On the Reliability of Suction Measurements for Skin Characterization

Intra- and inter-rater reliabilities of skin mechanical properties measured in healthy individuals using skin elasticity meter

PURPOSE

The aim of this study is to establish a standardized measurement method and to examine the intra- and inter-reliabilities and absolute reliability of measuring skin mechanical properties using a skin elasticity meter (Cutometer®).

METHODS

Ten healthy participated in the study. Skin mechanical properties were measured at four sites: upper arm, lower arm, upper leg and lower leg on both sides in supine position using a non-invasive skin elasticity meter by two trained different raters. The measurements include quantitative indices of the maximal distensibility (R0), elasticity (R2, R5, R7), and viscoelasticity (R6). Intra- and inter- relative reliabilities were determined using the intraclass correlation coefficient (ICC) (1,1) and ICC (2,1) methods, respectively. The absolute reliability was assessed via the Bland-Altman analysis. Moreover, we evaluated the minimal detectable change at a 95% confidence level (MDC95).

RESULTS

At each site, the ICC (1,1) values were >0.90, and the ICC (2,1) values were >0.50. The Bland-Altman analysis did not reveal any fixed errors, and several sites and parameters have proportional errors.

CONCLUSIONS

In this study, intra- and inter-reliabilities were measured at "excellent" and more than "moderate" levels, respectively. However, because some proportional errors were observed, the limits of reliability agreement should be considered when using the proposed methods. We believe that the results of this study can be applied to clinical research in field of rehabilitation treatment.

Mechanical damage in porcine dermis: Micro-mechanical model and experimental characterization

Skin is subjected to extreme mechanical loading during needle insertion and drug delivery to the subcutaneous space. There is a rich literature on the characterization of porcine skin biomechanics as the preeminent animal model for human skin, but the emphasis has been on the elastic response and specific anatomical locations such as the dorsal and the ventral regions. During drug delivery, however, energy dissipation in the form of damage, softening, and fracture, is expected. Similarly, reports on experimental characterization are complemented by modeling efforts, but with similar gaps in microstructure-driven modeling of dissipative mechanisms. Here we contribute to the bridging of these gaps by testing porcine skin from belly and breast regions, in two different orientation with respect to anatomical axes, and to progressively higher stretches in order to show damage accumulation and stiffness degradation. We complement the mechanical test with imaging of the collagen structure and a micro-mechanics modeling framework. We found that skin from the belly is stiffer with respect to the breast region when comparing the calf stiffness of the J-shaped stress-stretch response observed in most collagenous tissues. No significant direction dependent properties were found in either anatomical location. Both locations showed energy dissipation due to damage, evident though a softening of the stress-stretch response. The microstructure model was able to capture the elastic and damage progression with a small set of parameters, some of which were determined directly from imaging. We anticipate that data and model fits can help in predictive simulations for device design in situations where skin is subject to supra-physiological deformation such as in subcutaneous drug delivery.

Identifiability of soft tissue constitutive parameters from in-vivo macro-indentation

Reliable identification of soft tissue material parameters is frequently required in a variety of applications, particularly for biomechanical simulations using finite element analysis (FEA). However, determining representative constitutive laws and material parameters is challenging and often comprises a bottleneck that hinders the successful implementation of FEA. Soft tissues exhibit a nonlinear response and are commonly modeled using hyperelastic constitutive laws. In-vivo material parameter identification, for which standard mechanical tests (e.g., uniaxial tension and compression) are inapplicable, is commonly achieved using finite macro-indentation test. Due to the lack of analytical solutions, the parameters are commonly identified using inverse FEA (iFEA), in which simulated results and experimental data are iteratively compared. However, determining what data must be collected to accurately identify a unique parameter set remains unclear. This work investigates the sensitivities of two types of measurements: indentation force-depth data (e.g., measured using an instrumented indenter) and full-field surface displacements (e.g., using digital image correlation). To eliminate model fidelity and measurement-related errors, we employed an axisymmetric indentation FE model to produce synthetic data for four 2-parameter hyperelastic constitutive laws: compressible Neo-Hookean, and nearly incompressible Mooney-Rivlin, Ogden, and Ogden-Moerman models. For each constitutive law, we computed the objective functions representing the discrepancies in the reaction force, the surface displacement, and their combination, and visualized them for hundreds of parameter sets, spanning a representative range as found in the literature for the bulk soft tissue complex in human lower limbs. Moreover, we quantified three identifiability metrics, which provided insights into the uniqueness (or lack thereof) and the sensitivities. This approach provides a clear and systematic evaluation of the parameter identifiability, which is independent of the selection of the optimization algorithm and initial guesses required in iFEA. Our analysis indicated that the indenter's force-depth data, despite being commonly used for parameter identification, was insufficient for reliably and accurately identifying both parameters for all the investigated material models and that the surface displacement data improved the parameter identifiability in all cases, although the Mooney-Rivlin parameters remained poorly identifiable. Informed by the results, we then discuss several identification strategies for each constitutive model. Finally, we openly provide the codes used in this study, to allow others to further investigate the indentation problem according to their specifications (e.g., by modifying the geometries, dimensions, mesh, material models, boundary conditions, contact parameters, or objective functions).

Control of hydrostatic pressure and osmotic stress in 3D cell culture for mechanobiological studies

Hydrostatic pressure (HP) and osmotic stress (OS) play an important role in various biological processes, such as cell proliferation and differentiation. In contrast to canonical mechanical signals transmitted through the anchoring points of the cells with the extracellular matrix, the physical and molecular mechanisms that transduce HP and OS into cellular functions remain elusive. Three-dimensional cell cultures show great promise to replicate physiologically relevant signals in well-defined host bioreactors with the goal of shedding light on hidden aspects of the mechanobiology of HP and OS. This review starts by introducing prevalent mechanisms for the generation of HP and OS signals in biological tissues that are subject to pathophysiological mechanical loading. We then revisit various mechanisms in the mechanotransduction of HP and OS, and describe the current state of the art in bioreactors and biomaterials for the control of the corresponding physical signals.

Bayesian Inference With Gaussian Process Surrogates to Characterize Anisotropic Mechanical Properties of Skin From Suction Tests

One of the intrinsic features of skin and other biological tissues is the high variation in the mechanical properties across individuals and different demographics. Mechanical characterization of skin is still a challenge because the need for subject-specific in vivo parameters prevents us from utilizing traditional methods, e.g., uniaxial tensile test. Suction devices have been suggested as the best candidate to acquire mechanical properties of skin noninvasively, but capturing anisotropic properties using a circular probe opening-which is the conventional suction device-is not possible. On the other hand, noncircular probe openings can drive different deformations with respect to fiber orientation and therefore could be used to characterize the anisotropic mechanics of skin noninvasively. We propose the use of elliptical probe openings and a methodology to solve the inverse problem of finding mechanical properties from suction measurements. The proposed probe is tested virtually by solving the forward problem of skin deformation by a finite element (FE) model. The forward problem is a function of the material parameters. In order to solve the inverse problem of determining skin properties from suction data, we use a Bayesian framework. The FE model is an expensive forward function, and is thus substituted with a Gaussian process metamodel to enable the Bayesian inference problem.

Case report: Long-term follow-up of a large full-thickness skin defect treated with a photosynthetic scaffold for dermal regeneration

It is broadly described that almost every step of the regeneration process requires proper levels of oxygen supply; however, due to the vascular disruption in wounds, oxygen availability is reduced, being detrimental to the regeneration process. Therefore, the development of novel biomaterials combined with improved clinical procedures to promote wound oxygenation is an active field of research in regenerative medicine. This case report derives from a cohort of patients enrolled in a previously published ongoing phase I clinical trial (NCT03960164), to assess safety of photosynthetic scaffolds for the treatment of full skin defects. Here, we present a 56 year old patient, with a scar contracture in the cubital fossa, which impaired the elbow extension significantly affecting her quality of life. As part of the treatment, the scar contracture was removed, and the full-thickness wound generated was surgically covered with a photosynthetic scaffold for dermal regeneration, which was illuminated to promote local oxygen production. Then, in a second procedure, an autograft was implanted on top of the scaffold and the patient’s progress was followed for up to 17 months. Successful outcome of the whole procedure was measured as improvement in functionality, clinical appearance, and self-perception of the treated area. This case report underscores the long-term safety and applicability of photosynthetic scaffolds for dermal regeneration and their stable compatibility with other surgical procedures such as autograft application. Moreover, this report also shows the ability to further improve the clinical outcome of this procedure by means of dermal vacuum massage therapy and, more importantly, shows an overall long-term improvement in patient´s quality of life, supporting the translation of photosynthetic therapies into human patients.

Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue

Closed-form constitutive models are the standard to describe soft tissue mechanical behavior. However, inherent pitfalls of an explicit functional form include poor fits to the data, non-uniqueness of fit, and sensitivity to parameters. Here we design deep neural networks (DNN) that satisfy desirable physics constraints in order to replace expert models of tissue mechanics. To guarantee stress-objectivity, the DNN takes strain (pseudo)-invariants as inputs, and outputs the strain energy and its derivatives. Polyconvexity of strain energy is enforced through the loss function. Direct prediction of both energy and derivative functions enables the computation of the elasticity tensor needed for a finite element implementation. We showcase the DNN ability to learn the anisotropic mechanical behavior of porcine and murine skin from biaxial test data. A multi-fidelity scheme that combines high fidelity experimental data with a low fidelity analytical approximation yields the best performance. Finite element simulations of tissue expansion with the DNN model illustrate the potential of this method to impact medical device design for skin therapeutics. We expect that the open data and software from this work will broaden the use of data-driven constitutive models of tissue mechanics.

On modeling the multiscale mechanobiology of soft tissues: Challenges and progress

Tissues grow and remodel in response to mechanical cues, extracellular and intracellular signals experienced through various biological events, from the developing embryo to disease and aging. The macroscale response of soft tissues is typically nonlinear, viscoelastic anisotropic, and often emerges from the hierarchical structure of tissues, primarily their biopolymer fiber networks at the microscale. The adaptation to mechanical cues is likewise a multiscale phenomenon. Cell mechanobiology, the ability of cells to transform mechanical inputs into chemical signaling inside the cell, and subsequent regulation of cellular behavior through intra- and inter-cellular signaling networks, is the key coupling at the microscale between the mechanical cues and the mechanical adaptation seen macroscopically. To fully understand mechanics of tissues in growth and remodeling as observed at the tissue level, multiscale models of tissue mechanobiology are essential. In this review, we summarize the state-of-the art modeling tools of soft tissues at both scales, the tissue level response, and the cell scale mechanobiology models. To help the interested reader become more familiar with these modeling frameworks, we also show representative examples. Our aim here is to bring together scientists from different disciplines and enable the future leap in multiscale modeling of tissue mechanobiology.

Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations

Data-driven methods are becoming an essential part of computational mechanics due to their advantages over traditional material modeling. Deep neural networks are able to learn complex material response without the constraints of closed-form models. However, data-driven approaches do not a priori satisfy physics-based mathematical requirements such as polyconvexity, a condition needed for the existence of minimizers for boundary value problems in elasticity. In this study, we use a recent class of neural networks, neural ordinary differential equations (N-ODEs), to develop data-driven material models that automatically satisfy polyconvexity of the strain energy. We take advantage of the properties of ordinary differential equations to create monotonic functions that approximate the derivatives of the strain energy with respect to deformation invariants. The monotonicity of the derivatives guarantees the convexity of the energy. The N-ODE material model is able to capture synthetic data generated from closed-form material models, and it outperforms conventional models when tested against experimental data on skin, a highly nonlinear and anisotropic material. We also showcase the use of the N-ODE material model in finite element simulations of reconstructive surgery. The framework is general and can be used to model a large class of materials, especially biological soft tissues. We therefore expect our methodology to further enable data-driven methods in computational mechanics.

Longitudinal monitoring and prediction of long-term outcome of scar stiffness on pediatric patients

Background

Hypertrophic scarring after burn injury is one of the greatest unmet challenges in patients with burn injuries. A better understanding of the characteristics of scar maturation and early prediction of the long-term outcome of scarring are prerequisites for improving targeted therapies and pivotal for patient counselling.

Methods

Repeated measurements of scar stiffness in 11 pediatric patients were performed over the course of 1 year using 2 suction devices, the Cutometer and the Nimble. In addition, the observer pliability score of the Patient and Observer Scar Assessment Scale was applied. This longitudinal study allowed quantification of the ability of each of the measured parameters to reflect scar maturation, as indicated by change in skin pliability/stiffness, over time (using linear regression); the ability to distinguish individual patients (intraclass correlation coefficient (ICC)); the correlation of the devices (Spearman correlation coefficient); and the ability to predict long-term scar maturation based on early scar assessment (using receiver operating characteristic).

Results

All the tools used showed significant longitudinal decrease of scar stiffness from 3 months until 12 months after the injury. The Nimble (ICC_patient ^Nimble = 0.99) and the Cutometer (ICC_patient ^Cuto = 0.97) demonstrated an excellent ability to distinguish between individual patients. The Nimble seemed to be able to predict the 12-month pliability of scars based on early (3-month) measurements (area under the curve (AUC)_12m ^POSAS = 0.67; AUC_12m ^C = 0.46; AUC_12m ^N = 0.79).

Conclusions

The results of this preliminary study suggest that all 3 tools provide suitable means to quantify alterations in scar stiffness over time. Initial evidence suggests the Nimble is most favorable for predicting changes in stiffness associated with long-term scar maturation. Further studies with a larger sample size are required to validate tissue suction as a clinical tool for analysis of changes of scar stiffness over time.