to thrombosis. The discovery of a stable symmetry-breaking flow state provides a new haemodynamic mechanism linking valve stiffness, asymmetric particle transport, and the preferential localisation of thrombus formation, offering new insight into the mechanobiology of deep vein thrombosis.

Microglia are the resident immune cells of the central nervous system (CNS), including the brain, spinal cord, and retina, where they serve as the first line of defense against infection and inflammation. Dysregulated microglia activity has been implicated in vision-threatening diseases, highlighting the need to understand how retinal microglia respond to inflammatory stimuli. Importantly, acute inflammation induces substantial redistribution of microglia across retinal layers, yet the mechanisms governing this migration remain poorly understood. Here, we develop the first mathematical model of retinal microglia migration during inflammation to determine how inflammatory exposure, administration route, and species-specific pharmacokinetics shape redistribution dynamics across the retina. The model couples lipopolysaccharide (LPS) pharmacokinetics with microglia migration between the outer plexiform layer (OPL), inner plexiform layer (IPL), and ganglion cell layer/nerve fiber layer (GCL/NFL). Model parameters are calibrated to retinal microglia density measurements from mice following LPS (bacterial endotoxin) challenge, before extending the framework to rats and rhesus macaques to investigate species-specific responses. Simulations also compare how administration route, i.e. intravenous or intraperitoneal injections, alter retinal LPS exposure and subsequent microglia redistribution. Our results suggest that redistribution patterns are driven primarily by LPS delivery route and species-specific pharmacokinetics, rather than the initial microglia distribution across retinal layers. Together, these findings provide new insight into immune cell reorganization in the inflamed retina and demonstrate how mechanistic mathematical modeling can be adapted across experimental designs, administration routes, and animal species.

Background Tumor hypoxia and abnormal vasculature are closely associated with aggressiveness in solid tumors. Therefore, noninvasive assessment of these features in primary breast cancer is needed. Photoacoustic (PA) imaging is an emerging modality that enables real-time visualization of vascular architecture and hemoglobin oxygenation. Methods Breast PA imaging was performed in patients with primary breast cancer using a bed-type PA imaging system equipped with a hemispherical sensor and a flat specimen holder enabling mild breast compression. Three independent evaluators assessed predefined characteristics of tumor-associated vasculature: centripetal/disrupted vessels and intratumoral vessel-like signals. Oxygenation (S-factor) of tumor-associated vessels was estimated using dual-wavelength laser irradiation at 756 and 797 nm. Results PA imaging was performed in 9 tumors from 8 patients. Eight tumors were evaluable, after the exclusion of 1 tumor with segmental bloody discharge. Centripetal/disrupted vessels were identified in 7 tumors (87.5%). Intratumoral vessel-like signals were observed in all tumors (100%), with higher signal density than in surrounding tissue in 5 lesions (62.5%). Increased intratumoral signal density was associated with a higher Ki67-labeling index (two-sided P = .01). Mean intratumoral S-factor level (76.9% {+/-} 9.1%) was significantly lower than that of peritumoral vessels at 5 mm (86.4% {+/-} 5.9%) and 20 mm (88.5% {+/-} 4.9%) from the tumor margin (two-sided P < .01). Conclusion PA imaging with a flat specimen holder enables noninvasive visualization of tumor-associated vasculature with reduced oxygenation in primary breast cancer. This approach may provide a novel imaging platform for the early detection and functional assessment of breast cancer.

In the United States, roughly 550,000 people receive routine hemodialysis for end-stage renal disease. This treatment requires an arteriovenous access, most commonly a brachiocephalic fistula. However, these accesses often fail due to stenosis in the cephalic arch (CA), a common complication whose underlying causes remain unclear (Bennet et al., 2015). To characterize the hemodynamic environment in the CA, we employed patient-specific millifluidic models that were perfused with blood-mimicking fluid containing fluorescently labelled beads to visualize flow behaviors. We perfused our models across physiologic (28-45 mL/min) and elevated (60-423 mL/min) flow rates and quantified wall shear stress (WSS) and streamline angle as a measure of flow disturbance. Our findings show that the bulk curvature and remodeled wall topography each create regions of persistently low WSS, consistent with prior clinical observations (Hammes et al., 2016). Moreover, remodeled wall topography promotes disturbed flow at elevated flow rates, a hemodynamic profile associated with various vascular pathologies (Chiu & Chien, 2011). Independently performed computational fluid dynamics (CFD) modeling complements these results, showing that remodeled wall topography promotes vortex formation at elevated flow rates, as assessed by Q-criterion. Collectively, our experimental and computational results provide strong evidence for geometry-driven disturbed flow in the CA at elevated flow rates. Notably, we observed disturbed flow at flow rates as low as 81 mL/min, far below the 600 mL/min required for hemodialysis. Disturbed flow thus offers a plausible mechanism that relates access flow rates to the vascular pathologies that precede access failure. Significance StatementHemodialysis requires an arteriovenous fistula (AVF) that must remodel and mature to withstand chronically elevated blood flow rates. However, how remodeled vessel geometry interplays with elevated flow to shape local hemodynamics remains poorly understood. Here, we used millifluidic models of the cephalic arch (CA) to show that vessel geometry and elevated flow rates promote regions of low wall shear stress and disturbed flow. Notably, geometry-driven disturbed flow is observed at flow rates above physiologic levels but well below 600 mL/min, the flow rate necessary for adequate dialysis. Because disturbed flow is injurious to the endothelium, our findings are the first to show that vascular damage begins before fistula maturation.

Background: Circulating tumour DNA (ctDNA) liquid biopsy is now established across oncology for early cancer detection, minimal residual disease surveillance, and treatment monitoring. Detection thresholds for all current ctDNA assays are derived empirically through receiver operating characteristic analysis on training cohorts - a statistically valid but theoretically uninformed approach that does not specify the minimum detectable tumour fraction given assay technical characteristics, nor identify when increasing sequencing depth ceases to provide additional clinical information. Methods: We model ctDNA detection as a binary hypothesis testing problem with Binomial-distributed mutant allele counts against a sequencing error noise floor. The Neyman-Pearson lemma is applied to derive the uniformly most powerful detector and the minimum detectable tumour fraction in closed form. The sequencing assay is modelled as a binary symmetric channel and Shannon channel capacity is calculated. Empirical validation uses n=61 data points extracted from five published peer-reviewed analytical validation studies across five independent institutions in the US and EU (2018 - 2025): Yu et al. 2022, Stetson et al. 2018, Frydendahl et al. 2023, Northcott et al. 2024, and Cheng et al. 2025. Results: The minimum detectable tumour fraction is derived in closed form as f_min approximately equal to (z_alpha + z_beta) multiplied by the square root of (epsilon divided by N), where N is sequencing depth, epsilon is the platform error rate, and z_alpha, z_beta are standard normal quantiles at the specified false positive and false negative rates. Shannon channel capacity is C = 1 minus H(epsilon) bits per read, where H(epsilon) is binary entropy. Empirical validation yields 84.3% agreement for single-locus assays. Discordance for multi-locus tumour-informed assays (NeXT Personal, duplex WGS) is consistent with the single-locus model scope and identifies the principal theoretical extension required. Conclusions: This framework provides the first formal Neyman-Pearson optimality proof for ctDNA detection, a closed-form detection limit, and a platform-independent efficiency metric for NHS and regulatory standardisation. Keywords: circulating tumour DNA; liquid biopsy; Neyman-Pearson detection; Shannon channel capacity; sequencing depth; limit of detection; minimal residual disease; signal detection theory

Cerebral aneurysms are abnormal outpouchings of arteries within the brain and occur in [~]1 in 30 adults. Their initiation, growth, and rupture have been linked to focal blood flow abnormalities--often termed "disturbed" or "hostile" hemodynamics--but commonly-used hemodynamic metrics yield conflicting associations with pathology and lack a unifying mechanistic interpretation. Building on a theoretically-grounded link between wall shear stress and near-wall vorticity, we hypothesized that a topology-based description of near-wall flow can operationalize the concept of hostile hemodynamics in a reproducible way. Inspired by atmospheric tornadic phenomena, we sought a principled taxonomy of coherent near-wall fluid structures with potential mechanobiological and clinical implications. Using high-fidelity computational fluid dynamics simulations in anatomically realistic geometries, we identified coherent near-wall fluid structures whose organization mirrors well-studied atmospheric phenomena: tornado-like columnar rotating cores; downburst-like nonrotating wall-impinging jets with tangential outflow, roll-cloud-like tangential vortices; and mixed configurations. These tornadic events on the aneurysm luminal surface were identified from wall shear stress topology, consistent with its theoretical connection to near-wall vorticity kinematics. The presence of tornadic phenomena--and their imprints on the aneurysm wall--was independently observed in vivo using 4D flow magnetic resonance imaging. By translating concepts from atmospheric physics into vascular biomechanics, this topology-based framework yields a unified mechanistic language for describing near-wall hemodynamics, resolving blood flow complexity into interpretable and reproducible coherent fluid structures, enabling standardized hemodynamic phenotyping, and supporting hypothesis-driven studies of aneurysms and other cardiovascular diseases where greater fluid-mechanical specificity and interpretability may strengthen links between mechanobiology and clinical risk.

Copy number variations (CNVs) can serve as important clinical biomarkers for tumor classification and stratification. However, the utility of these CNV biomarkers for intraoperative tumor assessment within the timeframe of neurosurgical procedures has remained elusive due to the protracted duration of conventional CNV characterization methods. Here, we introduce CNVisor, a statistical framework for reliable and robust CNV detection from long-read sequencing, even under ultra-low coverage. Applied to neurosurgical tumor specimens, the proposed method enabled genome-wide CNV profiling and identified clinically relevant CNVs using roughly 60,000 reads within 20 minutes of sequencing. Integrating CNVisor with methylation-based classifiers can further reduce turnaround time and increase the accuracy of glioma subtype stratification. Together, these findings establish real-time CNV profiling using ultra-low coverage nanopore sequencing as a feasible strategy for intraoperative, genomics-informed assessment of CNS tumors.

IntroductionPreeclampsia is characterized by a pro-inflammatory, anti-migratory and anti-angiogenic placental phenotype. Impaired spiral artery remodeling stemming from trophoblast dysfunction is a key pathogenic mechanism. Little is known about the processes that govern trophoblast function normally and in preeclampsia. In preeclampsia, placental Let-7b-5p is reduced. The objectives of this study were to determine the normal function of Let-7b-5p in human trophoblast cells, to examine whether the ssRNA sensors, Toll-like receptor (TLR) 7 and/or TLR8 are mediators of trophoblast Let-7b-5p function, and whether disruption of this pathway promotes a preeclampsia-like phenotype in the trophoblast. MethodsThe human first trimester trophoblast cell line, Sw.71, was transfected with a Let-7b- 5p mimic, a Let-7b-5p inhibitor, or scramble control. Cells were treated with or without the TLR7 inhibitor IRS661 or the TLR8 inhibitor CUCPT9a. Trophoblast migration was measured using a two-chamber assay and interactions with human endometrial endothelial cells (HEECs) was measured using a 3D matrigel model. Trophoblast anti-angiogenic sFlt-1 release was measured by ELISA and sFLT1 mRNA measured by RT-qPCR. ResultsTransfection of trophoblast cells with a Let-7b-5p mimic elevated migration through activation of TLR7 and TLR8, while in a TLR7-dependent manner, the Let-7b-5p mimic negatively regulated sFlt-1 production. Furthermore, inhibition of trophoblast Let-7b-5p reduced migration, elevated FLT1 mRNA expression and sFlt-1 release, and reduced trophoblast-endometrial endothelial cell interactions. ConclusionsThis study highlights a role for TLR7/TLR8-activating Let-7b-5p in promoting normal trophoblast function and endothelial interactions and that disruption in this miR-driven signaling pathway may be relevant to processes driving a pre-eclamptic placental phenotype. HighlightsTrophoblast migration is positively driven by Let-7b-5p activating TLR7 and TLR8 Let-7b-5p, via TLR7, negatively regulates trophoblast anti-angiogenic sFlt-1 production. Inhibition of trophoblast Let-7b-5p reduces trophoblast migration and normal interactions with endometrial endothelial cells, while sFlt-1 production is elevated. TLR7/TLR8-activating Let-7b-5p promotes normal trophoblast function and endothelial interactions and disruption in this miR-driven signaling pathway may promote a preeclamptic placental phenotype.

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.

Direct experimental analysis of the mammalian oviduct is constrained by limited tissue access and the short lifespan of ex vivo preparations. Extracellular matrix-embedded three-dimensional epithelial organoids provide longer-term in vitro models. However, their inward-facing apical surface and the absence of supporting stromal cells limit physiological studies of the oviduct, including ciliary activity and maternal-embryonic interactions. Here, we provide a step-wise protocol detailing the generation of mouse and human oviductal assembloids in which epithelial cells form an outward-facing (apical-out) layer around a stromal core. Epithelial and stromal cells from adult mouse oviducts or human Fallopian tubes are isolated, expanded separately, and subsequently aggregated in a rotational culture system. The protocol also outlines morphological and immunostaining criteria for confirming cellular organization, whole-mount detection of external cilia, measurement of ciliary beat frequency, and co-culture of mouse assembloids with preimplantation embryos. Mouse and human assembloids retained epithelial and stromal identity and displayed cilia at the accessible outer surface. In a proof-of-concept experiment, embryos co-cultured with the assembloids developed to blastocysts at a rate similar to that of in vivo-derived blastocysts. This reductionist system provides a straightforward and tractable model to investigate oviduct physiology and embryo-maternal communication while allowing direct manipulation and observation of the epithelial interface. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/743297v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1917a7borg.highwire.dtl.DTLVardef@41d7org.highwire.dtl.DTLVardef@e2bf98org.highwire.dtl.DTLVardef@90c9f3_HPS_FORMAT_FIGEXP M_FIG C_FIG SummaryThe protocol for generating mouse and human oviductal assembloids by combining epithelial and stromal cells for studying oviductal function in an in vitro setting.

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

Precise, non-invasive manipulation of individual living cells remains a central challenge in biomedical science, with far-reaching implications for single-cell analysis, tissue engineering, and the study of cell-cell interactions. Here, we report the first demonstration of single-cell control using bulk acoustic standing-wave acoustofluidics with closed-loop feedback. We introduce VeLO (Vector-based Local Optimization), a model-free, reinforcement learning-inspired algorithm that enables programmable two-dimensional manipulation of individual cells using a single piezoelectric transducer. Without prior calibration or physical modeling, VeLO learns system dynamics online from acoustically induced cell displacements and automatically adapts to nonlinear, time-varying conditions. We achieve robust control across multiple cell types (DU-145, Jurkat, K-562) and independent manipulation of multiple cells, including controlled cell-cell contact. By combining simplicity of hardware with autonomous, adaptive control, this approach establishes multimodal acoustofluidics as a versatile tool for label-free, high-precision single-cell manipulation.

Physiological blood vessels are generally straight, but tortuous curvature is observed under pathological conditions across spatial scales, from large arteries to retinal microvasculature and tumor-associated vessels. Here, we compare two theoretical mechanisms of curved vessel formation--mechanical buckling and angiogenic biased random walk--within a common spectral framework. We simplify the Chaplain-Anderson angiogenesis model and an Euler-Bernoulli buckling model with surrounding-tissue support, reproduce curvature numerically, and analyze the power spectra of the resulting patterns. Buckling yields a single characteristic peak in the power spectrum, whereas angiogenesis yields k-2 scaling in the low-frequency range. Mathematical analysis explains the selective growth of a dominant buckling wavelength and scaling characteristics in the Chaplain-Anderson model. We further test these predictions using morphological descriptors (power spectrum, autocorrelation, and mean squared displacement) applied to a public retinal vessel dataset and propose a two-phase model combining angiogenic structure generation with subsequent mechanical remodeling. These results suggest that spectral fingerprints may help distinguish mechanically driven tortuosity from angiogenesis-driven tortuosity in vascular images.

Objective: Tumor protein D52 (TPD52) is aberrantly expressed in various malignancies; however, its systematic expression profile, prognostic significance, tumor microenvironment associations, and functional mechanisms in breast cancer remain poorly defined. Methods: GEO and TCGA breast cancer expression datasets were integrated to identify differentially expressed genes (DEGs). We evaluated the diagnostic performance of TPD52 via protein-protein interaction (PPI) network analysis, GO/KEGG enrichment analysis and eleven machine learning algorithms. Immunohistochemistry verified TPD52 protein expression in clinical specimens, and Kaplan-Meier analysis assessed its prognostic significance. Analysis of single-cell transcriptomic data (GSE176078) revealed the cell-type-specific distribution of TPD52 and its intercellular communication network in the breast cancer microenvironment. Weighted gene co-expression network analysis (WGCNA) explored relationships between TPD52 and tumor microbiome, hypoxia signatures as well as microsatellite instability. Moreover, TPD52 was knocked down by siRNA in MCF7 cells, and its impacts on cell migration, invasion, proliferation and the MAPK/ERK signaling pathway were examined through wound healing, Transwell, CCK-8 and Western blot assays. Results: TPD52 was significantly overexpressed in breast cancer tissues at both the mRNA and protein levels. A random forest-based diagnostic model demonstrated high accuracy across multiple datasets. Kaplan-Meier analysis revealed that elevated TPD52 expression was associated with longer overall survival in specific subgroups, including the basal-like subtype, invasive lobular carcinoma, and N0/N1 stages. Single-cell analysis showed that TPD52 was predominantly expressed in tumor epithelial cells, which occupied a central position within the intercellular communication network. WGCNA further identified a positive correlation between TPD52 and a hypoxia-associated microbial module, as well as a negative correlation with a microsatellite instability module. In vitro functional assays confirmed that TPD52 knockdown significantly suppressed the migration, invasion, and proliferation of MCF7 cells, and led to reduced p-ERK1/2 protein levels. Conclusion: TPD52 promotes the malignant phenotypes of breast cancer cells through activation of the MAPK/ERK signaling pathway, yet its prognostic significance is subtype- and microenvironment-dependent. These findings establish TPD52 as both a diagnostically valuable biomarker and a mechanistically defined potential therapeutic target.

The tumor microenvironment plays a pivotal role in tumor development, harboring elements such as endothelial cells, immune cells, fibroblasts, and soluble factors such as transforming growth factor-{beta} (TGF-{beta}) family. TGF-{beta} family regulates cell development and promotes tumor invasion, metastasis, angiogenesis, and endothelial-to-mesenchymal transition (EndoMT). Here, we investigate the effects of TGF-{beta} signaling on vascular remodeling using a three-dimensional (3D) vascular network in a microfluidic device. Using both a co-culture (3D-Co) and simplified endothelial monoculture (3D-CM), we demonstrate that TGF-{beta} signaling reduces the quality and functionality of the vasculature by regressing them. In addition, we observed the upregulation of EndoMT-related markers in mRNA and protein expressions, suggesting the induction of EndoMT in 3D vascular networks. The increased vascular permeability stimulated by TGF-{beta}2 also supports the loss of endothelial identity in the 3D-Co. Transcriptomic analysis revealed the coordinated activation of pathways associated with cell migration and EndoMT, along with the suppression of cell cycle progression. A comparative analysis of two-dimensional (2D) and 3D cultures revealed a fundamentally distinct endothelial response to TGF-{beta}2 in the 3D context, including metabolic reprogramming. These findings demonstrate that the 3D microenvironment critically modulates endothelial responses to TGF-{beta} and enables the emergence of vascular phenotypes not captured in 2D systems. This study provides a more physiologically relevant platform to investigate endothelial dysfunction and vascular remodeling.

The Next Generation Impactor (NGI) is one of the regulatory gold standards for characterizing aerodynamic particle size distributions (APSDs) of orally inhaled drug products (OIDPs); however, its reliance on complex, resource-intensive in vitro testing under tightly controlled environmental conditions limits experimental flexibility and introduces variability. In alignment with the growing regulatory emphasis on New Approach Methodologies (NAMs) for drug development, this study presents a rigorously validated computational fluid particle dynamics (CFPD) based virtual NGI (vNGI) as an in silico method complementary to conventional testing. The vNGI replicates a significant portion of the NGI geometry and airflow physics, enabling high-resolution spatiotemporal analysis of aerosol transport and deposition mechanisms that are otherwise inaccessible experimentally. A comprehensive verification and validation framework was implemented, including mesh and particle independence studies, turbulence model assessment, and comparison of stagewise deposition efficiencies with available in vitro data at 30 L/min. The model's capabilities were further extended to low and high flow rates, and two bio-relevant mouth-throat models and polydisperse particle laden aerosol were added. The model demonstrates strong predictive capability for a few stages and provides mechanistic insight into discrepancies in other stages, depending on the type of analysis. Importantly, this work establishes the vNGI as a fit-for-purpose according to NAM by (i) defining a clear context of use for APSD prediction and inhaler performance evaluation, (ii) capturing physically and biologically relevant air-particle interactions, and (iii) demonstrating technical robustness and reproducibility through systematic validation. The platform can potentially further enable simulation of environmental and physiological conditions, such as humidity effects, that are difficult to control experimentally, thereby improving human relevance and reducing reliance on costly and time-consuming in vitro testing. This study positions the vNGI as a scalable, regulatory aligned NAM capable of supporting early stage drug device combination product development, device optimization, and an alternative bioequivalence assessment, contributing to ongoing efforts to enhance predictive performance, reduce experimental burden, and transition toward human centric, inhalation product evaluation.

Biochemical Systems Theory (BST) represents nonlinear biochemical rate laws by local power-law approximations in logarithmic concentration coordinates. First-order coefficients are elasticities, whereas higher-order derivatives describe local log-synergism and its variation. Ordinary higher derivatives, however, are not tensorial under nonlinear reparameterizations and can mix biochemical response structure with coordinate artifacts. We formulate a covariant hierarchy, cBST1-cBST3, on a positive operating-point space equipped with a declared reference connection. cBST1 recovers classical elasticities in a flat logarithmic chart, cBST2 is the covariant Hessian of the log-response, and cBST3 is the symmetrized covariant derivative of cBST2. The framework is quantitatively evaluated using three representative rate laws from the curated yeast glycolysis model BIOMD0000000064: glucose transport, glucose phosphorylation, and phosphofructokinase. For 10,000 finite log-concentration perturbations at each of five radii, cBST2 reduced the cBST1 log-rate root-mean-square error by 96.6-98.6% at the largest tested radius, and cBST3 provided a further 68.4-98.1% reduction. The contracted cBST2 and cBST3 terms strongly predicted the corresponding lower-order truncation errors. Under the nonlinear transformation qi = sinh(ui), covariant contractions agreed across coordinates to within a 95th-percentile relative error of 1.2 x 10-13, whereas ordinary higher derivatives showed order-unity coordinate mismatches. Supplementary analytic tests recovered the expected second-, third-, and fourth-order truncation-error scaling. These results show that cBST1-cBST3 are not only coordinate-consistent descriptors but also practical diagnostics of where local power-law approximations require higher-order correction.