Numerical investigation of new rete ridge formation in a multi-layer model of skin subjected to tissue expansion.

The skin is a multilayered organ with microstructural and antomical heterogeneities that contribute to its unique mechanophysiology. Between the epidermis layer at the top and the dermis layer below, the basal keratinocytes form an interface with sinusoidal-like geometry termed rete ridges. In previous computational work we showed that the rete ridges contribute to lower delamination risk by increasing surface area and reducing the stress jump across the interface.

Tissue expansion mitigates radiation-induced skin fibrosis in a porcine model.

Tissue expansion (TE) is the primary method for breast reconstruction after mastectomy. In many cases, mastectomy patients undergo radiation treatment (XR). Radiation is known to induce skin fibrosis and is one of the main causes for complications during post-mastectomy breast reconstruction.

Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery.

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures.

Multiscale mechanical characterization and computational modeling of fibrin gels.

Fibrin is a naturally occurring protein network that forms a temporary structure to enable remodeling during wound healing. It is also a common tissue engineering scaffold because the structural properties can be controlled. However, to fully characterize the wound healing process and improve the design of regenerative scaffolds, understanding fibrin mechanics at multiple scales is necessary.

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.

Mechanobiological wound model for improved design and evaluation of collagen dermal replacement scaffolds.

Skin wounds are among the most common and costly medical problems experienced. Despite the myriad of treatment options, such wounds continue to lead to displeasing cosmetic outcomes and also carry a high burden of loss-of-function, scarring, contraction, or nonhealing. As a result, the need exists for new therapeutic options that rapidly and reliably restore skin cosmesis and function.

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.

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.