Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues.

Data-based approaches are promising alternatives to the traditional analytical constitutive models for solid mechanics. Herein, we propose a Gaussian process (GP) based constitutive modeling framework, specifically focusing on planar, hyperelastic and incompressible soft tissues. The strain energy density of soft tissues is modeled as a GP, which can be regressed to experimental stress-strain data obtained from biaxial experiments. Moreover, the GP model can be weakly constrained to be convex. A key advantage of a GP-based model is that, in addition to the mean value, it provides a probability density (i.e. associated uncertainty) for the strain energy density. To simulate the effect of this uncertainty, a non-intrusive stochastic finite element analysis (SFEA) framework is proposed. The proposed framework is verified against an artificial dataset based on the Gasser-Ogden-Holzapfel model and applied to a real experimental dataset of a porcine aortic valve leaflet tissue. Results show that the proposed framework can be trained with limited experimental data and fits the data better than several existing models. The SFEA framework provides a straightforward way of using the experimental data and quantifying the resulting uncertainty in simulation-based predictions.

MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics.

Gradient-based meta-learning methods have primarily been applied to classical machine learning tasks such as image classification. Recently, PDE-solving deep learning methods, such as neural operators, are starting to make an important impact on learning and predicting the response of a complex physical system directly from observational data. Taking the material modeling problems for example, the neural operator approach learns a surrogate mapping from the loading field to the corresponding material response field, which can be seen as learning the solution operator of a hidden PDE. The microstructure and mechanical parameters of each material specimen correspond to the (possibly heterogeneous) parameter field in this hidden PDE. Due to the limitation on experimental measurement techniques, the data acquisition for each material specimen is commonly challenging and costly. This fact calls for the utilization and transfer of existing knowledge to new and unseen material specimens, which corresponds to sampling efficient learning of the solution operator of a hidden PDE with a different parameter field. Herein, we propose a novel meta-learning approach for neural operators, which can be seen as transferring the knowledge of solution operators between governing (unknown) PDEs with varying parameter fields. Our approach is a provably universal solution operator for multiple PDE solving tasks, with a key theoretical observation that underlying parameter fields can be captured in the first layer of neural operator models, in contrast to typical final-layer transfer in existing meta-learning methods. As applications, we demonstrate the efficacy of our proposed approach on PDE-based datasets and a real-world material modeling problem, illustrating that our method can handle complex and nonlinear physical response learning tasks while greatly improving the sampling efficiency in unseen tasks.

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements.

We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From in-distribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on out-of-distribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the small-deformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the data-driven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.

Determination of a Strain Energy Density Function for the Tricuspid Valve Leaflets Using Constant Invariant-Based Mechanical Characterizations.

The tricuspid valve (TV) regulates the blood flow within the right side of the heart. Despite recent improvements in understanding TV mechanical and microstructural properties, limited attention has been devoted to the development of TV-specific constitutive models. The objective of this work is to use the first-of-its-kind experimental data from constant invariant-based mechanical characterizations to determine a suitable invariant-based strain energy density function (SEDF). Six specimens for each TV leaflet are characterized using constant invariant mechanical testing. The data is then fit with three candidate SEDF forms: (i) a polynomial model-the transversely isotropic version of the Mooney-Rivlin model, (ii) an exponential model, and (iii) a combined polynomial-exponential model. Similar fitting capabilities were found for the exponential and the polynomial forms (R2=0.92-0.99 versus 0.91-0.97) compared to the combined polynomial-exponential SEDF (R2=0.65-0.95). Furthermore, the polynomial form had larger Pearson's correlation coefficients than the exponential form (0.51 versus 0.30), indicating a more well-defined search space. Finally, the exponential and the combined polynomial-exponential forms had notably smaller but more eccentric model parameter's confidence regions than the polynomial form. Further evaluations of invariant decoupling revealed that the decoupling of the invariant terms within the exponential form leads to a less satisfactory performance. From these results, we conclude that the exponential form is better suited for the TV leaflets owing to its superb fitting capabilities and smaller parameter's confidence regions.

Parameterization, geometric modeling, and isogeometric analysis of tricuspid valves.

Approximately 1.6 million patients in the United States are affected by tricuspid valve regurgitation, which occurs when the tricuspid valve does not close properly to prevent backward blood flow into the right atrium. Despite its critical role in proper cardiac function, the tricuspid valve has received limited research attention compared to the mitral and aortic valves on the left side of the heart. As a result, proper valvular function and the pathologies that may cause dysfunction remain poorly understood. To promote further investigations of the biomechanical behavior and response of the tricuspid valve, this work establishes a parameter-based approach that provides a template for tricuspid valve modeling and simulation. The proposed tricuspid valve parameterization presents a comprehensive description of the leaflets and the complex chordae tendineae for capturing the typical three-cusp structural deformation observed from medical data. This simulation framework develops a practical procedure for modeling tricuspid valves and offers a robust, flexible approach to analyze the performance and effectiveness of various valve configurations using isogeometric analysis. The proposed methods also establish a baseline to examine the tricuspid valve's structural deformation, perform future investigations of native valve configurations under healthy and disease conditions, and optimize prosthetic valve designs.

On the simulation of mitral valve function in health, disease, and treatment.

The mitral valve (MV) is the heart valve that regulates blood ?ow between the left atrium and left ventricle (LV). In situations where the MV fails to fully cover the left atrioventricular ori?ce during systole, the resulting regurgitation causes pulmonary congestion, leading to heart failure and/or stroke. The causes of MV insuf?ciency can be either primary (e.g. myxomatous degeneration) where the valvular tissue is organically diseased, or secondary (typically inducded by ischemic cardiomyopathy) termed ischemic mitral regurgitation (IMR), is brought on by adverse LV remodeling. IMR is present in up to 40% of patients and more than doubles the probability of cardiovascular morbidity after 3.5 years. There is now agreement that adjunctive procedures are required to treat IMR caused by lea?et tethering. However, there is no consensus regarding the best procedure. Multicenter registries and randomized trials would be necessary to prove which procedure is superior. Given the number of proposed procedures and the complexity and duration of such studies, it is highly unlikely that IMR procedure optimization will be achieved by prospective clinical trials. There is thus an urgent need for cell and tissue physiologically based quantitative assessments of MV function to better design surgical solutions and associated therapies. Novel computational approaches directed towards optimized surgical repair procedures can substantially reduce the need for such trial-and-error approaches. We present the details of our MV modeling techniques, with an emphasis on what is known and investigated at various length scales.

A contact formulation based on a volumetric potential: Application to isogeometric simulations of atrioventricular valves.

This work formulates frictionless contact between solid bodies in terms of a repulsive potential energy term and illustrates how numerical integration of the resulting forces is computationally similar to the "pinball algorithm" proposed and studied by Belytschko and collaborators in the 1990s. We thereby arrive at a numerical approach that has both the theoretical advantages of a potential-based formulation and the algorithmic simplicity, computational efficiency, and geometrical versatility of pinball contact. The singular nature of the contact potential requires a specialized nonlinear solver and an adaptive time stepping scheme to ensure reliable convergence of implicit dynamic calculations. We illustrate the effectiveness of this numerical method by simulating several benchmark problems and the structural mechanics of the right atrioventricular (tricuspid) heart valve. Atrioventricular valve closure involves contact between every combination of shell surfaces, edges of shells, and cables, but our formulation handles all contact scenarios in a unified manner. We take advantage of this versatility to demonstrate the effects of chordal rupture on tricuspid valve coaptation behavior.

On the presence of affine fibril and fiber kinematics in the mitral valve anterior leaflet.

In this study, we evaluated the hypothesis that the constituent fibers follow an affine deformation kinematic model for planar collagenous tissues. Results from two experimental datasets were utilized, taken at two scales (nanometer and micrometer), using mitral valve anterior leaflet (MVAL) tissues as the representative tissue. We simulated MVAL collagen fiber network as an ensemble of undulated fibers under a generalized two-dimensional deformation state, by representing the collagen fibrils based on a planar sinusoidally shaped geometric model. The proposed approach accounted for collagen fibril amplitude, crimp period, and rotation with applied macroscopic tissue-level deformation. When compared to the small angle x-ray scattering measurements, the model fit the data well, with an r(2) = 0.976. This important finding suggests that, at the homogenized tissue-level scale of ∼1 mm, the collagen fiber network in the MVAL deforms according to an affine kinematics model. Moreover, with respect to understanding its function, affine kinematics suggests that the constituent fibers are largely noninteracting and deform in accordance with the bulk tissue. It also suggests that the collagen fibrils are tightly bounded and deform as a single fiber-level unit. This greatly simplifies the modeling efforts at the tissue and organ levels, because affine kinematics allows a straightforward connection between the macroscopic and local fiber strains. It also suggests that the collagen and elastin fiber networks act independently of each other, with the collagen and elastin forming long fiber networks that allow for free rotations. Such freedom of rotation can greatly facilitate the observed high degree of mechanical anisotropy in the MVAL and other heart valves, which is essential to heart valve function. These apparently novel findings support modeling efforts directed toward improving our fundamental understanding of tissue biomechanics in healthy and diseased conditions.

Quantification and simulation of layer-specific mitral valve interstitial cells deformation under physiological loading.

Within each of the four layers of mitral valve (MV) leaflet tissues there resides a heterogeneous population of interstitial cells that maintain the structural integrity of the MV tissue via protein biosynthesis and enzymatic degradation. There is increasing evidence that tissue stress-induced MV interstitial cell (MVIC) deformations can have deleterious effects on their biosynthetic states that are potentially related to the reduction of tissue-level maintenance and to subsequent organ-level failure. To better understand the interrelationships between tissue-level loading and cellular responses, we developed the following integrated experimental-computational approach. Since in vivo cellular deformations are not directly measurable, we quantified the in-situ layer-specific MVIC deformations for each of the four layers under a controlled biaxial tension loading device coupled to multi-photon microscopy. Next, we explored the interrelationship between the MVIC stiffness and deformation to layer-specific tissue mechanical and structural properties using a macro-micro finite element computational model. Experimental results indicated that the MVICs in the fibrosa and ventricularis layers deformed significantly more than those in the atrialis and spongiosa layers, reaching a nucleus aspect ratio of 3.3 under an estimated maximum physiological tension of 150N/m. The simulated MVIC moduli for the four layers were found to be all within a narrow range of 4.71-5.35kPa, suggesting that MVIC deformation is primarily controlled by each tissue layer's respective structure and mechanical behavior rather than the intrinsic MVIC stiffness. This novel result further suggests that while the MVICs may be phenotypically and biomechanically similar throughout the leaflet, they experience layer-specific mechanical stimulatory inputs due to distinct extracellular matrix architecture and mechanical behaviors of the four MV leaflet tissue layers. This also suggests that MVICs may behave in a layer-specific manner in response to mechanical stimuli in both normal and surgically modified MVs.

A generalized method for the analysis of planar biaxial mechanical data using tethered testing configurations.

Simulation of the mechanical behavior of soft tissues is critical for many physiological and medical device applications. Accurate mechanical test data is crucial for both obtaining the form and robust parameter determination of the constitutive model. For incompressible soft tissues that are either membranes or thin sections, planar biaxial mechanical testing configurations can provide much information about the anisotropic stress-strain behavior. However, the analysis of soft biological tissue planar biaxial mechanical test data can be complicated by in-plane shear, tissue heterogeneities, and inelastic changes in specimen geometry that commonly occur during testing. These inelastic effects, without appropriate corrections, alter the stress-traction mapping and violates equilibrium so that the stress tensor is incorrectly determined. To overcome these problems, we presented an analytical method to determine the Cauchy stress tensor from the experimentally derived tractions for tethered testing configurations. We accounted for the measured testing geometry and compensate for run-time inelastic effects by enforcing equilibrium using small rigid body rotations. To evaluate the effectiveness of our method, we simulated complete planar biaxial test configurations that incorporated actual device mechanisms, specimen geometry, and heterogeneous tissue fibrous structure using a finite element (FE) model. We determined that our method corrected the errors in the equilibrium of momentum and correctly estimated the Cauchy stress tensor. We also noted that since stress is applied primarily over a subregion bounded by the tethers, an adjustment to the effective specimen dimensions is required to correct the magnitude of the stresses. Simulations of various tether placements demonstrated that typical tether placements used in the current experimental setups will produce accurate stress tensor estimates. Overall, our method provides an improved and relatively straightforward method of calculating the resulting stresses for planar biaxial experiments for tethered configurations, which is especially useful for specimens that undergo large shear and exhibit substantial inelastic effects.

Bayesian Optimization-Based Inverse Finite Element Analysis for Atrioventricular Heart Valves.

Inverse finite element analysis (iFEA) of the atrioventricular heart valves (AHVs) can provide insights into the in-vivo valvular function, such as in-vivo tissue strains; however, there are several limitations in the current state-of-the-art that iFEA has not been widely employed to predict the in-vivo, patient-specific AHV leaflet mechanical responses. In this exploratory study, we propose the use of Bayesian optimization (BO) to study the AHV functional behaviors in-vivo. We analyzed the efficacy of Bayesian optimization to estimate the isotropic Lee-Sacks material coefficients in three benchmark problems: (i) an inflation test, (ii) a simplified leaflet contact model, and (iii) an idealized AHV model. Then, we applied the developed BO-iFEA framework to predict the leaflet properties for a patient-specific tricuspid valve under a congenital heart defect condition. We found that the BO could accurately construct the objective function surface compared to the one from a [Formula: see text] grid search analysis. Additionally, in all cases the proposed BO-iFEA framework yielded material parameter predictions with average element errors less than 0.02 mm/mm (normalized by the simulation-specific characteristic length). Nonetheless, the solutions were not unique due to the presence of a long-valley minima region in the objective function surfaces. Parameter sets along this valley can yield functionally equivalent outcomes (i.e., closing behavior) and are typically observed in the inverse analysis or parameter estimation for the nonlinear mechanical responses of the AHV. In this study, our key contributions include: (i) a first-of-its-kind demonstration of the BO method used for the AHV iFEA; and (ii) the evaluation of a candidate AHV in-silico modeling approach wherein the chordae could be substituted with equivalent displacement boundary conditions, rendering the better iFEA convergence and a smoother objective surface.

Systematic Review and Meta-Analysis of Endovascular Therapy Effectiveness for Unruptured Saccular Intracranial Aneurysms.

Background: Currently, endovascular treatment of intracranial aneurysms (ICAs) is limited by low complete occlusion rates. The advent of novel endovascular technology has expanded the applicability of endovascular therapy; however, the superiority of novel embolic devices over the traditional Guglielmi detachable coils (GDCs) is still debated. We performed a systematic review of literature that reported Raymond-Roy occlusion classification (RROC) rates of modern endovascular devices to determine their immediate and follow-up occlusion effectiveness for the treatment of unruptured saccular ICAs. Methods: A search was conducted using electronic databases (PUBMED, Cochrane, ClinicalTrials.gov, Web of Science). We retrieved studies published between 2000-2022 reporting immediate and follow-up RROC rates of subjects treated with different endovascular ICA therapies. We extracted demographic information of the treated patients and their reported angiographic RROC rates. Results: A total of 80 studies from 15 countries were included for data extraction. RROC rates determined from angiogram were obtained for 21,331 patients (72.5% females, pooled mean age: 58.2 (95% CI: 56.8-59.6), harboring 22,791 aneurysms. The most frequent aneurysm locations were the internal carotid artery (46.4%, 95% CI: 41.9%-50.9%), the anterior communicating artery (26.4%, 95% CI: 22.5%-30.8%), the middle cerebral artery (24.5%, 95% CI:19.2%-30.8%) and the basilar tip (14.4%, 95% CI:11.3%-18.3%). The complete occlusion probability (RROC-I) was analyzed for GDCs, the Woven EndoBridge (WEB), and flow diverters. The RROC-I rate was the highest in balloon-assisted coiling (73.9%, 95% CI: 65.0%-81.2%) and the lowest in the WEB (27.8%, 95% CI:13.2%-49.2%). The follow-up RROC-I probability was homogenous in all analyzed devices. Conclusions: We observed that the coil-based endovascular therapy provides acceptable rates of complete occlusion, and these rates are improved in balloon-assisted coils. Out of the analyzed devices, the WEB exhibited the shortest time to achieve >90% probability of follow-up complete occlusion (~18 months). Overall, the GDCs remain the gold standard for endovascular treatment of unruptured saccular aneurysms.

A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves.

A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. Forty-six samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May-Newman model was the most probable model for the porcine aortic valve data. When the samples were further grouped into different cusp types, the May-Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee-Sacks model and the May-Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode's amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to other tissue types, and has the potential to provide a structured and rational way of making simulations population-based.

Modulation of Smooth Muscle Cell Phenotype for Translation of Tissue-Engineered Vascular Grafts.

Translation of small-diameter tissue-engineered vascular grafts (TEVGs) for the treatment of coronary artery disease (CAD) remains an unfulfilled promise. This is largely due to the limited integration of TEVGs into the native vascular wall-a process hampered by the insufficient smooth muscle cell (SMC) infiltration and extracellular matrix deposition, and low vasoactivity. These processes can be promoted through the judicious modulation of the SMC toward a synthetic phenotype to promote remodeling and vascular integration; however, the expression of synthetic markers is often accompanied by a decrease in the expression of contractile proteins. Therefore, techniques that can precisely modulate the SMC phenotypical behavior could have the potential to advance the translation of TEVGs. In this review, we describe the phenotypic diversity of SMCs and the different environmental cues that allow the modulation of SMC gene expression. Furthermore, we describe the emerging biomaterial approaches to modulate the SMC phenotype in TEVG design and discuss the limitations of current techniques. In addition, we found that current studies in tissue engineering limit the analysis of the SMC phenotype to a few markers, which are often the characteristic of early differentiation only. This limited scope has reduced the potential of tissue engineering to modulate the SMC toward specific behaviors and applications. Therefore, we recommend using the techniques presented in this review, in addition to modern single-cell proteomics analysis techniques to comprehensively characterize the phenotypic modulation of SMCs. Expanding the holistic potential of SMC modulation presents a great opportunity to advance the translation of living conduits for CAD therapeutics.

Tricuspid Valve Regurgitation in Hypoplastic Left Heart Syndrome: Current Insights and Future Perspectives.

Hypoplastic Left Heart Syndrome (HLHS) is a congenital heart defect that requires a three-stage surgical palliation to create a single ventricle system in the right side of the heart. Of patients undergoing this cardiac palliation series, 25% will develop tricuspid regurgitation (TR), which is associated with an increased mortality risk. Valvular regurgitation in this population has been extensively studied to understand indicators and mechanisms of comorbidity. In this article, we review the current state of research on TR in HLHS, including identified valvular anomalies and geometric properties as the main reasons for the poor prognosis. After this review, we present some suggestions for future TR-related studies to answer the central question: What are the predictors of TR onset during the three palliation stages? These studies involve (i) the use of engineering-based metrics to evaluate valve leaflet strains and predict tissue material properties, (ii) perform multivariate analyses to identify TR predictors, and (iii) develop predictive models, particularly using longitudinally tracked patient cohorts to foretell patient-specific trajectories. Regarded together, these ongoing and future efforts will result in the development of innovative tools that can aid in surgical timing decisions, in prophylactic surgical valve repair, and in the refinement of current intervention techniques.

Finite Element Modeling of Residual Hearing after Cochlear Implant Surgery in Chinchillas.

Cochlear implant (CI) surgery is one of the most utilized treatments for severe hearing loss. However, the effects of a successful scala tympani insertion on the mechanics of hearing are not yet fully understood. This paper presents a finite element (FE) model of the chinchilla inner ear for studying the interrelationship between the mechanical function and the insertion angle of a CI electrode. This FE model includes a three-chambered cochlea and full vestibular system, accomplished using µ-MRI and µ-CT scanning technologies. This model's first application found minimal loss of residual hearing due to insertion angle after CI surgery, and this indicates that it is a reliable and helpful tool for future applications in CI design, surgical planning, and stimuli setup.

An investigation of how specimen dimensions affect biaxial mechanical characterizations with CellScale BioTester and constitutive modeling of porcine tricuspid valve leaflets.

Biaxial mechanical characterizations are the accepted approach to determine the mechanical response of many biological soft tissues. Although several computational and experimental studies have examined how experimental factors (e.g., clamped vs. suture mounting) affect the acquired tissue mechanical behavior, little is known about the role of specimen dimensions in data acquisition and the subsequent modeling. In this study, we combined our established mechanical characterization framework with an iterative size-reduction protocol to test the hypothesis that specimen dimensions affect the observed mechanical behavior of biaxial characterizations. Our findings indicated that there were non-significant differences in the peak equibiaxial stretches of tricuspid valve leaflets across four specimen dimensions ranging from 4.5×4.5mm to 9 × 9mm. Further analyses revealed that there were significant differences in the low-tensile modulus of the circumferential tissue direction. These differences resulted in significantly different constitutive model parameters for the Tong-Fung model between different specimen dimensions of the posterior and septal leaflets. Overall, our findings demonstrate that specimen dimensions play an important role in experimental characterizations, but not necessarily in constitutive modeling of soft tissue mechanical behavior during biaxial testing with the commercial CellScale BioTester.

The Lasso Suture: Ex Vivo Testing of an Alternative to the High-Tension Deep Dermal Stitch.

SUMMARY: Wound dehiscence, with an estimated occurrence rate greater than 4% in plastic surgery, is generally underreported, and can be an indicator of increased mortality and remission rates. The authors developed the lasso suture as a stronger alternative to the current standard patterns. The lasso suture takes less time to perform than the standard high-tension wound repair method. The authors dissected caprine skin specimens to create full-thickness wounds for suture repair using simple interrupted, vertical mattress, horizontal mattress, and deep dermal with running intradermal (DDR) sutures ( n = 10) and lasso sutures ( n = 9). They then conducted uniaxial failure testing to quantify the suture rupture stresses and strains. They also measured the suture operating time with medical students and residents (PGY or MS programs) performing wound repair (10-cm wide, 2-cm deep, 2-0 polydioxanone sutures) on soft-fixed human cadaver skin. The lasso stitch had a greater first-suture rupture stress compared with all other patterns ( P < 0.001): 2.46 ± 0.27 MPa for lasso versus 0.69 ± 0.14 MPa for simple interrupted, 0.68 ± 0.13 MPa for vertical mattress, 0.50 ± 0.10 MPa for horizontal mattress, and 1.17 ± 0.28 MPa for DDR sutures. Performing the lasso suture was 28% faster than performing standard DDR (264 ± 21 versus 349 ± 25 seconds; P = 0.027). In summary, the authors showed that the lasso has superior mechanical properties compared with the studied traditional sutures, and that the new technique can be performed more quickly than the current standard (DDR stitch) for high-tension wounds. Future animal and in-clinic studies will be helpful to confirm the authors' findings in this proof-of-concept study. CLINICAL RELEVANCE STATEMENT: The authors propose the lasso suture, a new suturing method with improved tensile performance compared with traditional techniques and a faster operative time than the deep dermal stitch typically used for high-tension wounds in reconstructive surgery to prevent wound dehiscence.

Layer Microdissection of Tricuspid Valve Leaflets for Biaxial Mechanical Characterization and Microstructural Quantification.

The tricuspid valve (TV) regulates the unidirectional flow of unoxygenated blood from the right atrium to the right ventricle. The TV consists of three leaflets, each with unique mechanical behaviors. These variations among the three TV leaflets can be further understood by examining their four anatomical layers, which are the atrialis (A), spongiosa (S), fibrosa (F), and ventricularis (V). While these layers are present in all three TV leaflets, there are differences in their thicknesses and microstructural constituents that further influence their respective mechanical behaviors. This protocol includes four steps to elucidate the layer-specific differences: (i) characterize the mechanical and collagen fiber architectural behaviors of the intact TV leaflet, (ii) separate the composite layers (A/S and F/V) of the TV leaflet, (iii) carry out the same characterizations for the composite layers, and (iv) perform post-hoc histology assessment. This experimental framework uniquely allows the direct comparison of the intact TV tissue to each of its composite layers. As a result, detailed information regarding the microstructure and biomechanical function of the TV leaflets can be collected with this protocol. Such information can potentially be used to develop TV computational models that seek to provide guidance for the clinical treatment of TV disease.

The role of elastin on the mechanical properties of the anterior leaflet in porcine tricuspid valves.

Elastin is present in the extracellular matrix (ECM) of connective tissues, and its mechanical properties are well documented. In Marfan syndrome, however, the inability to properly code for the protein fibrillin-1 prematurely leads to the degradation and loss of elastin fiber integrity in the ECM. In this study, the role of elastin in the ECM of the anterior leaflet of the tricuspid valve was investigated by examining the biomechanical behavior of porcine leaflets before and after the application of the enzyme elastase. Five loading protocols were applied to the leaflet specimens in two groups (elastase-treated and control samples). The mechanical response following elastase application yielded a significantly stiffer material in both the radial and circumferential directions. At a physiological level of stress (85 kPa), the elastase group had an average strain of 26.21% and 6.32% in the radial and circumferential directions, respectively, at baseline prior to elastase application. Following elastase treatment, the average strain was 5.28% and 0.97% in the radial and circumferential directions, respectively. No statistically significant change was found in the control group following sham treatment with phosphate-buffered saline (PBS). Two-photon microscopy images confirmed that after the removal of elastin, the collagen fibers displayed a loss of undulation. With a significant reduction in radial compliance, the ability to withstand physiological loads may be compromised. As such, an extracellular matrix that is structurally deficient in elastin may hinder normal tricuspid valve function.