Annals of Biomedical Engineering

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.

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.

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: 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.

Minimally invasive endovascular procedures offer reduced surgical trauma, shorter recovery times, and improved outcomes, but rely on 2D fluoroscopic X-ray imaging, which provides limited depth perception and exposes patients and clinicians to ionizing radiation. Here we present an augmented reality (AR) system that fuses intravascular ultrasound (IVUS) and electromagnetic (EM) position tracking with preoperative computed tomography (CT) to produce an anatomically accurate, deformation-corrected navigational reference. A robotic device performs ECG-gated pullback of the IVUS probe, capturing 4D aortic motion across the cardiac cycle. We introduce a deep learning architecture for extracting vascular lumen boundaries and side-branch orifices from artifact-prone IVUS streams, and a semantically driven non-rigid CT-IVUS fusion pipeline robust to false positive landmarks. We evaluate the platform with trained surgeons in benchtop phantom studies and in-vivo ovine models, and demonstrate its application to fenestrated endovascular aneurysm repair (FEVAR). Compared to fluoroscopy alone, AR guidance significantly reduces cannulation time, radiation exposure, and cognitive workload, while improving procedural efficiency and safety. Our IVUS-EM and CT aortic datasets are released open source.

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.

Accurate estimation of body segment inertial properties is essential for biomechanical analyses, yet commonly used scaling methods rely on limited datasets and do not generalize well across diverse adult body morphologies. We developed a data-driven framework that estimates segment lengths, masses, centers of mass, and moments of inertia using regression models trained on large anthropometric datasets (ANSUR II and NHANES) combined with a geometric representation of 16 body segments. The framework uses height, weight, and sex as primary inputs and incorporates waist and hip circumferences or other length and cross-sectional measurements when available to refine body-shape predictions. For individuals with obesity, additional geometric rules redistribute excess mass based on segment-specific volume changes. The resulting models reproduced segment lengths, cross-sectional dimensions, and lumped segment masses within the ranges observed in the training datasets and outperformed published regression equations, particularly at higher body mass index (BMI) values. To promote broad adoption, we provide an open-source API in Python that performs the full parameter estimation using the trained models. This framework offers an accurate and accessible method for estimating adult body segment properties across a wide range of body sizes and shapes, supporting improved motion analysis, musculoskeletal simulation, and clinical biomechanics.

Spinal cord injury (SCI) causes rapid and severe bone loss in the paralysed lower limbs, particularly at the distal femur and proximal tibia, where fragility fracture risk is high. In vitro nanoscale vibration at 1 kHz has been shown to promote osteogenic differentiation and inhibit osteoclastogenesis, suggesting potential as a targeted mechanical intervention. This study aimed to develop and evaluate a wearable device for delivering and monitoring localised nanovibration at the distal femur in individuals with SCI. The device delivered continuous sinusoidal nanoscale stimulation at 1 kHz via a bone-conduction transducer, with an opposing accelerometer used to monitor transmitted vibration in real time. Design and target-site selection were refined through two healthy-volunteer investigations comparing the distal femur, proximal tibia, and distal tibia. Bovine femur experiments characterised vibration transmission under controlled benchtop conditions. Preliminary repeated-use feasibility was assessed in one individual with motor-complete SCI. Healthy volunteer testing showed that although the ankle initially produced the highest transmitted amplitudes, these were highly variable, and positioning was inconsistent. Within the knee region, the distal femur provided the most practical and repeatable site for a wearable application. In bovine femur experiments, scanning laser vibrometry demonstrated measurable vibration on the condylar surface opposite the transducer, and depth-resolved measurements confirmed that nanoscale vibration remained detectable within bone. A gel interface layer reduced the transmitted amplitude. In the feasibility evaluation, 61 sessions were completed over 14 weeks, with logged accelerometry confirming repeated nanoscale vibration transmission. These findings establish feasibility and support further device optimisation and translational studies.

PurposeFunctional tricuspid regurgitation (FTR) is driven by right ventricular (RV) remodeling, annular dilation, and papillary muscle dislocation. Free wall approximation (FWA) has been proposed to treat FTR by addressing RV dilation, but its effects on tricuspid valve (TV) biomechanics remain unclear. We present a real-time 3D echocardiographic (rt3DE)-based finite element framework to quantify TV biomechanics under FTR, and preliminarily apply it to assess FWA effects. MethodsSubject-specific models were developed from rt3DE data of three dilated porcine hearts in an ex-vivo mock-loop. TV geometries at end-diastole and peak systole (PS) were complemented by parametric chordae tendineae and hyperelastic tissue properties. TV closure was simulated under a standard pressure load and image-based annular motion. After tuning chordae length to replicate the PS ground truth in FTR, FWA was simulated as 30% and 60% approximations along three anatomical directions (anterior-posterior, A-P; anterior-septal, A-S; anterior-septal wall, A-SW). ResultsIn FTR simulations, median geometric errors ranged from 1.16 to 1.26 mm; median stress ranged from 56.4 to 74.7 kPa. FWA simulations predicted regurgitant orifice area (ROA) reductions by 53-99%, albeit overestimating the residual ROA vs. in vitro ground truth when starting from particularly extreme FTR conditions; concomitantly, a median stress reduction by 8-43% vs. FTR conditions was predicted. ConclusionPreliminary data suggest that our rt3DE-based framework can reliably quantify FTR-related TV biomechanics and that post-FWA biomechanics depends on initial FTR conditions. A larger cohort is required to verify the method and obtain statistically significant results.

Introduction: Acute compartment syndrome (ACS) is a limb-threatening complication of extremity trauma that requires timely diagnosis to prevent irreversible muscle and nerve injury. Current diagnostic methods are invasive, intermittent, and operator-dependent. We evaluated the feasibility of a novel, Bluetooth-enabled electrical impedance myography (EIM) device (mAlert, Myolex, Inc., Brookline, MA, USA) for continuous, noninvasive detection of ACS-related tissue changes. Methods: Ten Yorkshire swine underwent anterior tibial compartment monitoring using three ACS models: albumin infusion (ALB, n=3), femoral artery and vein ligation (LIG, n=3), and combined albumin infusion plus ligation (ALB+LIG, n=4). Resistance (R), reactance (X), and phase (P) were measured every minute across 1 to 199 kHz alongside continuous intra-compartmental pressure (ICP) monitoring. Group differences in normalized impedance trends were evaluated using the Kruskal Wallis test with Dunn post hoc correction. As a proof-of-concept human study, nine healthy volunteers wore the device for up to five days to assess electrode durability and signal stability. Tissue ischemia was validated using pimonidazole immunohistochemistry. Results: ALB infusion produced progressive, frequency-dependent decreases in R, X, and P, whereas LIG produced consistent increases in R and X across frequencies. The ALB+LIG model generated mixed responses, reflecting the competing effects of edema and ischemia. Normalized phase slopes differed significantly among groups (H=6.14, p=0.046), with post hoc testing showing significant divergence between the ALB and LIG models (p=0.041). Control limbs remained stable throughout monitoring. Pimonidazole staining confirmed hypoxic injury in the intervention limb. In the human pilot study, three participants completed five days of monitoring, demonstrating sustained signal acquisition, while electrode degradation limited data collection in the remaining participants. Conclusions: This preliminary feasibility study demonstrates that wearable EIM can continuously detect model-specific physiological changes associated with ACS in a large-animal model. These findings support further development and clinical evaluation of wearable EIM as a non-invasive monitoring technology for early ACS detection in trauma patients.

Mesenchymal Stromal Cells (MSC) face several heterogeneity challenges hindering clinical and commercial success. Employing a multiple response model, interplay between donor heterogeneity, and critical processing parameters (CPPs), effects on MSC potency and cell expansion attributes were investigated through computed composite attribute scores. Twelve unique CPP combinations were tested in thirteen marrow-derived MSC(M) and five adipose-tissue MSC(AT) training and test datasets, respectively. Donor heterogeneity and select CPP conditions affected a curated gene panel (surrogate for MSC potency); while MSC expansion was primarily influenced by CPPs. Model performances were evaluated against clinical effectiveness data from a previously deployed clinical trial; top-performing model predicted donor rankings coincided with clinical effectiveness data, validating the modeling approach used. Our model predicted that only 8% of tested donors were agnostic to CPPs; a majority (62%) of donors showed CPP-dependent optimal composite quality attributes, with MSC seeding density as a key driver; medium supplementation and oxygen preferences were highly donor dependent. Approximately 30% of donors performed poorly at all conditions tested and may be prospectively identified using a subset of genes (TGFB, VEGF, PDCD1LG1, PDCD1LG2, IDO). Model predicted optimal parameters worked for 69% of tested donors, while sub-optimal parameters worked for only 23% of donors and were confirmed in an independent CD14+ macrophage assay. Our integrated computational and experimental framework predictably identified interactive effects of donor heterogeneity and CPP conditions to optimize MSC potency attributes.

Purpose: RF-induced heating remains a major barrier to MRI access for patients with epicardial cardiac implantable electronic devices (CIEDs). Although ISO/TS 10974 Tier-3 transfer function (TF) methods are established for unbranched leads, no analogous framework exists for bifurcated leads, in which branch asymmetry and inter-branch coupling may substantially alter heating. We developed and validated a cumulative transfer function (cTF) framework to address this gap. Methods: Following ISO/TS 10974 Tier-3 formalism, we measured, calibrated, and validated cTFs for a commercial 35 cm bipolar epicardial lead at 1.5 T. The framework explicitly accounts for branch-specific response and cross-branch coupling. Validation was performed with 24 canonical lead configurations in a homogeneous phantom and, without recalibration, in a heterogeneous anthropomorphic pediatric phantom with clinically derived trajectories. A single-branch TF approximation served as a comparator. The validated cTF was applied to predict RF heating across adult and pediatric human models at multiple imaging landmarks. Results: Compared with the single-branch TF approximation, the cTF reduced prediction error by nearly 70% in the primary validation dataset. In secondary validation, the cTF maintained low error across clinically relevant trajectories and imaging landmarks. In human models, the framework revealed marked anatomy- and landmark-dependent variation in predicted heating for the tested 35 cm lead, with low predicted heating in pediatric models and substantially higher heating in selected adult chest and upper abdominal imaging scenarios. Conclusion: The cTF provides a validated framework for RF-heating assessment of bifurcated leads and substantially improves prediction accuracy over single-branch TF approximations that neglect branch coupling.

The suspension culture of pluripotent stem (PS) cells in stirred bioreactors poses a delicate balance between maintaining homogeneous cell dispersion and avoiding excessive shear stress that can compromise cell viability and pluripotency. In this study, we used computational fluid dynamics (CFD) coupled with a discrete particle method (DPM) to simulate iPS cell behavior in a 5 mL delta-impeller stirred tank. Our analysis revealed that upward flow at the tank bottom and downward flow at the top are critical for maintaining a stable suspension. To optimize the stirring protocol, we applied Bayesian optimization to identify a time-dependent stirring schedule that begins with a high-speed phase for resuspension, followed by a low-speed phase for sustained suspension with minimal hydrodynamic stress. The optimized schedule demonstrated improved suspension ratio and reduced slip velocity, indicating lower mechanical stress on cells. These findings provide engineering insights into scalable bioreactor operation, contributing to the design of robust iPS cell manufacturing systems.

Skeletal muscle is a living, perfused soft tissue whose viscoelastic behavior is shaped by both voluntary contraction and hemodynamic state. However, the independent and superimposed contributions of contractile loading and blood flow restriction (BFR) have not been quantified simultaneously in real time. Twenty-six healthy adults underwent multi-frequency ultrasound time-harmonic elastography (THE, 60-80 Hz) of the vastus lateralis under six conditions: rest, 15% and 30% maximal voluntary contraction (MVC) before BFR, passive BFR after 4 min of cuff inflation, and 15% and 30% MVC shortly after cuff release. Shear wave speed (SWS), reflecting elasticity, and penetration rate (PR), reflecting inverse viscous damping, were extracted using the k-MDEV inversion algorithm. BFR significantly elevated SWS at all three contraction levels relative to the corresponding pre-BFR measurements (Holm-corrected p [&le;] 0.011; dz = 0.54-2.13). PR decreased during resting BFR (dz = 1.34, p < 0.001) and at 15% MVC after cuff release (dz = 0.94, p < 0.001), but not at 30% MVC (dz = 0.21, p = 0.294). BFR-related changes reduced the SWS-force slope by 14.5% and the PR-force slope by 40.7%. Men exhibited a greater BFR-induced increase in resting SWS than women. These findings show that THE can distinguish contractile and hemodynamic contributions to skeletal-muscle viscoelasticity and provide complementary information on elastic and dissipative tissue behavior in vivo.

The human spine is a complex, coordinated biomechanical system. Physiologically, its tissues are also highly interdependent in terms of function and viability. The interaction between mechanical stress and biological/biochemical activity over time constitutes a key driver of spinal degeneration. Research to date providing mechanistic insights into this process has focused on individual components (vertebra and disc tissue analogues), in isolation or as basic functional units. However, many observations from individual units will not translate to whole spine behaviour. The intricate complexity of the spine requires novel experimental models (synthetic and biotic), which must consider the spine at an organ level and adopt an integrative approach that can capture the dynamics which govern its function. Here, we report the development of a biomimetic spinal model prototype, amenable to cellular integration, which is miniaturised to the in vitro scale to provide a controlled environment and testbed for axial biological mechanics. The research presented here encompasses more than a decade of systematic investigations during which the gradual emergence of key manufacturing innovations progressively enabled addressing an exceptionally complex bioengineering challenge - organotypic spine engineering. The model comprises the full anatomical range of spinal vertebrae/bones (C1 to Sacrum & Coccyx), reproduced using bioceramic materials, assembled in sequence into a relevant columnar architecture and mechanically connected end-to-end by biochemically active interfaces. A range of assessments examining anatomical design, material behaviour and manufacturing processes is presented. The work explores concepts such as longitudinal mechanobiology and multi-segment coupling as well as manufacturing strategies using autonomous materials and instrumentation. This prototype introduces for the first time columnar level behaviour and the ability to study time dependent adaptations. This model is important because it can support tissue maturation, evolving mechanical properties and adaptive behaviour and it represents an intermediate step between isolated skeletal tissue models and future organ-level spinal constructs.

Urinary stone disease is a common urological condition with increasing incidence, particularly in developed countries. Laser lithotripsy (LL) has become a preferred minimally invasive treatment due to its high precision and low tissue damage. Recent studies suggest that cavitation plays a critical role in stone damage during LL, and three-dimensional passive cavitation mapping (3D-PCM) has emerged as a promising tool for detecting these events. However, clinical translation of 3D-PCM remains challenging due to limitations in imaging depth, field of view (FOV), and procedural compatibility. Here, we present a large-FOV dual-modality imaging system (3D-PCM and B-mode ultrasound) based on a large-aperture planar ultrasound array. Through array optimization and model-based reconstruction, our system achieves an expanded FOV of ~40*40mm^2 at a clinically relevant imaging depth of ~110mm, while maintaining high spatial resolution of ~0.6 mm laterally and ~0.4 mm axially. In vivo experiments in a porcine model demonstrate that the reconstructed cavitation distribution correlates well with stone damage. Our technology has the potential to provide real-time treatment feedback during LL without disrupting the standard workflow.

Background: Cardiac resynchronization therapy (CRT) fails in 30% of patients, often due to suboptimal left ventricular pacing site (LVPS) selection. Current practice lacks tools for pre-procedural, patient-specific LVPS optimization within the accessible coronary sinus (CS) tributaries. This study aimed to develop a digital twin and an explainable ML-based clinical decision support framework to address this issue. Methods: Personalized 3D cardiac models incorporating ventricular anatomy, myocardial fibrosis, and CS anatomy were constructed from CT and LGE-MRI for 74 CRT candidates. Finite-element Eikonal simulations of biventricular pacing generated patient-specific electrophysiological features at candidate LVPS. A Machine Learning (ML) classifier was trained on a hybrid feature set of pre-procedural clinical variables and model-derived indices, validated by leave-one-out cross-validation. SHAP analysis provided a physiologically interpretable rationale for each prediction. The framework was applied to a pilot cohort of 19 patients with reconstructed 3D CS anatomy to generate a spatial likelihood map of CRT response across all clinically implantable pacing sites within each patient's CS. Results: The ML classifier outperformed the reference Feeny clinical calculator under LOO-CV (accuracy 0.78 vs 0.58; F1-score 0.75 vs 0.43), AUC=0.78, sensitivity=0.80, specificity=0.77. Bootstrap analysis yielded mean AUC=0.85 (95% CI 0.70-0.95). In the pilot CS cohort, the framework identified that 8 of 13 clinical non-responders had no accessible CS site predicted to yield a positive response, supporting redirection towards alternative pacing strategies. In the remaining 5, alternative implantable sites with high predicted response probability were identified. SHAP analysis confirmed that dominant predictors were patient-specific in their relative contributions, supporting individualized over heuristic-based LVPS selection. Conclusion: This pilot study demonstrates the feasibility of a digital twin and explainable ML framework as a pre-procedural clinical decision support tool for CRT planning, stratifying patients and identifying optimal implantable sites with transparent anatomical rationale. Prospective validation and regulatory evaluation are required before clinical deployment.

BackgroundQuantitative dynamic contrast-enhanced myocardial perfusion cardiovascular magnetic resonance (CMR) enables estimation of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). These measurements require an arterial input function (AIF), which is typically derived from the left ventricular blood pool. However, the contrast agent bolus undergoes dispersion during transport through the coronary vasculature before reaching the myocardial microcirculation. This may introduce systematic and spatially heterogeneous errors in MBF and MPR estimates. PurposeThis work provides an extended segmental analysis of bolus-dispersion-induced errors in quantitative myocardial perfusion MRI using previously established computational fluid dynamics (CFD) simulations in realistic porcine coronary artery models. The focus of the present analysis is the assignment of coronary outlets to myocardial segments and the resulting segmental variability of MBF and MPR errors. MethodsRealistic three-dimensional models of the left and right coronary arteries were extracted from an ex-vivo porcine imaging cryomicrotome dataset. The models extended down to the pre-arteriolar level and included 364 outlets for the left coronary artery and 104 outlets for the right coronary artery, with an average outlet diameter of 383 {+/-} 85 {micro}m. Blood flow was simulated under rest and stress conditions using OpenFOAM. Contrast agent transport was then modeled by solving the advection-diffusion equation using a gamma-variate bolus as input. Outlet concentration-time curves were analyzed using an indicator-dilution model to estimate MBF and MPR errors. Outlets were assigned to standardized myocardial segments, and segmental averages were evaluated with respect to coronary supply territory and travel distance from the model inlet. ResultsThe simulations demonstrated marked segmental heterogeneity of volume blood flow and bolus-dispersion-induced MBF and MPR errors. Errors increased with travel distance from the coronary artery inlet and were more pronounced in regions supplied by the right coronary artery, consistent with lower flow velocities and stronger bolus dispersion. The resulting systematic errors led to underestimation of MBF and overestimation of MPR, with segmental deviations reaching up to approximately 60%. ConclusionBolus dispersion in the coronary vasculature may lead to substantial segmental and location-dependent errors in quantitative myocardial perfusion MRI. This extended analysis indicates that dispersion-related bias is not spatially uniform, but depends on coronary supply territory, travel distance, and flow conditions. These effects should be considered when interpreting regional MBF and MPR estimates, particularly as automated quantitative myocardial perfusion CMR becomes more widely used.

Background: The saphenous vein (SV) remains the most widely used graft in coronary artery bypass grafting (CABG). However, graft failure over the years has compromised long-term outcomes. Preservation of the vascular endothelium is fundamental for vein graft patency, and hydrogen sulfide (H2S) a protective gasotransmitter, plays a significant role in vascular homeostasis. This study evaluated how different intraoperative preservation solutions modulate H2S-synthesizing enzymes and endothelial integrity. Methods: SV segments from 20 CABG patients were subdivided into five groups: Control (immediate fixation), normal saline (NS; 0.9% NaCl), autologous heparinized arterial blood (AHB), histidine-tryptophan-ketoglutarate (HTK) solution, and a damage group (no solution for 30 minutes). Structural integrity was evaluated by measuring endothelial coverage using light microscopy, and the expression of eNOS, CD31, and H2S pathway enzymes (CSE, CBS, and 3-MPST) was assessed by immunofluorescence (IF) and confocal microscopy to determine mean fluorescence intensity (MFI). Results: LM analysis revealed that AHB (89.66% {+/-} 3.02) and HTK (88.72% {+/-} 3.07) preserved endothelial coverage significantly better than NS (78.06% {+/-} 4.48) and the Damage Group (76.82% {+/-} 4.90; p < 0.001). In IF, all interventions reduced eNOS and CD31 expression compared to the control, but AHB and HTK maintained significantly higher levels than NS (p < 0.001). All three H2S-producing enzymes were detected in the GSV endothelium, with CSE being the most expressed isoform. The use of NS caused a marked depletion of these enzymes, while AHB and HTK showed specific superiority in preserving H2S synthesizing enzymes. Conclusions: The choice of preservation solution significantly affects endothelial integrity and the modulation of enzymatic H2S synthesis. NS proved to be deleterious to the endothelium, whereas AHB and HTK better preserved vascular structure and function, suggesting their clinical superiority for the preparation of venous grafts during CABG.

Acute subdural hematoma (ASDH) is a prevalent injury with high mortality and morbidity, often resulting from bridging vein (BV) disruption secondary to cortical relative motion. As a thin membrane enveloping the brain surface and anchoring BVs, the pia mater is hypothesized to play a critical mechanical role in cortical response and hence ASDH pathogenesis. Finite element (FE) head models are valuable tools to predict ASDH occurrence during impacts. However, the pia mater is often represented as an elastic material in existing FE head models, despite experimental evidence reporting its nonlinear mechanical behavior. In this study, both linear (Young's modulus of 11.5 MPa) and nonlinear (the stress-strain curve derived from pial tension tests) material models of the pia mater were implemented in one FE head model. The models were subjected to three experimental impact loadings, one of which was known to cause ASDH and two of which were not. Results demonstrated that, across all simulated impacts, the model with nonlinear pia mater properties predicted larger cortical displacements and BV responses than the linear model. For the impact with known ASDH occurrence, the predicted BV strain was 0.17 for the nonlinear model and 0.094 for the linear model, with only the former approaching the reported rupture strain range of the BV-superior sagittal sinus complex (0.29 {+/-} 0.13). These findings verified the mechanical importance of the pia mater in cortical responses and hence the prediction of ASDH, suggesting that conventional linear pia modeling might over-constrain cortical motion, leading to underestimation of BV strain and ASDH risk. The current study supported the adoption of experimentally derived nonlinear pia mater properties in FE head models to improve the reliability of ASDH prediction.