Elastocapillary effects determine early matrix deformation by glioblastoma cell spheroids.

During cancer pathogenesis, cell-generated mechanical stresses lead to dramatic alterations in the mechanical and organizational properties of the extracellular matrix (ECM). To date, contraction of the ECM is largely attributed to local mechanical stresses generated during cell invasion, but the impact of "elastocapillary" effects from surface tension on the tumor periphery has not been examined. Here, we embed glioblastoma cell spheroids within collagen gels, as a model of tumors within the ECM. We then modulate the surface tension of the spheroids, such that the spheroid contracts or expands. Surprisingly, in both cases, at the far-field, the ECM is contracted toward the spheroids prior to cellular migration from the spheroid into the ECM. Through computational simulation, we demonstrate that contraction of the ECM arises from a balance of spheroid surface tension, cell-ECM interactions, and time-dependent, poroelastic effects of the gel. This leads to the accumulation of ECM near the periphery of the spheroid and the contraction of the ECM without regard to the expansion or contraction of the spheroid. These results highlight the role of tissue-level surface stresses and fluid flow within the ECM in the regulation of cell-ECM interactions.

Enhancing high-fidelity nonlinear solver with reduced order model.

We propose the use of reduced order modeling (ROM) to reduce the computational cost and improve the convergence rate of nonlinear solvers of full order models (FOM) for solving partial differential equations. In this study, a novel ROM-assisted approach is developed to improve the computational efficiency of FOM nonlinear solvers by using ROM's prediction as an initial guess. We hypothesize that the nonlinear solver will take fewer steps to the converged solutions with an initial guess that is closer to the real solutions. To evaluate our approach, four physical problems with varying degrees of nonlinearity in flow and mechanics have been tested: Richards' equation of water flow in heterogeneous porous media, a contact problem in a hyperelastic material, two-phase flow in layered porous media, and fracture propagation in a homogeneous material. Overall, our approach maintains the FOM's accuracy while speeding up nonlinear solver by 18-73% (through suitable ROM-assisted FOMs). More importantly, the proximity of ROM's prediction to the solution space leads to the improved convergence of FOMs that would have otherwise diverged with default initial guesses. We demonstrate that the ROM's accuracy can impact the computational efficiency with more accurate ROM solutions, resulting in a better cost reduction. We also illustrate that this approach could be used in many FOM discretizations (e.g., finite volume, finite element, or a combination of those). Since our ROMs are data-driven and non-intrusive, the proposed procedure can easily lend itself to any nonlinear physics-based problem.

An adhesive and resilient hydrogel for the sealing and treatment of gastric perforation.

Adhesive hydrogels have been recently proposed as a potential option to seal and treat gastric perforation (GP) which causes high mortality despite advancements in surgical treatments. However, to be effective, the hydrogels must have sufficient tissue adhesiveness, tough mechanical property, tunable biodegradability and ideally are easy to apply and form. Herein, we report an adhesive and resilient hydrogel for the sealing and treatment of gastric perforation. The hydrogel consists of a bioactive, transglutaminase (TG)-crosslinked gelatin network and a dynamic, borate-crosslinked poly-N-[Tris(hydroxymethyl)methyl]acrylamide (PTH) network. The hydrogel can be formed in situ, facilitating easy delivery to the GP and allowing for precise sealing of the defects. In vivo experiments, using a perforated stomach mouse model, shows that the adhesive hydrogel plug effectively seals GP defects and promotes gastric mucosa regeneration. Overall, this hydrogel represents a promising biomaterial for GP treatment.

A model for 3D deformation and reconstruction of contractile microtissues.

Tissue morphogenesis and regeneration are essentially mechanical processes that involve coordination of cellular forces, production and structural remodeling of extracellular matrix (ECM), and cell migration. Discovering the principles of cell-ECM interactions and tissue-scale deformation in mechanically-loaded tissues is instrumental to the development of novel regenerative therapies. The combination of high-throughput three-dimensional (3D) culture systems and experimentally-validated computational models accelerate the study of these principles. In our previous work [E. Mailand, et al., Biophys. J., 2019, 117, 975-986], we showed that prominent surface stresses emerge in constrained fibroblast-populated collagen gels, driving the morphogenesis of fibrous microtissues. Here, we introduce an active material model that allows the embodiment of surface and bulk contractile stresses while maintaining the passive elasticity of the ECM in a 3D setting. Unlike existing models, the stresses are driven by mechanosensing and not by an externally applied signal. The mechanosensing component is incorporated in the model through a direct coupling of the local deformation state with the associated contractile force generation. Further, we propose a finite element implementation to account for large deformations, nonlinear active material response, and surface effects. Simulation results quantitatively capture complex shape changes during tissue formation and as a response to surgical disruption of tissue boundaries, allowing precise calibration of the parameters of the 3D model. The results of this study imply that the organization of the extracellular matrix in the bulk of the tissue may not be a major factor behind the morphogenesis of fibrous tissues at sub-millimeter length scales.

An Algorithm for Automated Separation of Trabecular Bone From Variably Thick Cortices in High-Resolution Computed Tomography Data.

OBJECTIVE: Structural measurements after separation of cortical from trabecular bone are of interest to a wide variety of communities but are difficult to obtain because of the lack of accurate automated techniques. METHODS: We present a structure-based algorithm for separating cortical from trabecular bone in binarized images. Using the thickness of the cortex as a seed value, bone connected to the cortex within a spatially local threshold value is identified and separated from the remaining bone. The algorithm was tested on seven biological data sets from four species imaged using micro-computed tomography (µ-CT) and high-resolution peripheral quantitative computed tomography (HR-pQCT). Area and local thickness measurements were compared to images segmented manually. RESULTS: The algorithm was approximately 11 times faster than manual measurements and the median error in cortical area was -4.47 ± 4.15%. The median error in cortical thickness was approximately 0.5 voxels for µ-CT data and less than 0.05 voxels for HR-pQCT images resulting in an overall difference of -28.1 ± 71.1 µm. CONCLUSION: A simple and readily implementable methodology has been developed that is repeatable, efficient, and requires few user inputs, providing an unbiased means of separating cortical from trabecular bone. SIGNIFICANCE: Automating the segmentation of variably thick cortices will allow for the evaluation of large data sets in a time-efficient manner and allow for full-field analyses that have been previously limited to small regions of interest. The MATLAB code can be downloaded from https://github.com/TBL-UIUC/downloads.git.

Photodynamic activity of plant extracts from Sarawak, Borneo.

Photodynamic therapy (PDT) is a medical treatment that involves the irradiation of an administered photosensitizing drug with light of a particular wavelength to activate the photosensitizer to kill abnormal cells. To date, only a small number of photosensitizers have been clinically approved for PDT, and researchers continue to look for new molecules that have more desirable properties for clinical applications. Natural products have long been important sources of pharmaceuticals, and there is a great potential for discovery of novel chemotypes from under-explored biodiversities in the world. The objective of this study is to mine the terrestrial plants in Sarawak, Borneo Island, for new photosensitizers for PDT. In a screening program from 2004 to 2008, we prepared and studied 2,400 extracts from 888 plants for their photosensitizing activities. This report details the bioprospecting process, preparation and testing of extracts, analysis of the active samples, fractionation of four samples, and isolation and characterization of photosensitizers.