Annals of Biomedical Engineering

Angiogenesis drives the formation and remodeling of capillary networks throughout tissue repair, regulating the vascular environment that supports healing and tissue remodeling. Experimental characterization of these processes is commonly performed using CD31-stained histological tissue sections to quantify capillary surface density and morphology throughout healing. However, these measurements provide only two-dimensional characterization of an underlying three-dimensional (3D) vascular network, limiting direct estimation of volumetric capillary density and vascular architecture. To address this limitation, an experimentally informed framework was developed to generate representative 3D capillary networks, enabling estimation of volumetric capillary density from histologically quantified vascular measurements. CD31-stained histological sections obtained from a longitudinal porcine lumpectomy study were analyzed to quantify the percentage of CD31-positive area (%CD31+) and capillary morphology within healthy tissue and healing surgical cavities. Histologically quantified morphology distributions and literature-informed vascular branching characteristics were incorporated into a capillary network generation framework to construct representative 3D vascular networks. Capillary branches were iteratively generated within representative tissue volumes until virtual histological sections reproduced experimental %CD31+ measurements, enabling estimation of volumetric capillary density. Generated capillary networks demonstrated good agreement with experimentally characterized 3D vascular architecture, while simulated histological sections accurately reproduced experimentally quantified capillary counts and vascularization measurements. Application of the framework to the porcine lumpectomy dataset captured temporal changes in vascular remodeling throughout healing, revealing progressive increases in volumetric capillary density and vascular maturation. Collectively, this framework provides an experimentally informed methodology for relating histological vascular measurements to volumetric capillary density estimates, supporting future computational studies of angiogenesis and tissue repair.

Identification and treatment of pregnancies at risk for preterm birth is a central challenge in obstetric research. Many of the known causes of preterm birth originate from mechanical failure in reproductive tissues. To better understand the biomechanical environment of the gravid uterus and its potential contribution to preterm birth, this computational study presents a parametric method for modeling maternal reproductive anatomy during the early second trimester. A finite element modeling approach was built using existing sonographic measurements from early second-trimester maternal anatomy and material properties from published mechanical tests. We applied the same physiologically relevant intrauterine pressure to all models and quantified the resulting tissue stretch. The sensitivity of the stretch in the proximal cervix was explored by varying material properties and sonographic maternal anatomy dimensions. Cervical material properties, particularly the fiber stiffness modulus and ground substance Youngs modulus, were found to have the greatest effect on proximal cervix stretch compared to other material properties and sonographic dimensions. Among the sonographic dimension measurements, those defining the region surrounding the proximal cervix had the greatest effect on proximal cervix stretch, including the curvature of the posterior uterine wall and the thickness of the lower uterine segment. The computational modeling approach presented here enables future patient-specific studies of gravid reproductive tissues to elucidate differences between individuals who do and do not deliver preterm. Additionally, this study is foundational for building digital twins to support future virtual clinical studies on diagnostic and therapeutic device design to prevent preterm birth.

Introduction: Metastatic spinal lesions substantially alter vertebral mechanical properties and increase fracture risk. Computed tomography (CT) based finite element (FE) models can estimate vertebral strength, but their accuracy depends on how CT derived material properties are represented. This study evaluated the effect of two material grouping strategies on simulated strength and stiffness in metastatic vertebrae. Methods: We compared Adaptive Clustering (AC) with Uniform fixed width grouping in 44 vertebrae from 11 donors (8 osteolytic, 12 osteoblastic, 12 mixed, 12 no observed lesion (NOL)). FE models were generated based on CT scans with 2 to 500 material groups and compared for material mapping error and simulated strength and stiffness. Overall and lesion stratified agreement with experimental measurements was assessed in an exploratory analysis. Results: AC showed significantly lower Young's modulus root mean square error than Uniform (p < 0.05). Simulated strength and stiffness stabilised by 50 material groups. At 50 groups, simulated strength showed moderate correlation with experimental strength overall (R2 = 0.57), strongest in NOL vertebrae (R2 = 0.82) and lower in lesion-bearing vertebrae (R2 = 0.4-0.59). Stiffness showed weaker correlation overall (R2 = 0.27), highest in NOL vertebrae (R2 = 0.48) and negligible in mixed lesions (R2 = 0.007). Bland Altman analyses indicated systematic underestimation of experimental fracture load. Discussion: AC improved material-mapping fidelity, whereas increasing material groups beyond 50 had little influence on simulated strength or stiffness. Numerical stabilisation therefore did not imply experimental accuracy. Lesion stratified findings were exploratory and should be interpreted cautiously because of limited subgroup sizes.

Background: Acute pulmonary embolism (PE) is a leading cause of morbidity and mortality with persistent difficulties in choosing interventions and predicting outcomes for patients defined clinically as intermediate risk. Computational fluid dynamics (CFD) tools have been used to understand the hemodynamic environment and plan interventions in the pulmonary arteries across a variety of disease conditions. Several biomechanical metrics have been used to evaluate risk in narrowed vessels, including hemodynamic resistance, power dissipation, and fractional flow reserve (FFR). In this study, we evaluate differences in these CFD-derived biomarkers between healthy controls (HC) and intermediate risk, acute PE patients. Additionally, we examine the response of patient hemodynamics to mechanical thrombectomy and compare values of these biomarkers across post-intervention pressure status. Methods: A CFD framework was developed to simulate patient-specific hemodynamics within the pulmonary vasculature identifiable from clinical imaging. The pipeline involved reconstructing three-dimensional (3D) structures of the pulmonary arteries and modeling blood flow with the finite element method. Patient-specific boundary conditions were derived from matching pre-intervention inlet mPAP to the patient's measured value given their measured CO as steady inflow. Converged simulations allowed for precise quantification of primary hemodynamic characteristics (flow and pressure) as well as secondary flow phenomena, primarily wall shear stress (WSS) and simulated pressure metrics such as fractional flow reserve (FFR). Results: Our simulations revealed significant elevations in resistance, power dissipation, and the number of vessels with low FFR in those patients with acute PE (n=6) compared to HC (n=3). Occlusions of hemodynamic significance were generally found in segmental pulmonary arteries. For patients with normalized pulmonary pressures post-thrombectomy (n=3), we found significantly higher proximal power dissipation and counts of low FFR vessels in comparison to those with elevated pressures after intervention (n=3). Distal resistance, which was derived from the portion of resistance attributed to the outflow boundary conditions, was significantly higher in patients with elevated pressures post-intervention. Across all PE patients, FFR count was significantly correlated with post-thrombectomy pulmonary pressure and cardiac index. Discussion: CFD-derived biomarkers offer a promising tool for understanding disease severity in acute PE. Differences between HCs and acute PE patients reveal expected increases in metrics associated with proximal disease burden. Yet, in examining acute PE patients with varying post-intervention hemodynamics, we found that these metrics of proximal disease burden could also be useful to predict the efficacy of mechanical thrombectomy. Those patients with normalized pressures had higher values for proximal disease metrics and lower values for distal disease metrics than those with continued elevations in pressure. This suggests that accessibility of hemodynamically-significant emboli to thrombectomy may be useful as a predictor for outcomes.

Energy expenditure (EE) during running depends on the interplay between active muscle work and elastic energy storage and return, yet the relative contribution of mechanical power to EE remains debated. Quantifying the relative contributions of segment-level mechanical power can provide a way to address this debate. In this study, we aimed to quantify how segment-level mechanical power contributes to EE during running and to demonstrate that these mechanistic insights support wearable-based EE estimation. Joint dynamics and respiratory gas-based EE were collected from healthy young adults running at multiple speeds. Scale factors were derived to quantitatively link efficiency-weighted segment power to measured EE. The stance leg consistently showed the strongest correlation with EE, and this dominance was preserved across speeds. Including swing-leg hip power further improved accuracy. Scale factors were approximately 0.45, suggesting that active muscle work and elastic energy return contribute comparably to the mechanical power associated with EE. Using a lightweight machine learning model, stance-leg and swing-leg hip joint power were reconstructed from a single sacral IMU, enabling accurate EE prediction. These findings demonstrate that lower-limb mechanical power is a robust predictor of running EE, supporting both the extensibility of biomechanically-informed frameworks and wearable-based EE monitoring.

Purpose: Quantifying aortic morphology is central to surgical planning for thoracic endovascular aortic repair (TEVAR), yet no consensus exists on how best to represent three-dimensional aortic shape for outcome prediction. Two broad strategies have emerged: statistical shape analysis (SSA), which relies on statistical methods and dimensionality reduction to capture the most significant shape modes, and geometrically-informed approaches that extract descriptors grounded in differential geometry. Here, we directly compare these paradigms on a cohort of 290 CTA scans classified by surgical outcome (non-pathological, successful TEVAR, failed TEVAR). Methods: For the geometrically-informed approach, we use a two-dimensional feature space using normalized fluctuation in integrated Gaussian curvature $\widetilde{\delta K}$ and mean aortic radius $R$. For SSA, we construct a point-cloud shape model with dimensionality reduction using Principal Component Analysis (PCA) and evaluate classification performance as a function of the number of retained principal components. Results: SSA's leading principal components encode variations in global aortic size and are statistically redundant with ($R$, $\widetilde{\delta K}$), yet they lack a one-to-one correspondence with interpretable anatomical quantities. Testing on an unseen, independent dataset reveals that the geometrically-informed approach provided better generalizability than SSA. Using Gaussian process classification with 10-fold cross-validation, we find that the geometrically-informed approach achieves a higher weighted $F_1$ score than SSA achieves with up to 20 principal components. While SSA's full-dataset accuracy rises above 90\% with increasing dimensionality, this gain is driven by overfitting rather than genuine discriminative power. Conclusion: These results demonstrate that geometrically-informed descriptors offer a more interpretable, robust, and clinically translatable framework for aortic disease classification than data-driven statistical shape representations.

PurposeComputational models of the uterus during pregnancy enable analysis of electro-chemo-mechanical pathways to predict labor timing and guide treatment planning. We aim to develop a robust image-based modeling pipeline to investigate uterine passive mechanics during late pregnancy. MethodsA parametric model of the uterus and cervix was created using a patients MRI measurements at 38 weeks of gestation. Inspired by advances in cardiac mechanics models, we created Laplace-Dirichlet solutions to inform tissue domains, fiber structure within the uterus and cervix, and spatially varying Robin boundary conditions. Prior imaging and mechanical testing data were used to fit material parameters. Boundary condition parameters were tuned to match the displacements of a previously established approach that employed contact with surrounding tissue. The tissue mechanical response to a physiologic load was assessed across varying material properties and fiber architectures. ResultsDiscrepancies in nodal displacements between the current approach and the contact-based model were limited to 3.4 {+/-} 1.8 mm, yielding nearly 90 % computational savings. Uterine tensile strains were more sensitive to ground substance elastic modulus (E) compared to fiber properties. Reduced E and fiber stiffness increased cervical strains and compression. Fiber dispersion and architecture modulated the opening of the cervical internal ostium but had a reduced impact on compression. ConclusionWe developed a novel workflow for modeling passive uterine mechanics, informed by patient-specific measurements and in vitro mechanical tests. The robust workflow may prove useful for studying labor progression and conducting longitudinal studies to enhance our understanding of normal and pathological pregnancies.

Background and AimsActive capsule endoscopy could advance gastrointestinal diagnostics by enabling controlled navigation beyond passive peristalsis. However, current systems are often limited by inefficient propulsion, high power demands, or reliance on external actuation. Herein, we designed, developed and evaluated a novel electromagnetic impact-actuated capsule endoscope incorporating a ferromagnetic rail-enhanced locomotion mechanism. MethodsThe capsule employed an internal electromagnetic actuator comprising a movable coil-armature assembly guided along a ferromagnetic rail and surrounded by permanent magnets. Controlled current pulses generated reciprocating motion and propulsion through momentum transfer. Bench-top testing using a deformable intestinal model assessed locomotion and power consumption. Ex-vivo experiments were subsequently performed in porcine intestine under dry and physiologically simulated wet conditions. Transit speed, power consumption, and system stability were recorded. ResultsBench-top testing demonstrated stable propulsion at speeds up to 8.5 mm/s with a mean power consumption of 84 mW. During ex-vivo evaluation, mean capsule velocities were 1.95 mm/s and 7.2 mm/s under dry and wet conditions, respectively. Average power consumption was 96 mW and 193 mW. The actuator maintained reliable locomotion while preserving a compact system volume of [~]6.19 cm3. Lubricated conditions, representative of the intestinal environment, resulted in enhanced propulsion efficiency despite a concomitant increase in instantaneous power consumption. ConclusionThe electromagnetic impact-actuated capsule demonstrated reliable locomotion in biologically relevant ex-vivo environments while maintaining compact dimensions and moderate power requirements. Ferromagnetic rail-enhanced flux concentration offers a promising propulsion strategy for future actively navigated and therapeutic capsule endoscopy platforms.

Non-valvular atrial fibrillation (AF) is associated with a five-fold increased risk of stroke, mainly due to impaired contractility of the left atrium (LA) leading to blood stasis and subsequent thrombus formation within the left atrial appendage (LAA). Current AF stroke risk stratification schemes, such as the CHA2DS2-VASc/ CHA2DS2-VA score, use comorbidities and do not capture mechanistic factors like blood flow dynamics and hypercoagulability. To address this, we developed a multiphase computational fluid dynamics (CFD) model of the LA, incorporating patient-specific geometries; modelling of the coagulation cascade; and non-Newtonian blood behaviour within the LAA. Using 84 simulation cases generated via Latin Hypercube Sampling of physiological blood parameters and 21 patient-derived LA anatomies, we trained surrogate machine learning models, including Ridge regression, XGBoost, Gaussian Process Emulators (GPEs), and deep learning networks, to predict CFD outputs such as blood viscosity in and fibrin concentrations in the LAA. Deep learning achieved R{superscript 2} values up to 0.90, with the accuracy increasing when both physiological parameters and the raw CT image were included. Other models showed uneven performance with R2 values below 0.7, highlighting the role of nonlinearities between parameters. The study presents a novel CFD model that captures the transition from blood stasis to clot formation, representing the full thrombotic continuum underlying stroke risk in AF, and a deep learning approach to enable efficient prediction of mechanistic outputs of clinical value for stroke risk stratification in AF patients. Author SummaryAtrial fibrillation is a common heart rhythm disorder that greatly increases the risk of stroke. In many patients, blood can pool inside a small pouch of the heart called the left atrial appendage, where clots may form and later travel to the brain. Current clinical tools used to estimate stroke risk mainly rely on a patients medical history and do not directly assess the mechanistic processes that lead to clot formation. In this study, we developed a computer model that simulates how blood flows and clots inside the heart using patient-specific heart anatomies derived from medical imaging. Our model combines blood flow, blood biochemistry, and the changing physical properties of blood during clot formation. We then used machine learning methods to predict these complex simulation results more efficiently. Deep learning models performed best, particularly when both clinical parameters and heart imaging data were included. Our work provides a new way to study the full process linking abnormal blood flow to clot formation in atrial fibrillation. In the future, this approach could support more personalised and mechanistic assessment of stroke risk and help guide treatment decisions.

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.

Physiological cross-sectional area (PCSA) is defined as the summed cross-sectional area of all muscle fibers contracting in parallel and at optimal length. PCSA is widely used, yet the conventional equations used to compute PCSA were developed from two-dimensional (2D) interpretations of muscle architecture and may not accurately represent muscle fiber cross-sections in the case of real three-dimensional (3D) geometries of muscles. Related measures of functional cross-sectional area (FCSA) and geometric cross-sectional area (GCSA) were also developed and interpreted with simplified 2D representations. Using realistic 3D muscle architectures derived from medical imaging, we sought to investigate whether conventional definitions of PCSA, FCSA, and GCSA represent the summed cross-sectional areas of all parallel muscle fibers, the fundamental definition of PCSA. We found that none of these measures consistently represented this definition. Thus, we introduce the physiological cross-sectional surface (PCSS), a curved surface within a muscle volume that is everywhere perpendicular to the local fiber direction. We estimated PCSS in 3D muscle surface meshes reconstructed from MRI data, using fiber orientations derived from Laplacian fiber reconstruction. PCSS-derived estimates were compared with PCSA, FCSA, and GCSA across six muscles representing five architectural classes. PCSS differed from all conventional measures, with the magnitude and direction of disagreement depending on muscle architecture. PCSS-to-PCSA ratios ranged from 0.771 to 1.399, while GCSA underestimated PCSS by up to a factor of 2.256 in bi- and multipennate muscles and overestimated it in muscles with more uniform fiber arrangements. PCSS demonstrated high geometric fidelity (perpendicularity>0.987) and robustness to fiber density across a tenfold range (coefficients of variation 0.39-3.95%). These findings indicate that conventional cross-sectional area measures do not consistently account for all fiber cross-sections in parallel within realistic 3D muscle geometries. PCSS provides a geometrically rigorous alternative that may improve estimation of functional muscle capacity from subject-specific imaging data.

The temporal resolution of medical imaging sequences used to drive patient-specific computational fluid dynamics (CFD) simulations remains limited, typically providing 10-20 frames per cardiac cycle. Therefore, temporal interpolation to reconstruct left atrial (LA) wall motion and boundary conditions is required, but its impact on hemodynamic predictions has not been systematically characterized. To investigate this, we constructed high-temporal-resolution reference wall-motion data using electromechanical (EM) simulations on five patient-specific atrial geometries with a history of atrial fibrillation. We then generated temporally downsampled datasets to emulate clinical frame rates (5, 10, 20, and 40 frames per cycle) and performed CFD simulations to isolate the effects of temporal undersampling on hemodynamic metrics. The focus was placed on kinetic energy, KE, and residence time, TR, particularly in the left atrial appendage (LAA), where thrombosis is most likely to occur. We employed an immersed boundary method to prescribe the wall motion and computed blood TR through a passive scalar transport equation. Results indicate that while global LA hemodynamic indices were marginally affected by the frame rate (errors < 9%), LAA metrics were more sensitive with errors up to 31% compared to reference values. The results based on 20 and 40 frames per cycle yielded favorable agreement with reference results, while 5-and 10-frame reconstructions showed larger, though not systematically biased, deviations from the reference. Importantly, patient ranking by blood-stasis indices was largely preserved. The analysis suggests that patientspecific LA reconstructions derived from dynamic CT imaging provide a reliable basis for estimating LAA blood-stasis indices. Higher frame rates ([&ge;] 20 per cycle) offer improved quantitative accuracy, while lower temporal resolutions may remain informative for patient stratification purposes, where relative ranking is more relevant than absolute accuracy.

Background and Objective: Indications for right ventricular assist device (RVAD) insertion include right heart failure after implantation of a left ventricular assist device or early graft failure following heart transplantation. This study aimed to investigate how the upstream and downstream circulatory network interacts with the Impella RP(R) device. Methods: A numerical model of the Impella RP(R) was implemented within CARDIOSIM(C) software platform for this study. In the numerical configuration, the RVAD aspirated blood from either the right atrium (RA-PA connection) or the right ventricle (RV-PA connection) and delivered it to the pulmonary artery. Only RA-PA connection is the currently used setting for Impella RP(R) in clinical practice. Based on right ventricular (RV) decompression and total flow, our study may help define the need for a direct RV-unloading Impella RP(R). Results: The simulations showed that activating the RVAD in RA-PA mode, regardless of its rotational speed, the mean pulmonary artery pressure (PAP) percentage change was higher than the unsupported condition when the mean systemic venous pressure (SVP) and the pulmonary artery wedge pressure (PAWP) were both set to 20 mmHg. When RV-PA connection was applied, a similar trend was observed although the PAP percentage changes were about halved compared to the RA-PA connection. Conclusions: The Impella RP(R) has the potential to become a valid option for RV support based on current experimental and simulation data. Although already in use, further evaluation in the clinical setting will likely confirm its potential and lead to a more routinely application for RV support.

Successful instrumented fusion of the lumbar spine is a complex surgical challenge, with positive patient outcomes dependent on careful surgical planning. Material selection is of critical importance to a mechanical construct supporting successful spinal fusion. Therefore, the aims of this study were to (a) evaluate the potential clinical use of finite element analysis (FEA) and (b) conduct a retrospective mechanical analysis of different implant materials in patients having undergone spinal fusion using FEA. Our methodology involved segmenting the spine from post-operative computed tomography (CT) image data from patients with previous spinal fusion. FEA models representing post-surgery cases were developed and different biomechanical loading conditions such as compression, flexion, bending and extension whilst testing pedicle screws of different materials were simulated. Patient specific finite element models were created, and biomechanical analysis were completed for all three patients. Polyetheretherketone (PEEK) constructs typically demonstrated lower peak implant stress when compared to titanium constructs for all spinal fusion levels. Furthermore, increasing the spinal fusion level resulted in significant differences in the maximum von Mises stress within both the bone and the instrumentation, whereas the 2-level fusion exhibited comparable stress levels in the bone irrespective of the instrumentation material. This pilot explores the potential of FEA as a clinical tool for assessing device and bone stresses. In our cohort, different materials can influence the stresses in both the instrumentation and the instrumented vertebrae, suggesting FEA can be useful pre- operative tool with regards to instrument selection and post-operatively to assess instrumentation and bone stresses. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/742715v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@55959aorg.highwire.dtl.DTLVardef@d0b9d6org.highwire.dtl.DTLVardef@158c348org.highwire.dtl.DTLVardef@7ce828_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Purpose: Fibrotic remodeling of the uterus, associated with aging, disease, and environmental exposures, alters collagen organization and tissue stiffness, yet how these changes influence organ-level mechanical behavior remains poorly understood. Glutaraldehyde (GA)-induced collagen crosslinking was used as a controlled surrogate for fibrotic remodeling to determine whether image-informed inverse finite element analysis (iFEA), combined with inflation testing and micro-computed tomography (microCT), could detect and quantify the resulting changes in uterine constitutive behavior. Methods: Murine uteri (n = 6 untreated, n = 6 GA-crosslinked) underwent volume-controlled balloon inflation with simultaneous microCT imaging to quantify deformation of the inner and outer wall boundaries for iFEA. Specimen-specific Gasser-Ogden-Holzapfel (GOH) finite element models were optimized by adjusting model parameters to reproduce experimentally measured wall contours throughout inflation. Model performance was evaluated using contour root mean square error (RMSE), and parameter identifiability was assessed through sensitivity analyses. Results: GA treatment significantly increased inflation work, linear stiffness, and maximum inflation resistance (p < 0.001). The iFEA framework accurately reproduced experimental deformation (RMSE < 3%) and revealed significant increases in the estimated GOH parameters C10 (9.2-fold), k1 (2.0-fold), and k2 (2.7-fold), consistent with increased effective tissue stiffness and a shift toward earlier collagen fiber recruitment. Sensitivity analyses demonstrated unique, well-defined minima for all parameter combinations. Conclusion: Image-informed iFEA provides a quantitative framework for relating collagen remodeling to organ-level uterine mechanics through specimen-specific constitutive parameter estimation. This approach establishes a foundation for investigating the mechanical consequences of uterine fibrosis and other remodeling processes.

Repetitive low-intensity blast (LIB) exposure has been identified as a probable cause of mild blast-induced traumatic brain injury (mbTBI) and a chronic injury risk to U.S. military personnel. However, the human brains biomechanical response to this loading regime remains poorly characterized. Using blast-exposure-validated 3D models of human anatomy, we simulated the intracranial tissue response to blast pressure typically experienced by Warfighters during weapons training. Two scenarios were evaluated, a single-dose exposure and a repetitive-dose exposure, to study intracranial pressure (ICP), shear strains, and spectral content. Ansys LS-DYNA was used to generate planar blast waves with peak overpressures of 4-90 kPa and positive phase durations of 2.2-10 ms. Single exposures produced ICP ranging from 4.7-112.7 kPa, dependent on dose and positive phase duration. Under repetitive LIB exposure, peak ICP increased by 8-26% relative to single exposures, with an increase of high-frequency components (>2 kHz). These results demonstrate that LIB can produce measurable intracranial responses that are amplified through repetition, producing pronounced spectral content and elevated pressures despite low strain levels. This study underscores the need to further investigate cumulative dose effects and the value of computational approaches to clarify hypothesized mbTBI mechanisms in operationally relevant conditions.

Background: Spinal cord injury (SCI) is frequently associated with orthostatic hypotension, defined by a sustained decrease in blood pressure upon assuming an upright posture due to impaired autonomic regulation. Cardiovascular spinal cord epidural stimulation (CV-scES) can regulate systolic blood pressure (SBP) in people with SCI, but stimulation paradigms are highly individualized. To make this treatment available to more patients, we developed an algorithm to tailor individualized CV-scES paradigms that closely mimic researcher-developed paradigms. Methods: We performed an offline analysis using datasets collected from eight individuals with SCI with epidural stimulators implanted over the lumbosacral spinal segments. During data collection, researchers modulated stimulation parameters with the goal of maintaining SBP between 110-120 mmHg. Each two-hour dataset included synchronized SBP and stimulation recordings. We ran optimization analyses offline to determine temporal requirements before modifying stimulation amplitude to mitigate out-of-range SBP. Results: The algorithm parameters that best matched researcher-selected stimulation changed relatively quickly during the first 12 min (one every ~40 sec), and more slowly thereafter (one every ~79 sec). Overall, algorithmic stimulation closely tracked researcher-controlled stimulation, with a mean correlation coefficient of 0.94. To evaluate online performance, we tested the algorithm in real time with a single participant. We found that a faster approach was needed to respond to changes in SBP caused by rapid, unpredictable events, such as postural changes. We implemented a sigmoid-based paradigm that determined the time to wait before changing stimulation as a function of the current SBP, with worse SBP values requiring faster responses. The new paradigm outperformed the original algorithm and researcher-controlled stimulation across measures of SBP stability, though recovery from a postural tilt maneuver remained slower than with researcher control. Conclusions: Our results indicate that algorithmic stimulation may minimize assistance required from researchers and participants, making CV-scES more feasible for clinical translation.

Background/PurposeDiagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). MethodsPersonalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. ResultsAverage area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. ConclusionThis study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

PurposeMild therapeutic hypothermia (MTH) preserves cochlear function in animal models and is now entering early-phase human trials for hearing preservation. However, the extent to which the human cochlea can actually be cooled, and the mechanisms underlying MTH, remain unclear, in part because blood perfusion is expected to oppose localized cooling. In this study we evaluated the impact of blood flow on human cochlear temperature exposed to the MTH device using a combined experimental and computational approach. MethodsTemperature measurements were obtained from a human cadaver skull exposed to a commercial MTH device. These data were used to validate a three-dimensional bioheat transfer model incorporating realistic skull anatomy. The validated model was subsequently extended to include physiological blood perfusion in the internal carotid artery; a major heat source located near the cochlea. Finally, the in silico model was further expanded to incorporate the surrounding skin and brain tissues. ResultsIncorporating blood flow in internal carotid artery substantially altered predicted cochlear temperature distributions, highlighting the importance of localized vascular heat transport in the human cochlea during MTH. Although cochlear cooling was attenuated in the presence of perfusion, the therapeutic effects of MTH may not depend solely on the magnitude of local intracochlear temperature reduction. Additional mechanisms, such as reduced facial surface temperature, may also contribute to its efficacy. ConclusionThe validated in silico model provides a physiologically realistic framework for evaluating human cochlear thermal responses, investigating MTH mechanisms, and optimizing temperature-based strategies for hearing preservation.