Journal of Biomechanical Engineering

Anterior cruciate ligament (ACL) injuries disproportionately affect female adolescent athletes, with hormonal influences implicated in this sex disparity. However, the relationship between pubertal hormonal changes and ACL gene and protein expression remains poorly understood. This study characterized hormone receptor expression and transcriptional profiles in the anteromedial (AM) and posterolateral (PL) bundles of female porcine ACLs before and after puberty. ACL bundles were collected from pre-pubescent (8 weeks) and post-pubescent (>8 months) female Yorkshire cross-breed pigs (n=6/group) and analyzed using gene expression profiling, western blotting, and immunofluorescence. Pre-pubescent ACLs exhibited greater expression of primary matrix genes (COL1A1, COL1A2, ELN, TNMD), suggesting active matrix synthesis, while post-pubescent ACLs showed elevated secondary matrix genes (COL3A1, LUM, COMP), indicating a homeostatic state. Notably, estrogen receptor alpha (ER) gene and protein expression were significantly greater in post-pubescent ACLs, particularly in AM bundles, whereas G-protein coupled estrogen receptor (GPR30) expression was elevated pre-puberty. Both receptors were distributed homogeneously throughout the tissue. Progesterone receptor protein expression was not detected in any samples. Histologically, post-pubescent ACLs demonstrated decreased cellularity and thicker fascicles compared to pre-pubescent tissues. These findings indicate that ACL sensitivity to estrogen varies across development, with increased ER expression post-puberty potentially rendering the ligament more responsive to circulating estrogen. This work provides foundational evidence for age-dependent hormonal responsiveness in the ACL and motivates further investigation into how sex hormones influence ACL injury risk in adolescent females.

Pathologic vertebral fractures are a major complication in metastatic spine disease. However, current clinical scores, such as Spinal Instability Neoplastic Score (SINS), show limited predictive capability, particularly within the indeterminate range where most clinical uncertainty lies. This study aimed to develop and evaluate quantitative computed tomography (qCT)-based subject-specific finite element (SSFE) models to predict vertebral strength in presence of different metastatic lesion types. Twelve ex vivo human spine segments, each containing one metastatic (n=12) and one adjacent control vertebra (n=12), were scanned using qCT and calibrated using a calibration phantom. Homogenised nonlinear finite element models were developed with spatially heterogeneous, isotropic, density-dependent material properties and loaded under uniaxial compression corresponding to 1.9% apparent strain. Ultimate failure load, stiffness, and strain distributions were compared between metastatic and control vertebrae. Predicted failure load ranged from 0.2 kN to 6.2 kN (mean. {+/-} standard deviation: 1.8 {+/-} 1.6 kN metastatic; 1.7 {+/-} 1.5 kN control), with no statistically significant difference between groups (p > 0.05). Normalised failure load varied widely, reflecting lesion-specific mechanical heterogeneity. Lytic lesions generally weakened vertebrae, whereas mixed and blastic lesions occasionally enhanced strength, likely due to localised sclerosis or reactive bone formation. High compressive axial strains (greater than 0.019) were frequently concentrated near the endplates, particularly in lytic vertebrae. qCT-derived bone mineral density strongly correlated with failure load (R{superscript 2} = 0.74-0.77). These findings highlight the complexity of metastatic vertebral mechanics and demonstrate that qCT-based SSFE modelling provides a quantitative framework for assessing fracture risk, complementing conventional imaging-based tools.

Intervertebral disc degeneration (IVDD) compromises disc structures and mechanics, yet systematic evaluations of the mechanical responses and their relationship to morphological changes in preclinical models remain limited. This systematic review and meta-analysis synthesized mechanical and morphological alterations following experimental disc injury in in vivo animal models. Searches of MEDLINE, EMBASE and Web of Science databases were conducted in accordance with PRISMA guidelines. Study quality and risk of bias were assessed using modified CAMARADES and SYRCLE tools. Twenty-eight studies were included. Pooled analyses showed significant reductions in stiffness, Youngs modulus, and disc height, and significant increases in range of motion and degeneration grade, indicating both mechanical and structural deterioration. Youngs modulus appeared to be the most sensitive marker of functional degeneration. By contrast, creep and other viscoelastic responses showed non-significant changes. High heterogeneity was evident across studies, reflecting variability in injury models, species, timepoints, and testing methods. Evidence of publication bias was detected in several domains, and moderate methodological quality was noted with overall insufficient blinding and lack of sample size calculations. In vivo animal models of IVDD demonstrate robust and consistent mechanical and morphological degeneration after injury. Youngs modulus is a sensitive mechanical indicator, supporting its use in future preclinical research. Standardization of outcome definitions, methodology, and reporting is essential to improve comparability and enhance translation of preclinical findings to clinical research.

BackgroundAortic microstructure-function relationships and the pathophysiology of how medial degeneration leads to aortic dissection remain poorly defined. We aimed to determine how degeneration of individual components of the extracellular matrix (ECM), namely elastin, collagen, and proteoglycans, influence biomechanical properties of aortic tissue through an improved, disease-motivated enzymatic digestion framework. MethodsPorcine aortic tissue was sectioned into 200 {micro}m thick samples in the media, and progressively digested with elastase or collagenase for selective degradation of these ECM components. Full thickness human aortic tissues were treated with chondroitinase, hyaluronidase, and heparinase to completely remove proteoglycans. Biomechanical characterization was performed using planar biaxial tensile testing, from which low- and high-strain modulus, transition-zone behaviour, strain-energy density, and energy loss were derived. Degree of elastin fiber degradation was analyzed using two photon excitation fluorescence imaging. Analysis of collagen degradation was performed using picrosirius red staining under brightfield and polarized light. Alcian blue staining was used to evaluate proteoglycan content. ResultsInduced fragmentation and disorganization of elastin fibers reduced low-strain load bearing capacity, evidenced by reduced low-strain modulus, strain-energy density, and transition zone stress, along with reduced energy loss. Targeted collagen disorganization similarly reduced strain-energy density and decreased strain at the onset of transition, consistent with premature collagen recruitment, and was accompanied by reductions in high strain modulus and energy loss with increasing collagen degradation. Proteoglycan removal decreased energy loss and was found to modulate low- and high-strain behaviour, including reduced strain-energy density and strain at onset of transition, and increased high strain modulus. ConclusionsThrough targeted modelling of ECM degenerative features on aortic tissue mechanics, we have identified distinct disease-associated biomechanical roles for major matrix constituents, with overlapping effects. These findings delineate mechanical consequences of component-specific matrix degeneration while underscoring the complex, multifactorial nature of structure-function relationships in aortic disease.

BackgroundComputational modeling is a tool being deployed for orthopaedic solutions but its use in the hand and wrist remains limited. This work used a model to simulate a clinically relevant provocative scaphoid shift maneuver (SSM) with different levels of scapholunate interosseous ligament (SLIL) injuries to observe the effect on different metrics. MethodsA personalized model simulated the full SSM motion cycle from ulnar deviation with extension to radial deviation with flexion informed by the participants motion obtained from dynamic computed tomography. Models repeated the SSM under different levels of SLIL injury and reported changes in joint kinematics, contact mechanics, and ligament forces. ResultsThe fully injured model increased scaphoid dorsal translation, flexion, and radial deviation compared to the intact condition and caused a subluxation of the scaphoid. Radioscaphoid contact areas were approximately 200% greater in the fully injured model compared with all others and the fully injured model was the only condition where contact force decreased across the motion cycle. Ligament forces in the intact condition were on average 33.0 N and 54.2 N for the volar and dorsal SLIL, respectively. Lastly, the long radiolunate, an extrinsic stabilizer, had forces that increased following SLIL injury. ConclusionsComputational models can successfully recreate clinically observed behaviors of an SSM, including scaphoid subluxation, while providing new insights via quantification of contact mechanics and ligament forces. Contact mechanics metrics may be important for understanding the long-term progression of untreated SLIL injuries to osteoarthritis. Additionally, ligament force metrics may explain the progression of SLIL injuries from volar SLIL to dorsal SLIL and highlight the importance of repairing extrinsic stabilizers of the joint, due to increased force sharing following SLIL injury. This work provides a pathway to future studies investigating the effects of SLIL injury and repair, both acutely and chronically.

The fibrous microstructure of tendons and ligaments is an important determinant of their mechanical behavior and integrity. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the inference of microstructural features within fibrous tissues and has recently been used to characterize the microstructure of dense connective tissues such as tendon and ligament. However, the effect of microstructural variations in tendon and ligament on DTI metrics remains unclear. To address this gap, we simulated diffusion MRI of second harmonic generation (SHG) image-informed square lattice fiber networks to determine which microstructural features have the strongest influence on DTI metrics. Then, we performed a second set of diffusion MRI simulations for randomly dispersed fibers within synthetic tendon volumes to relate DTI metrics to the influential microstructural features, including fiber dispersion. All DTI metrics were insensitive to collagen fiber crimp. Fiber dispersion did not affect mean diffusivity, decreased axial diffusivity, increased radial diffusivity, and decreased fractional anisotropy. These results provide valuable insight into the relationships between DTI metrics and microstructural properties of tendon and ligament, which is particularly relevant for inferring microstructural changes in impaired tissue using DTI. Furthermore, our findings are an important step in the translation of DTI for clinical and computational studies of dense connective tissues such as tendon and ligament.

ObjectiveTo investigate the effects of different gait patterns on knee joint biomechanics and dynamic stability during stair ascent. MethodsFourteen healthy males were recruited to ascend stairs using two distinct gait patterns: the "single-step" (leading with the same leg) and "cross-step" (alternating legs) strategies. Kinematic and kinetic data were collected synchronously using a Qualisys infrared motion capture system and a Kistler 3D force plate. Dynamic stability was quantified using the Margin of Stability (MOS), and knee joint biomechanics were evaluated using Patellofemoral Joint Stress (PFJS) and other relevant metrics. ResultsThroughout the gait cycle, there was no significant difference in the Medio-Lateral (ML) MOS between the single-step and cross-step patterns (P=0.318). However, in the Anterior-Posterior (AP) direction, the MOS for both patterns remained negative and decreased over time, with the cross-step pattern exhibiting significantly lower AP MOS values than the single-step pattern (P=0.002). At the moment of left foot-off, significant differences were observed in the right knee joint angle, right knee joint moment, net joint moment, effective quadriceps muscle lever arm, Quadriceps Force (QF), the angle between the quadriceps tendon and patellar ligament, Patellofemoral Joint Force (PFJF), patellofemoral joint stress, and patellofemoral contact area (all P<0.001). ConclusionsDuring stair ascent, the cross-step pattern reduces body stability, thereby increasing the risk of backward falls. Furthermore, this pattern increases patellofemoral joint stress, subjecting the knee to greater loading. Therefore, it is recommended to enhance lower limb muscle strength through targeted training to reduce fall risk. Additionally, adopting a more cautious gait strategy (such as the single-step pattern) can help minimize patellofemoral joint loading and mitigate the risk of patellofemoral pain.

PurposeTo evaluate the hemodynamic impact of restoring a normal sino-tubular junction (STJ) following a novel Hegar dilator-based procedure in patients undergoing root-sparing ascending thoracic aortic aneurysm (ATAA) repair using computational modeling. MethodsWe retrospectively selected an ATAA patient who underwent pre- and postoperative gated computed tomography angiography (CTA). We developed a novel workflow to segment the lumen, thick-walled aorta, and aortic valve from CTA images for subsequent blood flow analysis using computational fluid dynamics (CFD) and fluid-structure interaction (FSI). Morphological and hemodynamic characteristics of the root were quantified and compared against those of a control subject, with no noted ascending aortic dilation. The models sensitivity to graft properties and leaflet material heterogeneity was analyzed. ResultsBoth CFD and FSI results showed that the postoperative geometry reconstructed with a normal STJ profile reintroduces sinus vortices during peak systole, similar to the control subject, but were absent pre-surgery. Accounting for aortic valve leaflets in FSI studies yielded qualitatively similar results to the CFD cases, albeit with locally elevated velocities, time-averaged wall shear stress (TAWSS), and energy dissipation, likely due to the dynamically changing orifice area and differing profiles of the left ventricular outflow tract (LVOT). ConclusionWe demonstrated that the novel Hegar dilator-based STJ reconstruction restores normal blood flow patterns, highlighting the importance of reprofiling the aortic sinuses and STJ. The study also highlights the models sensitivities, particularly the LVOT shape and leaflet morphology and mobility, and may assist planning STJ reconstruction to yield optimal hemodynamics before intervention.

The present study presents a systematic approach for generating data-driven synthetic cerebral aneurysm geometries and evaluating their hemodynamics through computational fluid dynamics. Seven patient-specific aneurysm geometries from the right internal carotid artery were reconstructed from time-of-flight magnetic resonance angiography images and standardized through orientation alignment, followed by non-rigid registration onto a common spherical point cloud as a template. Principal component analysis (PCA) was then applied to the aligned point-cloud data to quantify morphological variability and parameterize shape deformation. The first four principal components captured over 90% of the total variance; however, higher-order components were required to capture the detailed geometrical features of the original geometries. Computational fluid dynamic simulations were performed on the PCA-based synthetic geometries under pulsatile flow conditions to investigate the influence of shape variations on intra-aneurysmal flow patterns, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). The first principal component score (PCS1), which was associated with changes in aneurysm height and dome width, had the strongest effects on TAWSS and OSI levels. Lower PCS1 values, which corresponded to taller and more oblique domes, produced slower adjacent flow and elevated OSI, whereas higher PCS1 values increased TAWSS. The second principal component score primarily modulated lateral geometric asymmetry and further influenced OSI distribution for the lower PCS1 values. Collectively, these findings indicate that PCA-based shape parameterization provides a practical approach for generating synthetic aneurysm datasets and systematically assessing how specific morphological features govern hemodynamic behavior. The proposed approach is expected to contribute to the future development of surrogate modeling and data-driven hemodynamic prediction.

BACKGROUNDThe cardiac Purkinje network plays a critical role in maintaining synchronized activation of ventricles but remains challenging to image due to its fine and unique structure. Conventional MRI techniques lack sufficient contrast to distinguish the underlying structural composition of Purkinje Fibers (PF). PURPOSEThis study investigates the potential of inhomogeneous Magnetization Transfer (ihMT) as a novel contrast mechanism for visualizing and differentiating subregions of the PF. METHODSFive fixed ex-vivo sheep hearts (n = 5) containing free running PF were scanned with a 9.4T MRI using a 2D ihMT RARE sequence. ASSESSMENTihMTR maps were analyzed using manually defined regions-of-interest (ROIs) corresponding to free-running PF, insertion points, and myocardium. Histological analysis (light and polarized microscopy) was performed on matched sections to quantify collagen types I and III, adipocytes, Purkinje cells, and cardiomyocytes. RESULTSThree ihMT protocols, which produced high ihMTR values in free-running PF (9.25-10.83%) and strong absolute contrast relative to the myocardium (2.00-2.17%) and insertion points (2.99-3.40%) in one sample were selected and applied to all samples. Across all samples, mean ihMTR in free-running was consistently higher than in insertion points (11.5 {+/-} 1.5% vs. 9.0 {+/-} 2.9%). Histological analysis revealed a significantly greater collagen content in free-running regions compared with insertion points (72.4 {+/-} 15.9% vs. 31.1 {+/-} 13.1%; p = 0.001), along with higher adipocyte content at insertion points vs. free-running regions (12.3 {+/-} 6.1% vs. 3.8 {+/-} 2.7%, non-significant). Collagen type III was more prominent at insertion points but remained a minor component overall. CONCLUSIONihMT imaging can distinguish PF subregions based on microstructural differences, particularly collagen and adipocyte distribution. This study lays the groundwork for developing biophysical models to interpret ihMT signals in terms of tissue composition and microstructure, providing a foundation for future studies. SponsorThis study received financial support from the French Government by the National Research Agency (ANR; SYNATRA ANR-21-CE19-0014-01) and Region Nouvelle Aquitaine (convention N{degrees}AAPR2022-2021-16609210).

Vertical ground reaction force (vGRF) profiles during walking typically exhibit a double-peaked structure with a mid-stance trough, yet the mechanical conditions governing this morphology remain incompletely defined. In this study, we examined how the balance between push-off and collision impulses during the step-to-step transition influences the temporal and structural characteristics of the vGRF trajectory. Empirical relationships describing push-off and collision work were used to compute transition impulses across walking speeds ranging from 0.8 to 1.4 m{middle dot}s{square}1. A normalized Impulse Balance Index (IBI) was defined to quantify the relative dominance of push-off and collision impulses. The temporal position of the mid-stance trough was quantified using a Trough Deficit Index (TDI) derived from quadratic fits of the vGRF trajectory. Across walking speeds, push-off and collision variations produced step-to-step active work performance imbalance. Push-off and collision became approximately balanced near 1.2 m{middle dot}s{square}1, corresponding to the mechanically preferred walking speed. Deviations from this balanced condition were associated with systematic shifts in trough timing: the trough occurred 1.83% and 1.56% earlier in stance at 0.8 and 1.0 m{middle dot}s{square}1, respectively, and 1.31% later at 1.4 m{middle dot}s{square}1 relative to the reference speed. TDI exhibited a strong inverse relationship with impulse balance (IBI), indicating that vGRF morphology is tightly coupled to the mechanical balance of the step transition. A simplified pendular model further demonstrated that active torque, representing work, during single support shifts the quadratic vertex of the force trajectory by approximately 48.6-51.1% of stance, consistent with the observed trough timing variations. These results show that vertical GRF morphology reflects the imbalance between push-off and collision provides a simple signal of step-to-step transition mechanics, that may be used for rehabilitation and exoskeleton modulation.

Computational growth and remodeling (G&R) models have been extentively used to investigate abdominal aortic aneurysm (AAA) progression and to support clinical decision-making. However, the development of robust predictive models is often limited by the scarcity of large-scale longitudinal imaging datasets. In this study, we propose a physics-based G&R framework to simulate AAA shape evolution and generate a virtual cohort of aneurysms, thereby addressing data limitations and enabling integration with data-driven machine learning approaches for growth prediction. The proposed arterial G&R model incorporates key mechanisms influencing aneurysm progression, including elastin degradation and stress-mediated collagen production. A modified elastin degradation formulation was introduced to generate realistic aneurysm geometries exhibiting clinically relevant features such as asymmetry and tortuosity. By systematically varying parameters governing elastin damage and collagen production, 200 distinct G&R simulations were performed to produce a diverse set of AAA geometries. The dataset was further expanded using kriging-based spatial interpolation to construct a large in silico cohort. The synthetic dataset, combined with longitudinal imaging data from 25 patients, was used to train and validate four machine learning models: Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). A two-step training strategy was adopted to predict maximum aneurysm diameter and growth rate based on prior geometric characteristics. The LSTM model achieved the highest performance for maximum diameter prediction (R{superscript 2} = 0.92), while the RNN demonstrated strong overall performance (R{superscript 2} = 0.90 for maximum diameter and 0.89 for growth rate). The DBN and GRU models also showed competitive predictive capability. Overall, this study demonstrates that integrating physics-based G&R simulations with machine learning enables accurate prediction of AAA growth and maximum diameter. The proposed framework provides a scalable strategy for augmenting limited clinical datasets and offers a promising tool to support personalized risk assessment and treatment planning.

BackgroundArthroscopic release for elbow stiffness is considered a minimally invasive and effective treatment. However, the extent to which each intraoperative step contributes to improvement in range of motion (ROM) has not been well investigated. PurposeTo sequentially evaluate the relationship between intraoperative surgical steps and changes in elbow ROM during arthroscopic release for severe elbow stiffness, and to identify the key procedural stage contributing most significantly to ROM improvement. MethodsFive elbows in five patients with severe elbow stiffness following fracture or dislocation were retrospectively reviewed. Arthroscopic release was performed using a stepwise posterior-based approach, starting from the posterior soft-spot portal, followed by exposure of the olecranon fossa and progression into the posteromedial compartment. Changes in elbow ROM were assessed at each intraoperative step, and ROM at final follow-up was also evaluated. ResultsAll patients demonstrated improvement in elbow ROM at final follow-up. Intraoperative ROM improvement did not occur in a continuous manner but rather in a stepwise fashion. Gradual improvement was observed with establishment of the posterior and posteromedial working spaces, followed by the most substantial increase in ROM immediately after release of the soft tissue attached to the posterior aspect of the humeral medial epicondyle. Although the maximum ROM achieved intraoperatively was not fully maintained at final follow-up, no patient experienced deterioration to preoperative ROM levels. ConclusionsIn arthroscopic release for severe elbow stiffness, improvement in elbow ROM occurs in a stepwise rather than continuous pattern. Release of the posteromedial structures attached to the posterior aspect of the humeral medial epicondyle may represent a critical turning point contributing significantly to ROM improvement.

AA amyloidosis is a severe complication of chronic inflammatory diseases characterized by fibrillar protein deposition in the kidneys, leading to progressive organ failure. This study presents a mathematical model coupling SAA-HDL binding dynamics with renal amyloid aggregation kinetics to elucidate disease pathogenesis. Under normal conditions, Serum Amyloid A (SAA) circulates bound to high-density lipoprotein (HDL), which acts as a molecular chaperone preventing misfolding. However, during chronic inflammation, SAA production exceeds HDL binding capacity, resulting in free SAA that undergoes renal filtration. The model calculates free SAA concentration from reversible binding equilibrium and incorporates renal filtration, mesangial accumulation, and conversion to amyloid fibrils through primary nucleation and autocatalytic growth mechanisms. A central contribution of this work is quantifying accumulated nephrotoxicity arising from AA oligomers, which inflict cumulative cytotoxic damage to mesangial and tubular cells over time. Because oligomers are continuously generated during ongoing aggregation, their toxic burden integrates across the entire duration of the disease. Combined nephrotoxicity, encompassing both oligomer-mediated cellular injury and fibril-driven mechanical disruption of renal architecture, therefore reflects not merely the current disease state but the full inflammatory trajectory of the patient. This cumulative damage defines renal biological age, a measure of functional deterioration whose portion attributable to accumulated nephrotoxicity is irreversible. Renal biological age is also path-dependent: two patients with identical present-day SAA levels may carry different renal damage burdens depending on the duration, timing, and severity of their prior inflammatory episodes. Sensitivity analysis reveals that HDL concentration and SAA cleavage rate are critical determinants of amyloid burden.

Alzheimers disease (AD) is characterized by the accumulation of amyloid-{beta} (A{beta}), yet the specific link between plaque burden and cognitive decline remains a subject of intense investigation. This paper presents a mathematical model that simulates the coupled dynamics of A{beta} monomers, soluble oligomers, and fibrillar species in the brain tissue. By modifying existing moment equations to include a dedicated conservation equation for A{beta} monomers, the model explores how various microscopic processes, such as primary nucleation, surface-catalyzed secondary nucleation, fibril elongation, and fragmentation, contribute to macroscopic disease progression. Central to this study is the concept of "accumulated neurotoxicity" as a surrogate marker of biological age, defined as the time-integrated concentration of soluble A{beta} oligomers. Unlike plaque burden, accumulated neurotoxicity cannot be reversed, and the harm it causes depends critically on the sequence of events that produced it. Numerical results demonstrate that while plaque burden and neurotoxicity both increase over time, their relationship is non-linear and highly sensitive to the efficiency of protein degradation machinery. Specifically, impaired degradation leads to a rapid advancement of biological age relative to calendar age. The model further identifies oligomer dissociation and fibril fragmentation as potential protective mechanisms that can counterintuitively reduce neurotoxic burden by diverting monomers away from the soluble oligomer pool. These findings provide a quantitative framework for understanding why individuals with similar plaque burdens may experience vastly different cognitive outcomes, underscoring the importance of targeting soluble oligomers early in therapeutic interventions.

Thoracic Endovascular Aortic Repair (TEVAR) is a minimally invasive procedure for the treatment of thoracic aortic pathologies, such as Thoracic Aortic Aneurysm (TAA). Computational simulations can provide valuable insights into TEVAR outcomes and complications prior to surgery, making them a useful tool in the procedural planning. In this work, Fluid-Structure Interaction (FSI) computational simulations are carried out in ten pre-TEVAR patient-specific TAA cases, for which post-TEVAR outcomes are known, to quantify the hemodynamic drag forces acting on the aortic wall. Based on these results, this study proposes a new risk factor R to predict the occurrence of type I and III endoleaks. The patient cohort is divided in a calibration set, used to associate specific R values with three different risk levels, and a validation set, to test the risk factor efficacy. Based on the risk factor values obtained for the calibration set, R[&le;] 0.33 is associated with low risk of endoleak formation, 0.33 < R[&le;] 0.67 with moderate risk, and R > 0.67 with high risk. Once it is applied to the validation set,the risk factor is able to predict the formation of a type Ia endoleak. The risk factor proposed in this work is capable of identifying all the endoleak cases analysed, as well as conditions known to increase the risk of TEVAR complications. This study represents a preliminary attempt to determine whether pre-TEVAR hemodynamics can effectively predict post-TEVAR complications and thereby aid clinicians in the pre-operative planning.

Cartilage deterioration is a hallmark of the most common joint disease, osteoarthritis, and there is substantial interest in developing strategies for cartilage synthesis and repair. Cyclical mechanical stimulation has been known for decades to drive synthesis of cartilage matrix proteins. Matrix synthesis requires activation of central metabolism for producing precursors to non-essential amino acids required for protein translation. However, there are gaps in knowledge regarding how mechanical stimuli affect chondrocyte central metabolism. Here, we find that cyclical shear and compression drive differences in chondrocyte central metabolism in a sex-dependent manner. Based on established biochemistry, we developed and validated a stoichiometric model containing 139 metabolites and 172 reactions from central metabolism that includes production of the key cartilage matrix proteins of types II and VI collagen and aggrecan. We then used experimental data from shear and compressive stimulation of osteoarthritis chondrocytes to constrain this model and ran multiple simulations examining flux through the production reactions of matrix proteins and ATP. Our results show that both shear and compression can stimulate osteoarthritic chondrocyte metabolism in a manner consistent with production of cartilage matrix proteins, with notable differences in simulated central metabolism between male and female chondrocytes. Additionally, and importantly, our simulation results suggest that nitrogen availability is a key limitation to chondrocyte synthesis of matrix proteins. These results are a starting point for using central metabolism of chondrocytes to optimize synthesis of matrix proteins for cartilage repair. For example, increasing glutamine levels in the presence of cyclical compression has potential to increase production of both types II and VI collagen. These strategies have potential for improving cartilage tissue engineering and repair.

This study investigates closed kinematic chain biomechanics in cycling with a focus on knee joint loading. Data from 16 cyclists collected on a standardized ergometer were analyzed in OpenSim using inverse dynamics, static optimization, and joint reaction analysis. To keep the pipeline consistent across all subjects, the report summarizes right-knee outputs over a steady-state interval between 120 and 124 s. Peak knee joint moments ranged from 15.79 to 44.85 Nm (mean 30.49 {+/-} 7.66 Nm), while peak resultant knee reaction forces ranged from 1187.61 to 3309.04 N (mean 2317.19 {+/-} 728.19 N). Static optimization showed strong contributions from the rectus femoris and vastus lateralis during power production, with additional stabilization from the biceps femoris long head and gastrocnemius medialis. Mean peak muscle activation was highest for the rectus femoris (0.72 {+/-} 0.19), followed by the biceps femoris long head (0.66 {+/-} 0.20). Mean peak muscle force was highest for the vastus lateralis (1078.1 {+/-} 305.8 N) and rectus femoris (994.1 {+/-} 379.2 N). The results confirm substantial inter-subject variability in knee loading and support the use of personalized training or rehabilitation strategies when cycling is used for performance development or joint recovery.

Selecting suitable tribo-pairs is crucial for measuring the tribological properties of the blinking process, especially for dry eye disease research. The tribo-pairs, lubricants, loads, and sliding speeds used in the friction models reported so far vary greatly, which limits the development of artificial tear fomulations, that could be effective in treating the effects of dry eye disease. This study compares tribo-pairs under the same experimental conditions and provides a test model closer to the real physiological blinking environment. This study proposes a to use the porcine eyeball-eyelid tribo-pair as an ex vitro tissue friction model to explore the tribological behavior during blinking. Additionally, the presence of mucin on the eyelids and cornea was detected. The tribo-pair was compared with the eyeball-glass and eyeball-mucin coated glass tribo-pairs in terms of friction coefficient, relief time, and wear. Artificial tribo-pairs such as contact lens-glass or contact lens-mucin coated glass were not included because of their irrelevance to dry eye disease. The results showed that the static friction coefficient of the eyelid/eyeball tribo-pairs was significantly lower than that of the bare glass/eyeball group. In addition, its dynamic friction coefficient was higher than that of the glass/eyeball tribo-pairs, but the friction damage caused was lower than that of the glass/eyeball group. The relief period (RP) of the eyelid/eyeball tribo-pair was significantly higher than that of bare glass and mucin-coated glass, showing stronger hydrophilicity within this system. To conduct relevant dry eye disease (DED) research, it is critical to simulate the natural eyelid-eyeball friction system as realistically as possible. Despite its limitations, the use of the porcine eye as an in vitro model provides a structurally and biomechanically realistic platform to capture the key interactions between the eyelid and the ocular surface. This approach allows for a more accurate assessment of friction, tear film dynamics, and therapeutic interventions in dry eye.

Background and ObjectivesLung cancer is one of the most frequently diagnosed cancers worldwide. While non-surgical treatment options have increased in number and efficacy, lung resection for primary cancers is still a mainstay of treatment. Lung resection has been shown to impair right ventricular function, although the mechanism for the impairment remains unclear. Wave intensity is increasingly used as a metric for increased post-operative afterload. Here, we develop a computational framework to assess the impact of simulated lung resection on wave intensity to establish that post-operative changes in wave intensity are attributable to the change in pulmonary artery morphometry. MethodsWe analyse a 48 pulmonary arterial surfaces segmented from CT images in patients with no evidence of lung disease to obtain 1D representations of the pulmonary vasculature. For each pulmonary vasculature we sequentially remove vessel branches to mimic post-operative morphometric changes to the arterial network. Using an established 1D computational flow model, we simulate pulsate blood flow in 44 pre-operative cases and 1596 post-operative cases. We compute wave intensity in the main, right, and left pulmonary arteries for all simulations. ResultsWe compare the change in computed wave intensities pre-versus post-operatively to the results of an experimental clinical study comparing pre- and post-operative wave intensity in a 27 patient cohort. We see good agreement between the changes in the parameters of wave intensity between this study and those reported in the clinical study. Further, we capture flow distribution the changes pre-versus post-operatively which indicates that the computational model behaves as expected. ConclusionsIn this preliminary study on a computational framework to capture changes in pulmonary arterial haemodynamics following lung resection, we have shown that our model and analysis pipeline is capable of capturing post-operative changes to wave intensity and flow redistribution between the pulmonary arteries following lung resection. These results motivate further research to develop and validate a patient specific model which is an area of active research for us.