Journal of Biomechanical Engineering

Aortic stiffness is both heterogenous and anisotropic. Current non-invasive methods to estimate aortic stiffness are limited to characterizing the aortic tissue as isotropic due to the lack the techniques required to extract multi-axial strain from 3D dynamic images. Vascular deformation mapping (VDM) is a nonrigid image registration technique which has thus far been applied to map aortic growth using longitudinal imaging. In this study, we propose to use VDM to assess 3D aortic deformation by mapping diastolic and systolic images. During image registration process, penalty parameters are employed to fine-tune image alignment and penalize non-physiological deformations. These penalty parameters must be calibrated to ensure that VDM successfully reproduces multi-axial aortic motion patterns in health and disease. In this paper, we developed a calibration pipeline for these parameters using synthetic data. A rotation-free shell model was used to generate physics-based synthetic data on aortic motion incorporating patient-specific geometries, root motion, and blood pressure from a cohort of 14 subjects (healthy, Marfans syndrome and thoracic aortic aneurysm). An error metric was defined to quantify the quality of the VDM results. Furthermore, a k-means clustering technique was used to categorize the subjects into three clusters based on ascending aortic motion. Optimal penalty parameters were identified for each of the three clusters. The results indicated that patient clusters with smaller aortic root motion required larger rigidity penalty values. The calibrated parameters successively reduced errors in 3D displacement and multi-axial stretch compared to un-optimized VDM predictions, enhancing the accuracy of capturing aortic deformation from dynamic images. Among the different aortic regions, the ascending thoracic aorta exhibits the largest error reduction.

The mechanical behavior of right ventricular (RV) myocardium is governed by its anisotropic microstructure, yet constitutive models that account for fiber dispersion and enable reliable parameter identification remain limited. In this study, we propose a physics-embedded constitutive neural network framework for automated discovery of strain energy functions and microstructural parameters from experimental data. The model is formulated within an incompressible, orthotropic hyperelastic setting using invariant-based representations. Fiber, sheet, and normal directions are incorporated through a rotated structural basis, and dispersion effects are modeled using a generalized structure tensor approach. The framework is trained on multi-axial mechanical data from ovine RV myocardium, including uniaxial tension-compression and simple shear tests. We investigate two training scenarios: (i) full datasets containing both tensile and compressive regimes and (ii) datasets restricted to tensile loading. In both cases, the model accurately reproduces the measured stress-strain responses and identifies sparse, interpretable constitutive models which involve isotropic, anisotropic, and coupling invariants. However, the identifiability of microstructural parameters strongly depends on the available loading conditions. While tensile-only data yield higher predictive accuracy, they result in non-unique or biased estimates of fiber dispersion. In contrast, inclusion of compressive data enables consistent identification of dispersion parameters by separating fiber and matrix contributions. These results highlight the importance of multi-axial loading data for robust parameter identification and demonstrate the capability of constitutive neural network-based approaches for data-driven modeling of anisotropic soft tissues.

Patellar osteochondral allograft (OCA) transplantation is widely used to treat large full-thickness cartilage defects, yet long-term failure and reoperation rates remain high. Although surface congruity and osseous integration are emphasized clinically, cartilage thickness and mechanical compatibility between donor and recipient are not considered. Our previous work suggests that cartilage thickness mismatch can amplify local deformation at the graft boundary, potentially compromising graft longevity. This study investigates how combined mismatches in cartilage thickness and mechanical properties influence the local strain environment at the patellar OCA interface. Simplified two-dimensional axisymmetric finite element models of patellar OCA repair were developed in ABAQUS. Donor-to-recipient cartilage thickness ratios ranging from 0.33 to 3.25 were evaluated together with donor-recipient Youngs modulus mismatches (2.5-7.0 MPa). Cartilage was modeled using homogeneous linear elastic and functionally graded material formulations to account for depth-dependent stiffness. A compressive pressure of 1.0 MPa was applied to represent patellofemoral joint loading, and peak compressive and shear strains were quantified at the graft boundary. Cartilage thickness mismatch produced localized high-strain regions (HSR) of compressive and shear strain at the donor-recipient interface that were absent in thickness-matched constructs. Strain amplification increased with both thickness and mechanical property mismatch. Compressive strain exhibited directional asymmetry, with donor-side-thicker configurations producing greater amplification than recipient-side-thicker configurations. Incorporating depth-dependent cartilage stiffness reduced peak strain magnitudes but did not eliminate mismatch-driven strain amplification. These findings demonstrate that cartilage thickness and mechanical disparity can create HSR at the patellar OCA graft boundary that may predispose grafts to impaired integration and long-term failure.

The aorta, normally resilient to hemodynamic stresses, becomes vulnerable to structural failure due to diverse conditions that weaken the wall. We injected fluid into excised specimens of human ascending aorta with pressure monitoring to quantify the impact of clinical and histological factors on mural damage. Two modes of medial injury emerged with distinct pressure tracings. Extravasation was characterized by diffuse infiltration of fluid with widespread damage of smooth muscle cells and collagen fibers but limited separation of elastic lamellae. By contrast, delamination was characterized by marked separation of elastic lamellae along a single plane with damage to cells and fibrillar matrix restricted to adjacent laminae. Aging, aortic dilatation, and family history associated with lower pressures causing delamination, whereas a diagnosis of hypertension associated with higher pressures suggesting resilience to dissection. Collagen fraction adjacent to delamination correlated with higher pressures as did decreased smooth muscle cell density and increased glycosaminoglycan fraction, although several clinical and histological variables were interrelated. Protein cross-linking strengthened and enzymatic digestion of collagen weakened the aortic wall, while acute cell lysis with detergent had no effect. We conclude that increased functional medial collagen has an adaptive protective role in aortic remodeling rather than signifying medial degeneration.

Background and ObjectiveThe foot-ankle complex is a highly articulated and mechanically constrained system, often simplified as a chain of few rigid segments, neglecting many bone-to-bone motions and raising questions about the accurate representation of interaction with ground. This study proposes a new reduced-order multibody formulation that captures intrinsic kinematic constraints of the foot through motion synergies. MethodsBones kinematic coupling, or motion synergies, were experimentally derived from weight-bearing CT scans using principal component analysis. These couplings were embedded in a synergy-based multibody kinematic optimization framework describing the foot-ankle with five degrees of freedom: ankle flexion; foot adduction, pronation, and arching; and toe flexion. Model accuracy was evaluated against bone-level experimental kinematics. The model was applied to gait data from healthy, flat, and diabetic feet and compared with a standard multi-segment foot model, assessing robustness by progressively reducing the number of skin markers. ResultsAverage errors were about 1{degrees} and 0.5 mm when using subject-specific synergies and below 7{degrees} and 4 mm when scaling the generic model, matching or exceeding the accuracy of existing models. Reliable reconstruction was obtained using only four foot markers. In clinical gait analysis, the model showed superior discrimination between populations and enabled assessment of transverse arch deformation, not accessible with conventional models. ConclusionThe proposed synergy-based model provides an accurate, low-complexity framework for reconstructing bone-level foot and ankle kinematics, substantially simplifying gait analysis while improving biomechanical interpretability. This framework supports future integration with dynamic models aimed at studying load transmission in the foot.

Background: Coronary artery bypass grafting (CABG) remains the standard of care for complex multivessel and left main coronary artery disease. However, current preoperative planning remains largely subjective, relying on qualitative interpretation of coronary CT angiography (CCTA), operator-dependent stenosis grading, and fragmented multi-software workflows. Invasive fractional flow reserve (FFR), the reference standard for physiologic lesion assessment, is infrequently acquired preoperatively, leaving distal anastomosis planning without an objective hemodynamic basis. Methods: We developed a fully automated, AI-powered platform that converts routine CCTA into a patient-specific CABG planning workflow through five integrated modules: nnU-Net based segmentation of coronary lumen and calcification; quantitative morphological and topological characterization generating more than thirty descriptors; automated stenosis detection using a local reference-radius formulation; a nine-point composite scoring framework for distal anastomosis site selection incorporating luminal caliber, landing-zone length, calcification burden, distal perfusion reserve, and bifurcation proximity; and interactive virtual graft construction coupled to a distributed reduced-order solver for pre- and post-bypass FFR estimation. Results: Lumen segmentation achieved a mean Dice similarity coefficient of 0.96 {+/-} 0.01, whereas calcium segmentation achieved 0.73 {+/-} 0.15 on the held-out cohort. Platform-derived FFR demonstrated strong agreement with invasively measured FFR (r=0.96, mean absolute relative difference 1.73 {+/-}1.42%) across the evaluated lesions, supporting the physiologic validity of the reduced-order hemodynamic solver. End-to-end analysis from raw CCTA to hemodynamic assessment and virtual graft planning was completed in approximately seven minutes per case on a standard workstation, representing a substantial reduction in processing time compared with conventional multi-tool and CFD-based workflows. Conclusions: The proposed platform demonstrates the feasibility of rapid, reproducible, and physiology-informed CABG planning using routine CCTA. By integrating anatomical characterization, automated target-site analysis, virtual graft construction, and reduced-order hemodynamic assessment into a single workflow, the framework provides objective, quantitative surgical decision support compatible with routine clinical workflows. Keywords: Coronary artery bypass grafting (CABG); Fractional flow reserve (FFR); Coronary CT angiography (CCTA); Surgical planning

BackgroundBone graft biomaterials play a critical role in bone regeneration by influencing osteoblast differentiation and mineralization. However, comparative data regarding the osteogenic potential of commonly used graft materials under standardized conditions remain limited. Method and materialIn this in vitro experimental study, osteoblast-like cells (MG-63) were cultured with four bone graft materials, including Bio-Oss, Cerasorb, Bio-Tiss Cerabone, and Pro Osteon. The relative mRNA expression of osteogenic markers (COL1 and OPN) was evaluated at 1, 7, 14, and 21 days using real-time PCR. Alkaline phosphatase (ALP) activity and mineralization capacity were also assessed using colorimetric assay and Alizarin Red staining. Data were analyzed using one-way ANOVA and Tukey post hoc test (P < 0.05). ResultsSignificant differences were observed among the tested materials across all evaluated parameters. Bio-Oss and Cerasorb demonstrated higher gene expression levels and ALP activity compared to Bio-Tiss Cerabone and Pro Osteon (P < 0.05). Mineralization analysis showed significantly greater calcium deposition in the Bio-Oss and Cerasorb groups, whereas Pro Osteon consistently exhibited the lowest osteogenic performance. ConclusionBone graft biomaterials significantly influence osteogenic activity in osteoblast-like cells. Bio-Oss and Cerasorb showed superior osteogenic potential, while Pro Osteon demonstrated weaker performance. These findings highlight the importance of material properties in optimizing bone regeneration.

Mechanical properties of epithelial tissues play essential roles in morphogenesis and physiological function. In this study, we analytically derived the in-plane bulk modulus, shear modulus, and Poissons ratio of a three-dimensional cell vertex model of epithelial monolayers. We showed that the model can robustly reproduce a near-zero in-plane Poissons ratio, a mechanical feature reported in cultured epithelial tissues. Numerical simulations further confirmed that the theoretically predicted Poissons ratio accurately describes the response of the model under finite, biologically relevant strains. In addition, the model exhibits not only morphological bistability between squamous-like and columnar-like states, but also mechanical bistability characterized by distinct elastic responses. Together, these results provide a minimal three-dimensional framework that links cell-scale mechanical interactions and epithelial morphology to tissue-scale elastic properties.

AO_SCPLOWBSTRACTC_SCPLOWMuscle efficiency decreases with increasing size, largely due to a relative decrease in its mechanical output. Muscle mechanical output depends on its activation, strain, and strain rate and thus varies between different muscles within a limb during locomotion. Distinct muscle coordination patterns are required for efficient cycling, and so we would expect that the coordination patterns for efficient cycling or indeed locomotion would change across animal sizes. We tested whether muscle coordination would change with muscle size using data derived from human cycling: this paradigm allowed for controlled changes in both crank torque and cadence, allowing the multifactorial problem of muscle power output to be decomposed. We used kinematic and pedal data from 12 cyclists undergoing steady pedalling at cadences from 80 to 140 r.p.m. and generated musculoskeletal simulations of their movements. We introduced novel multisegment muscle models in the simulation that incorporated the internal muscle mass and thus accounted for the scaling effects of muscle tissue inertia. We solved the simulations for the muscle activity that was required to minimise the metabolic cost during cycling for each condition. The masses of the muscle models were scaled across five orders of magnitude. The predicted muscle activations were classified by Principal Component analysis to identify whether the coordination of muscle activity was modulated across models with different sized muscles. Analysis of variance revealed significant changes in coordination at the large-scale factors. This study shows how the coordination of muscle activity during locomotion will likely change across a range of body sizes due to the non-linear effects of the inertial mass within the muscle tissues.

Intro: Characteristics of the pulse wave transmitted through the carotid arteries are predictive of cognitive decline and cerebrovascular health in humans. This study aimed to identify risk factor trajectories in childhood, adolescence and early adulthood that are associated with forward compression wave intensity (FCWI) in the common carotid artery in adults aged 28 years. Methods: Systolic blood pressure (SBP), body mass index (BMI) and fasting blood glucose (FBG) measured at multiple time-points when participants were aged between 8-20 years were included in a trajectory analysis. At age 28 years, FCWI was measured in 402 (M=206, F=196) participants who underwent a Duplex ultrasound assessment of the common carotid artery. Statistical analysis assessed differences in FCWI between each trajectory group for males and females separately. Results: In males, four trajectory groups were identified for BMI, three for SBP, and two for FBG. In females, three trajectory groups were identified for BMI, SBP, and FG. In males, having higher BMI (P=0.006), SBP (P=0.021) and FBG (P=0.002) from ages 8-20 years was associated with greater FCWI at age 28 years. In females, no associations were found between FCWI at age 28-years and trajectory groups for BMI (P=0.185), SBP (P=0.289) or FBG (P=0.070). Conclusion: Having high BMI, SBP and FBG throughout childhood, adolescence and early adulthood was associated with higher FCWI in the carotid artery at age 28 years in males, but not females. This may have a direct impact on the etiology of cognitive decline and cerebrovascular disease in later life.

PurposeTo characterize the frequency-dependent bioimpedance properties of major ocular tissues in intact ex vivo porcine eyes under simulated surgical conditions and evaluate tissue separability at discrete frequencies. MethodsBioimpedance spectra were acquired from sclera, corneal epithelium, iris, lens, vitreous, and retina in intact ex vivo porcine eyes using a two-electrode probe and a precision LCR meter over 5 kHz to 1 MHz. Measurements were obtained under balanced salt solution and ophthalmic viscosurgical device conditions. Probe-tissue contact was verified by microscope visualization and optical coherence tomography. Tissue separability at 5, 50, 100, and 900 kHz was evaluated using global and pairwise statistical comparisons, effect sizes, and ROC-based separability metrics. Robotic-stabilized and handheld measurements were also compared. ResultsOcular tissues demonstrated distinct, frequency-dependent impedance magnitude distributions. Across sampled frequencies, 60% to 80% of tissue pairs showed significant differences after multiplicity correction. Median pairwise effect sizes ranged from Cohens d = 0.48 at 5 kHz to 1.04 to 1.06 at 50 to 100 kHz. Median ROC-based separability was 0.91 at 5 kHz and 0.76 to 0.77 at 50 to 900 kHz. Robotic-stabilized measurements showed lower variance than handheld measurements, although tissue-specific impedance ranges and frequency-dependent trends were preserved across acquisition modes. ConclusionsMajor ocular tissues exhibit reproducible, frequency-dependent bioimpedance signatures in intact ex vivo eyes under simulated surgical preparation. These findings establish a physiologically relevant ocular impedance reference dataset and support bioimpedance as a complementary modality for tissue differentiation in ophthalmic microsurgery.

BackgroundThe pivot shift (PS) test is the most specific clinical examination for anterolateral rotational instability in ACL-deficient knees, yet grading remains subjective, as evidenced by poor inter-observer reliability, particularly for Grade 2. Since low-grade (Grade 1) versus high-grade (Grades 2/3) PS is the threshold for recommending lateral extra-articular augmentation, performing the test in awake clinic patients limits grading reproducibility and introduces variability in surgical decision-making. Existing methods to quantify the pivot shift usually require examiner-performed testing under general anaesthesia. No prior approach has ascertained PS grading from a separate patient-performed functional movement. PurposeTo evaluate the feasibility of a machine learning (ML) classifier, trained on kinematic ultrasound bone-tracking signals acquired during a patients sit-stand-sit (SSS) knee movement, to predict their PS grade, and to clinically validate its ability to differentiate low versus high-grade PS. MethodsUltrasound bone-tracking kinematic data were collected during SSS manoeuvres in 23 ACL-injured patients using the GATOR device, and ground truth PS grades (0-3) were assigned under general anaesthesia by fellowship-trained orthopaedic sports surgeons. From the data collected, Leave-one-out cross-validation (LOOCV) was used to train the ML classifier. Clinical SSS data from 6 ACL-deficient patients was used for independent held-out validation of their low-grade (Grade 1) versus high-grade (Grade 2/3) PS. Multiple deep learning architectures (XceptionTime, InceptionTime, FCN, ResNet, ResCNN) and training strategies (including mixup augmentation and supervised contrastive learning) were tested. Performance was measured by one-versus-rest (OVR) AUC under LOOCV and by AUC (low vs high grade PS) from the held-out patient sessions. ResultsThe ML classifier achieved a maximum OVR AUC of 0.928 {+/-} 0.084 under LOOCV. Classifier performance increased with pivot-shift severity: Grade 3 was identified most reliably (AUC ~0.81; sensitivity 0.70-0.80), whereas Grade 2 remained the most challenging boundary (sensitivity 0.20-0.75 across configurations). For the clinically relevant binary classification of low-versus high-grade pivot shift, the classifier generalised well to a completely unseen patient cohort (AUC 0.889; accuracy 0.860; sensitivity 0.850; minimum-class sensitivity 0.767). ConclusionThe study demonstrates that kinematic ultrasound bone-tracking during sit-stand-sit contains transferable information about rotational instability severity in ACL-deficient patients, and represents the first reported approach to predict pivot shift grade from a patient-performed functional movement. The strong cross-validation performance confirms that the signals contain meaningful PS grade-discriminative information, but larger datasets targeting 50-100 sessions per grade will be required to achieve patient-level generalisation and advance this novel rotational instability assessment tool toward full clinical adoption. Level of EvidenceLevel IV, diagnostic feasibility study.

BackgroundPulmonary artery (PA) wave reflection is a key determinant of right ventricular (RV) afterload. RV function is the most important factor determining long-term prognosis in patients with surgically repaired tetralogy of Fallot (rTOF). This study aimed to evaluate PA wave reflection in rTOF using RV pressure phase plane (PPP) analysis, and to identify the clinical, morphological, and hemodynamic characteristics associated with increased PA wave reflection in patients with rTOF. MethodsAugmentation pressure (AugPr) during late systole was quantified using the inflection point of systolic dP/dt on the PPP. The ratio of AugPr to RV systolic pressure (RVSP) was defined as the AugPr index. The study included 87 patients with rTOF (mean age, 15.9 {+/-} 10.0 years), 17 control subjects (13.3 {+/-} 6.3 years), and seven patients with pulmonary arterial hypertension (PAH) (16.4 {+/-} 11.7 years). The rTOF cohort was categorized according to surgical procedure: pulmonary valve-sparing repair (PVS, n = 5), transannular patch repair (TAP, n = 34), and the Rastelli procedure (n = 48). ResultsThe prevalence of AugPr was 0% in the control group, 100% in the PAH group, and 26.4% in the rTOF group (p < 0.0001). Among the surgical subgroups, the prevalence was 0% in PVS, 14.7% in TAP, and 41.7% in the Rastelli group (p < 0.0027). AugPr and the AugPr index were significantly higher in the Rastelli group than in the other two groups (p = 0.0447 and 0.0433, respectively). In addition, AugPr showed significant correlations with RVSP, RV outflow tract obstruction, maximal dP/dt, and pulmonary regurgitation grade (all p < 0.05). ConclusionsPA wave reflection can be clearly visualized using PPP. The Rastelli group demonstrated a higher prevalence and magnitude of PA wave reflection, suggesting a greater increase in RV afterload compared with other surgical repair types.

Various ankle-foot conditions (e.g., fractures, diabetic foot ulcers, and post-surgical recovery) require periods of complete non-weightbearing followed by gradually increasing partial loadings. However, existing assistive devices often provide inconsistent or uncomfortable offloading during gait. Additionally, prolonged proximal leg offloading can contribute to muscle atrophy, reduced bone density, and overuse of other body segments. We present a novel offloading ankle-foot orthosis (OLAFO) designed to overcome these limitations. The OLAFO features a patient-specific load-bearing shank brace, designed through a digital workflow and fabricated from a 3D-printed core reinforced with carbon-fiber composite lamination. Interlocking serrated side struts, adjustable in 2 mm increments, modulate load sharing between the shank and plantar surfaces. Furthermore, the OLAFO incorporates contact plates with a rocker profile informed by roll-over-shape measurements to support forward progression and gait symmetry. Proof-of-concept biomechanical verification in one able-bodied participant evaluated complete offloading, five partial-loading levels, and normal gait using a pressure walkway to compute vertical ground reaction forces and impulses. In complete offloading, the affected foot generated no contact pressures. Across partial-loading levels, the foot impulse increased from 14% to 53% of the total load and scaled linearly with strut height adjustments, supporting clinician-prescribed loading increments. Contralateral stance duration increased only modestly compared to commonly used assistive devices, indicating reduced compensatory loading on the intact limb. These findings demonstrate the proof-of-concept feasibility of the OLAFO, highlighting its potential for verifying full offloading and prescribing partial-loading targets during rehabilitation. Future research will evaluate performance across patient populations and clinical rehabilitation tasks.

Thin structures such as heart valves and aortic dissection flaps interact dynamically with blood flow in human vessels. Their flexibility and capacity for large deformations generate complex, highly transient hemodynamic patterns over the cardiac cycle. Accurately resolving these interactions remains challenging for conventional boundary-fitted fluid-structure interaction approaches. We present an immersed boundary method for simulating thin structures in incompressible flow on unstructured grids. The method couples a stabilized finite element fluid solver with a nonlinear, rotation-free shell formulation through a direct forcing immersed boundary approach. The framework supports both weak (explicit) and strong (implicit) time-coupling strategies, enabling stable simulations over a wide range of solid-to-fluid density ratios. Hydrodynamic forces acting on thin structures are computed from fluid solutions sampled on both sides of the structure, allowing accurate force reconstruction for zero-thickness shells. To our knowledge, this is the first immersed boundary formulation that couples an unstructured finite element fluid solver with a two-dimensional, rotation-free shell model to simulate interactions between thin structures and incompressible flow. Fluid-structure coupling is achieved using predefined finite element shape functions, which provide consistent projection between Eulerian and Lagrangian fields without additional interpolation procedures. The framework is validated using three-dimensional benchmark problems involving thin structures. Then, valve-like model is used to compare strong and weak coupling strategies. Finally, the method is applied to an idealized type-B aortic dissection model. The proposed approach is implemented within the open-source software CRIMSON, a finite element platform for cardiovascular simulation.

ObjectiveRecently, alternatives to animal testing, such as new approach methodologies, are being developed in the orthopedic research field; animal models still provide valuable insights into the pathogenesis of knee osteoarthritis (OA). However, commonly used models develop OA much more rapidly and severely than those observed in human patients. We aimed to develop a novel murine model that closely mimics the slow progression of human OA with posterior Cruciate ligament (PCL) rupture. Design12-week-old C57BL/6 mice were induced to PCL-rupture (PCL-R) by manually applying an external tibial posterior translation force. We analyzed joint kinematics, histological observations, and bone structure to confirm the absence of concurrent injury on day 0. Then, joint stability and the pathophysiological progression of knee OA were analyzed at 8, 16, and 34 weeks post-PCL-R. The destabilized medial meniscus (DMM) model was also analyzed to compare the OA progression. ResultsNon-invasive PCL-R intervention induced the complete rupture in the central region of PCL without concurrent injury. The PCL-R group showed larger posterior tibial deviation than the INTACT (P=0.008). Regarding the range of motion in the PCL-R group, there was no limitation in range of motion on day 0, but extension limitations occurred at weeks 16 and 34 weeks. Histologically, articular cartilage degeneration in PCL-R was milder than DMM. In the subchondral bone, micro-CT reconstruction images indicated that, compared with the INTACT group, the DMM group observed progressive subchondral bone formation from 16 weeks post-surgery. In contrast, the PCLR group maintained the subchondral bone structure even at 34 weeks. ConclusionsPCL-R model induced mild abnormal mechanical stress depending on posterior instability, and cartilage degeneration occurred more slowly in this model than in DMM models.

PurposeTo address the limitations of single-distance, 1D performance metrics in RF coil design. This work introduces a multi-objective, volume-of-interest (VOI) based analysis to systematically characterize the trade-offs between power efficiency, pSAR efficiency, and homogeneity as a function of dipole length (l) and distance-to-load (d) for multiple dipole geometries and target anatomies. MethodsElectromagnetic simulations of straight and end-meandered dipole antennas were performed with varying lengths (100-500 mm) and distance-to-load (1-81 mm) over three anatomical targets (prostate, kidney, heart). Homogeneity, power efficiency, pSAR efficiency, and load sensitivity performance metrics were calculated within each anatomical VOI. Inter-element coupling at variable d was assessed in a 3-element array, and a subset of single-element simulations was experimentally validated using B1+ mapping. ResultsA fundamental trade-off was found between power efficiency and pSAR efficiency. Optimal power efficiency was achieved with shorter dipoles (150 mm < l < 300 mm) closer to the sample (d < 30 mm), while optimal pSAR efficiency and homogeneity were achieved with longer dipoles at further from the sample (d > 60 mm). Inter-element coupling increased with distance-to-load but could be managed by increasing element spacing. Experimental measurements were in good agreement with simulation trends. ConclusionIncreasing distance-to-load to 40-60 mm, compared with commonly used distances of 20-30 mm, offers a practical strategy for improving pSAR efficiency and homogeneity with a minimal decrease in power efficiency. This work provides a quantitative analysis that enables RF coil designers to make informed, data-driven decisions when developing next-generation body arrays and suggests that unshielded end-meandered dipoles could be an optimal transmit element geometry.

BackgroundThe clinical assessment of knee stability after an Anterior Cruciate Ligament (ACL) injury is routinely conducted via operator-dependent physical examination tests (i.e. pivot shift) and standardized patient-reported outcomes. Unfortunately, both are unable to perceive and quantify the subtle rotational biomechanical deficiencies from an ACL tear. Although specialized laboratory-based motion capture systems may provide objective measurements, they are found in research institutions and thus, are not suitable for clinical use. In contrast, GATOR PRO is a clinic-based multimodal wearable sensor system that uses a machine learning (ML) model (ensemble deep learning) to differentiate and classify its data outputs for assessing in-vivo dynamic rotational knee stability. ObjectiveThe purpose of this study is to validate the deep machine learning model and its performance used in GATOR PRO, which integrates knee-mounted Inertial Measurement Units (IMUs) with ultrasound images to derive high-fidelity in-vivo biomechanical rotational data. Based on this data collected by the GATOR PRO, it is hypothesized that the model can effectively classify knee stability after ACL injury and reconstruction. MethodsThis prospective clinical study at Singapore General Hospital (SGH) (CIRB 2019/2766, PDPA-compliant) aimed to enroll 60 patients (30 ACL-deficient, 30 ACL-reconstructed [&ge;]6 months post-surgery). At the halfway point of the clinical trial, 29 patients (8 ACL-deficient, 21 ACL-reconstructed [&ge;]6 months post-surgery) were recruited through physician referral at SGH outpatient clinics to perform standardized chair-stand tests. An ensemble deep learning model that combines convolutional (EfficientNet) and time-series (InceptionTime) classifiers is used to output binary stability classifications (ACL-deficient/ACL-reconstructed). The models performance was evaluated using 10-fold stratified cross-validation with patient-wise splitting, repeated across 100 random seeds to assess variability. ResultsAt the halfway point of the trial, the ensemble model performance with regard to the Receiver Operating Characteristic area under the curve (ROC-AUC) was 0.8365 (SD: 0.042, p-value < 0.001), and the classification accuracy was 75.9% (SD: 3.2%) when the model was tested on the 29 CIRB-approved patients. For the ACL-reconstructed class, the performance indicators were as follows: precision 71.4%, recall 93.8%, F1-score 81.1%. For the ACL-deficient class, the indicators were: precision 87.5%, recall 53.8%, F1-score 66.7%.Against the clinical pivot shift tests low sensitivity (24-32%), the model delivers an almost 2X better sensitivity (53.8%)[2, 3], with a comparable specificity (93.8% vs. 90-98%) ConclusionThe multimodal machine learning model was able to perform at a level that was relevant to clinical classification (AUC-ROC 0.8365, accuracy 75.9%) in differentiating between ACL-deficient and ACL-reconstructed knees. Moreover, the model demonstrated far superior sensitivity than previously published estimates for manual pivot shift testing (53.8% vs. 24-32%). These findings demonstrate that rotational knee instability can be reliably differentiated in clinical settings with a ML model deployed on GATOR PRO data.

A major challenge in neuroscience is to predict how currents in nanodomains affect voltage and ionic concentrations. Cable and Rall theory provide analytic current-voltage relations by neglecting concentration gradients, and the impact of concentration gradients is usually studied numerically with the Poisson-Nernst-Planck (PNP) model. A precise quantitative understanding of the combined dynamics remains limited because analytic current-voltage-concentration relations are missing. In this work we derive such relations using a novel approach based on cross-diffusion equations. For narrow cylindrical domains, we derive time-dependent and steady-state expressions that explicitly show how currents affect voltage and ionic concentrations. We find that the influx of only one ion can significantly change the concentrations of all the other ions even if no channels for these ions are present. After a current injection we compute a biphasic voltage transient where the small-time asymptotic corresponds to the steady-state solution of the cable equation. We show that the accuracy of cable theory prediction for the voltage depends on how the current is distributed among the various ions. Finally, we develop an iterative method to accurately compute steady-state profiles for voltage and concentrations using first-order results by subdividing a cylinder into small segments.

Actin-bundle organization is essential for vascular smooth muscle cell mechanics and is implicated in actin-related diseases. However, it remains unclear how cell stretching affects intracellular actin bundles when actin polymerization is impaired. Here, we performed live imaging of Latrunculin A-treated A7r5 vascular smooth muscle cells in a stretch chamber. GFP--actinin imaging showed that Latrunculin A reduced actin-bundle coverage while periodicity was maintained. Subsequent mechanical stretch disrupted both actin-bundle coverage and periodicity. We constructed a stochastic filament bundle model in which actin filament length, actin crosslinking protein dynamics, external stretch, and myosin-driven contractile shortening determine bundle connectivity. The model generated non-spanning, collapse, and persistent states based on spanning connectivity before and after stretch, shaped by filament length and applied strain. A reduced model further showed that these states are governed by a balance between connectivity formation and stretch-induced loss. Together, our results suggest that reduced actin polymerization destabilizes intracellular actin-bundle organization under mechanical stretch, providing a mechanism linking actin polymerization defects to mechanical fragility in vascular smooth muscle cells.