Self-Assembly of Organic Semiconductors on Strained Graphene under Strain-Induced Pseudo-Electric Fields.

Graphene is used as a growth template for van der Waals epitaxy of organic semiconductor (OSC) thin films. During the synthesis and transfer of chemical-vapor-deposited graphene on a target substrate, local inhomogeneities in the graphene-in particular, a nonuniform strain field in the graphene template-can easily form, causing poor morphology and crystallinity of the OSC thin films. Moreover, a strain field in graphene introduces a pseudo-electric field in the graphene. Here, the study investigates how the strain and strain-induced pseudo-electric field of a graphene template affect the self-assembly of π-conjugated organic molecules on it. Periodically strained graphene templates are fabricated by transferring graphene onto an array of nanospheres and then analyzed the growth and nucleation behavior of C60 thin films on the strained graphene templates. Both experiments and a numerical simulation demonstrated that strained graphene reduced the desorption energy between the graphene and the C60 molecules and thereby suppressed both nucleation and growth of the C60. A mechanism is proposed in which the strain-induced pseudo-electric field in graphene modulates the binding energy of organic molecules on the graphene.

Exploring uncertainty in hyper-viscoelastic properties of scalp skin through patient-specific finite element models for reconstructive surgery.

Understanding skin responses to external forces is crucial for post-cutaneous flap wound healing. However, the in vivo viscoelastic behavior of scalp skin remains poorly understood. Personalized virtual surgery simulations offer a way to study tissue responses in relevant 3D geometries. Yet, anticipating wound risk remains challenging due to limited data on skin viscoelasticity, which hinders our ability to determine the interplay between wound size and stress levels. To bridge this gap, we reexamine three clinical cases involving scalp reconstruction using patient-specific geometric models and employ uncertainty quantification through a Monte Carlo simulation approach to study the effect of skin viscoelasticity on the final stress levels from reconstructive surgery. Utilizing the generalized Maxwell model via the Prony series, we can parameterize and efficiently sample a realistic range of viscoelastic response and thus shed light on the influence of viscoelastic material uncertainty in surgical scenarios. Our analysis identifies regions at risk of wound complications based on reported threshold stress values from the literature and highlights the significance of focusing on long-term responses rather than short-term ones.

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis.

Background: Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices. Objective: This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors. Methods: People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ2 or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors. Results: A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007). Conclusions: This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

Multi-Fidelity Gaussian Process Surrogate Modeling of Pediatric Tissue Expansion.

Growth of skin in response to stretch is the basis for tissue expansion (TE), a procedure to gain new skin area for reconstruction of large defects. Unfortunately, complications and suboptimal outcomes persist because TE is planned and executed based on physician's experience and trial and error instead of predictive quantitative tools. Recently, we calibrated computational models of TE to a porcine animal model of tissue expansion, showing that skin growth is proportional to stretch with a characteristic time constant. Here, we use our calibrated model to predict skin growth in cases of pediatric reconstruction. Available from the clinical setting are the expander shapes and inflation protocols. We create low fidelity semi-analytical models and finite element models for each of the clinical cases. To account for uncertainty in the response expected from translating the models from the animal experiments to the pediatric population, we create multifidelity Gaussian process surrogates to propagate uncertainty in the mechanical properties and the biological response. Predictions with uncertainty for the clinical setting are essential to bridge our knowledge from the large animal experiments to guide and improve the treatment of pediatric patients. Future calibration of the model with patient-specific data-such as estimation of mechanical properties and area growth in the operating room-will change the standard for planning and execution of TE protocols.

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.

Bayesian calibration of a computational model of tissue expansion based on a porcine animal model.

Tissue expansion is a technique used clinically to grow skin in situ to correct large defects. Despite its enormous potential, lack of fundamental knowledge of skin adaptation to mechanical cues, and lack of predictive computational models limit the broader adoption and efficacy of tissue expansion. In our previous work, we introduced a finite element model of tissue expansion that predicted key patterns of strain and growth which were then confirmed by our porcine animal model. Here we use the data from a new set of experiments to calibrate the computational model within a Bayesian framework. Four 10×10cm2 patches were tattooed in the dorsal skin of four 12 weeks-old minipigs and a total of six patches underwent successful tissue expander placement and inflation to 60cc for expansion times ranging from 1 h to 7 days. Six patches that did not have expanders implanted served as controls for the analysis. We find that growth can be explained based on the elastic deformation. The predicted area growth rate is k∈[0.02,0.08] [h-1]. Growth is anisotropic and reflects the anisotropic mechanical behavior of porcine dorsal skin. The rostral-caudal axis shows greater deformation than the transverse axis, and the time scale of growth in the rostral-caudal direction is given by rate parameters k1∈[0.04,0.1] [h-1] compared to k2∈[0.01,0.05] [h-1] in the transverse direction. Moreover, the calibration results underscore the high variability in biological systems, and the need to create probabilistic computational models to predict tissue adaptation in realistic settings. STATEMENT OF SIGNIFICANCE: Tissue expansion is a widely used technique in reconstructive surgery because it triggers growth of skin for the correction of large skin lesions and for breast reconstruction after mastectomy. Despite of its potential, complications and undesired outcomes persist due to our incomplete understanding of skin mechanobiology. Here we quantify the deformation and growth fields induced by an expander over 7 days in a porcine animal model and use these data to calibrate a computational model of skin growth using finite element simulations and a Bayesian framework. The calibrated model is a leap forward in our understanding skin growth, we now have quantitative understanding of this process: area growth is anisotropic and it is proportional to stretch with a characteristic rate constant of k∈[0.02,0.08] [h-1].

High-Throughput Magnetic Actuation Platform for Evaluating the Effect of Mechanical Force on 3D Tumor Microenvironment.

Accurately replicating and analyzing cellular responses to mechanical cues is vital for exploring metastatic disease progression. However, many of the existing in vitro platforms for applying mechanical stimulation seed cells on synthetic substrates. To better recapitulate physiological conditions, a novel actuating platform is developed with the ability to apply tensile strain on cells at various amplitudes and frequencies in a high-throughput multi-well culture plate using a physiologically-relevant substrate. Suspending fibrillar fibronectin across the body of the magnetic actuator provides a matrix representative of early metastasis for 3D cell culture that is not reliant on a synthetic substrate. This platform enables the culturing and analysis of various cell types in an environment that mimics the dynamic stretching of lung tissue during normal respiration. Metabolic activity, YAP activation, and morphology of breast cancer cells are analyzed within one week of cyclic stretching or static culture. Further, matrix degradation is significantly reduced in breast cancer cell lines with metastatic potential after actuation. These new findings demonstrate a clear suppressive cellular response due to cyclic stretching that has implications for a mechanical role in the dormancy and reactivation of disseminated breast cancer cells to macrometastases.

The Geometry of Incompatibility in Growing Soft Tissues: Theory and Numerical Characterization.

Tissues in vivo are not stress-free. As we grow, our tissues adapt to different physiological and disease conditions through growth and remodeling. This adaptation occurs at the microscopic scale, where cells control the microstructure of their immediate extracellular environment to achieve homeostasis. The local and heterogeneous nature of this process is the source of residual stresses. At the macroscopic scale, growth and remodeling can be accurately captured with the finite volume growth framework within continuum mechanics, which is akin to plasticity. The multiplicative split of the deformation gradient into growth and elastic contributions brings about the notion of incompatibility as a plausible description for the origin of residual stress. Here we define the geometric features that characterize incompatibility in biological materials. We introduce the geometric incompatibility tensor for different growth types, showing that the constraints associated with growth lead to specific patterns of the incompatibility metrics. To numerically investigate the distribution of incompatibility measures, we implement the analysis within a finite element framework. Simple, illustrative examples are shown first to explain the main concepts. Then, numerical characterization of incompatibility and residual stress is performed on three biomedical applications: brain atrophy, skin expansion, and cortical folding. Our analysis provides new insights into the role of growth in the development of tissue defects and residual stresses. Thus, we anticipate that our work will further motivate additional research to characterize residual stresses in living tissue and their role in development, disease, and clinical intervention.

Improving reconstructive surgery design using Gaussian process surrogates to capture material behavior uncertainty.

To produce functional, aesthetically natural results, reconstructive surgeries must be planned to minimize stress as excessive loads near wounds have been shown to produce pathological scarring and other complications (Gurtner et al., 2011). Presently, stress cannot easily be measured in the operating room. Consequently, surgeons rely on intuition and experience (Paul et al., 2016; Buchanan et al., 2016). Predictive computational tools are ideal candidates for surgery planning. Finite element (FE) simulations have shown promise in predicting stress fields on large skin patches and in complex cases, helping to identify potential regions of complication. Unfortunately, these simulations are computationally expensive and deterministic (Lee et al., 2018a). However, running a few, well selected FE simulations allows us to create Gaussian process (GP) surrogate models of local cutaneous flaps that are computationally efficient and able to predict stress and strain for arbitrary material parameters. Here, we create GP surrogates for the advancement, rotation, and transposition flaps. We then use the predictive capability of these surrogates to perform a global sensitivity analysis, ultimately showing that fiber direction has the most significant impact on strain field variations. We then perform an optimization to determine the optimal fiber direction for each flap for three different objectives driven by clinical guidelines (Leedy et al., 2005; Rohrer and Bhatia, 2005). While material properties are not controlled by the surgeon and are actually a source of uncertainty, the surgeon can in fact control the orientation of the flap with respect to the skin's relaxed tension lines, which are associated with the underlying fiber orientation (Borges, 1984). Therefore, fiber direction is the only material parameter that can be optimized clinically. The optimization task relies on the efficiency of the GP surrogates to calculate the expected cost of different strategies when the uncertainty of other material parameters is included. We propose optimal flap orientations for the three cost functions and that can help in reducing stress resulting from the surgery and ultimately reduce complications associated with excessive mechanical loading near wounds.

Personalized Computational Models of Tissue-Rearrangement in the Scalp Predict the Mechanical Stress Signature of Rotation Flaps.

OBJECTIVE: To elucidate the mechanics of scalp rotation flaps through 3D imaging and computational modeling. Excessive tension near a wound or sutured region can delay wound healing or trigger complications. Measuring tension in the operating room is challenging, instead, noninvasive methods to improve surgical planning are needed. DESIGN: Multi-view stereo allows creation of 3D patient-specific geometries based on a set of photographs. The patient-specific 3D geometry is imported into a finite element (FE) platform to perform a virtual procedure. The simulation is compared with the clinical outcome. Additional simulations quantify the effect of individual flap parameters on the resulting tension distribution. PARTICIPANTS: Rotation flaps for reconstruction of scalp defects following melanoma resection in 2 cases are presented. Rotation flaps were designed without preoperative FE preparation. MAIN OUTCOME MEASURE: Tension distribution over the operated region. RESULTS: The tension from FE shows peaks at the base and distal ends of the scalp rotation flap. The predicted geometry from the simulation aligns with postoperative photographs. Simulations exploring the flap design parameters show variation in the tension. Lower tensions were achieved when rotation was oriented with respect to skin tension lines (horizontal tissue fibers) and smaller rotation angles. CONCLUSIONS: Tension distribution following rotation of scalp flaps can be predicted through personalized FE simulations. Flaps can be designed to reduce tension using FE, which may greatly improve the reliability of scalp reconstruction in craniofacial surgery, critical in complex cases when scalp reconstruction is essential for coverage of hardware, implants, and/or bone graft.

Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression.

A key feature of living tissues is their capacity to remodel and grow in response to environmental cues. Within continuum mechanics, this process can be captured with the multiplicative split of the deformation gradient into growth and elastic contributions. The mechanical and biological response during tissue adaptation is characterized by inherent variability. Accounting for this uncertainty is critical to better understand tissue mechanobiology, and, moreover, it is of practical importance if we aim to develop predictive models for clinical use. However, the current gold standard in computational models of growth and remodeling remains the use of deterministic finite element (FE) simulations. Here we focus on tissue expansion, a popular technique in which skin is stretched by a balloon-like device inducing its growth. We construct FE models of tissue expansion with various levels of detail, and show that a sufficiently broad set of FE simulations from these models can be used to train an accurate and efficient multi-fidelity Gaussian process (GP) surrogate. The approach is not limited to simulation data, rather, it can fuse different kinds of data, including from experiments. The main appeal of the framework relies on the common experience that highly detailed models (or experiments) are more accurate but also more costly, while simpler models (or experiments) can be easily evaluated but are bound to have some error. In these situations, doing uncertainty analysis tasks with the high fidelity models alone is not feasible and, conversely, relying solely on low fidelity approximations is also undesirable. We show that a multi-fidelity GP outperforms the high fidelity GP and low fidelity GP when tested against the most detailed FE model. In turn, having trained the multi-fidelity GP model, we showcase the propagation of uncertainty from the mechanical and biological response parameters to the spatio-temporal growth outcomes. We expect that the methods and applications in this paper will enable future research in parameter calibration under uncertainty and uncertainty propagation in real clinical scenarios involving tissue growth and remodeling.

Modeling Tissue Expansion with Isogeometric Analysis: Skin Growth and Tissue Level Changes in the Porcine Model.

BACKGROUND: Tissue expansion relies on the ability of skin to grow in response to sustained mechanical strain. This study focuses on correlation of cellular and histologic changes with skin growth and deformation during tissue expansion. METHODS: Tissue expanders were placed underneath the skin of five Yucatan minipigs and inflated with one fill of 60 cc of saline 1 hour, 24 hours, 3 days, and 7 days before the animals were killed, or two fills of either 30 cc or 60 cc at 10 and 3 days or 14 and 7 days before the animals were killed. Skin biopsy specimens and three-dimensional photographs were used to calculate skin growth and stretch according to the authors' novel finite element analysis model. RESULTS: The mitotic index of keratinocytes in the basal layer increased 1 hour after stimulus was applied (4 percent) (p = 0.022), peaked at approximately day 3 (26 percent) (p < 0.0001), and tapered by day 7 (12.5 percent) (p = 0.012) after tissue expansion. The authors demonstrated that it is the volume per fill rather than the total volume in the expander that scales the magnitude of response. Lastly, the authors demonstrated that the ratio of deformation attributable to growth versus stretch (Fgrowth/Fstretch) after 60 cc of tissue expansion fill was 1.03 at 1 hour, 0.82 at 1 day, 0.85 at day 3, and 0.95 at 7 days. CONCLUSIONS: Peak cell proliferation occurred 3 days after tissue expansion fill and is scaled in response to stimulus magnitude. The growth component of deformation equilibrates to the stretch component at day 7, as cell proliferation has started to translate to skin growth.

Propagation of material behavior uncertainty in a nonlinear finite element model of reconstructive surgery.

Excessive mechanical stress following surgery can lead to delayed healing, hypertrophic scars, and even skin necrosis. Measuring stress directly in the operating room over large skin areas is not feasible, and nonlinear finite element simulations have become an appealing alternative to predict stress contours on arbitrary geometries. However, this approach has been limited to generic cases, when in reality each patient geometry and procedure are unique, and material properties change from one person to another. In this manuscript, we use multi-view stereo to capture the patient-specific geometry of a 7-year-old female undergoing cranioplasty and complex tissue rearrangement. The geometry is used to setup a nonlinear finite element simulation of the reconstructive procedure. A key contribution of this work is incorporation of material behavior uncertainty. The finite element simulation is computationally expensive, and it is not suitable for uncertainty propagation which would require many such simulations. Instead, we run only a few expensive simulations in order to build a surrogate model by Gaussian process regression of the principal components of the stress fields computed with these few samples. The inexpensive surrogate is then used to compute the statistics of the stress distribution in this patient-specific scenario.

Multi-view stereo in the operating room allows prediction of healing complications in a patient-specific model of reconstructive surgery.

Excessive mechanical stress leads to wound healing complications following reconstructive surgery. However, this knowledge is not easily applicable in clinical scenarios due to the difficulty in measuring stress contours during complex tissue rearrangement procedures. Computational tools have been proposed as an alternative to address this need, but obtaining patient specific geometries with an affordable and flexible setup has remained a challenge. Here we present a methodology to determine the stress contours from a reconstructive procedure on a patient-specific finite element model based on multi-view stereo (MVS). MVS is a noninvasive technology that allows reconstruction of 3D geometries using a standard digital camera, making it ideal for the operating room. Finite element analysis can then be used on the patient-specific geometry to perform a virtual surgery and predict regions at risk of complications. We applied our approach to the case of a 7-year-old patient who was treated to correct a cranial contour deformity and resect two large areas of scalp scarring. The simulation showed several zones of high stress, particularly near the suture lines at the distal ends of the flaps. The patient did show delayed healing and partial flap tip necrosis at one of such predicted regions at the 30-day follow up visit. Our results further establish the application of computational tools in individualized medical scenarios to advance preoperative planing and anticipate regions of concern immediately after surgery.

Improving tissue expansion protocols through computational modeling.

Tissue expansion is a common technique in reconstructive surgery used to grow skin in vivo for correction of large defects. Despite its popularity, there is a lack of quantitative understanding of how stretch leads to growth of new skin. This has resulted in several arbitrary expansion protocols that rely on the surgeon's personal training and experience rather than on accurate predictive models. For example, choosing between slow or rapid expansion, or small or large inflation volumes remains controversial. Here we explore four tissue expansion protocols by systematically varying the inflation volume and the protocol duration in a porcine model. The quantitative analysis combines three-dimensional photography, isogeometric kinematics, and finite growth theory. Strikingly, all four protocols generate similar peak stretches, but different growth patterns: Smaller filling volumes of 30 ml per inflation did not result in notable expander-induced growth neither for the short nor for the long protocol; larger filling volumes of 60 ml per inflation trigger skin adaptation, with larger expander-induced growth in regions of larger stretch, and more expander-induced growth for the 14-day compared to the 10-day expansion protocol. Our results suggest that expander-induced growth is not triggered by the local stretch alone. While stretch is clearly a driver for growth, the local stretch at a given point is not enough to predict the expander-induced growth at that location. From a clinical perspective, our study suggests that longer expansion protocols are needed to ensure sufficient growth of sizable skin patches.

Efficacy of methotrexate therapy in patients with tubal pregnancy and a serum human chorionic gonadotropin level above 10,000 IU/L.

OBJECTIVE: To estimate the failure rate of medical treatment and to identify variables associated with treatment failure in patients with tubal pregnancy and an initial serum level of human chorionic gonadotropin (HCG) over 10,000 IU/L. METHODS: The inclusion criteria were tubal pregnancy diagnosed using ultrasonography, primary treatment of intramuscular methotrexate injection at one of the four institutions between January 2003 and December 2011, a serum HCG level within two days before treatment>10,000 IU/L, and follow-up data to determine treatment success or failure. Exclusion criteria were other primary treatments besides intramuscular methotrexate injection. The clinicopathologic data of 36 patients were collected and analyzed. RESULTS: Medical treatment failed and surgery was performed in 19 (53%) patients. In univariable analysis, age, parity, and size of the gestational sac were associated with treatment failure, but none of the variables were associated with treatment failure in multivariable analysis. The failure rate in the subgroup with age<33 years and size of gestational sac≥1.1 cm was significantly higher than those of the other subgroups (82% vs. 41% [mean of the other subgroups], respectively). CONCLUSION: Patients with a serum HCG level>10,000 IU/L who received medical treatment had a high failure rate. Among them, patients aged<33 years and with a gestational sac≥1.1 cm had an extremely high failure rate.