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  1. Free, publicly-accessible full text available October 1, 2024
  2. Free, publicly-accessible full text available September 17, 2024
  3. Abstract

    Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.

    Significance:

    Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.

     
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  4. Abstract

    Reliable and controllable growth of two-dimensional (2D) hexagonal boron nitride (h-BN) is essential for its wide range of applications. Substrate engineering is one of the critical factors that influence the growth of the epitaxial h-BN films. Here, we report the growth of monolayer h-BN on Ni (111) substrates incorporated with oxygen atoms via molecular beam epitaxy. It was found that the increase of incorporated oxygen concentration in the Ni substrate through a pretreatment process prior to the h-BN growth step would have an adverse effect on the morphology and growth rate of 2D h-BN. Under the same growth condition, h-BN monolayer coverage decreases exponentially as the amount of oxygen incorporated into Ni (111) increases. Density functional theory calculations and climbing image nudged elastic band (CI-NEB) method reveal that the substitutional oxygen atoms can increase the diffusion energy barrier of B and N atoms on Ni (111) thereby inhibiting the growth of h-BN films. As-grown large-area h-BN monolayer films and fabricated Al/h-BN/Ni (MIM) nanodevices were comprehensively characterized to evaluate the structural, optical and electrical properties of high-quality monolayers. Direct tunneling mechanism and high breakdown strength of ∼11.2 MV cm−1are demonstrated for the h-BN monolayers grown on oxygen-incorporated Ni (111) substrates, indicating that these films have high quality. This study provides a unique example that heterogeneous catalysis principles can be applied to the epitaxy of 2D crystals in solid state field. Similar strategies can be used to grow other 2D crystalline materials, and are expected to facilitate the development of next generation devices based on 2D crystals.

     
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    Free, publicly-accessible full text available October 5, 2024
  5. We present the method of direct van der Waals simulation (DVS) to study computationally flows with liquid-vapor phase transformations. Our approach is based on a discretization of the Navier-Stokes-Korteweg equations, which couple flow dynamics with van der Waals’ nonequilibrium thermodynamic theory of phase transformations, and opens an opportunity for first-principles simulation of a wide range of boiling and cavitating flows. The proposed algorithm enables unprecedented simulations of the Navier-Stokes-Korteweg equations involving cavitating flows at strongly under-critical conditions and 𝒪(105) Reynolds number. The proposed technique provides a pathway for a fundamental understanding of phase-transforming flows with multiple applications in science, engineering, and medicine.

     
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  6. While theoretical estimates suggest that cavitation of water should occur when pressure falls much below −25 MPa at room temperature, in experiments, we commonly observe conversion to vapor at pressures of the order of 3 kPa. The commonly accepted explanation for this discrepancy is that water usually contains nanometer-sized cavitation nuclei. When the pressure decreases, these nuclei expand and become visible to the naked eye. However, the origin of these cavitation nuclei is not well understood. An earlier work in this field has mainly focused on the inception of nuclei which are purely composed of water vapor, whereas experimental data suggest that these nuclei are mainly composed of air. In this Letter, we develop a theoretical approach to study the inception of cavitation nuclei in water with uniformly dissolved air, using a diffuse interface approach. We derive equations which govern the transition of water with uniformly dissolved air to a critical state. Our results show that the dissolved air decreases the free energy barrier from the initial to the critical state, thereby aiding the formation of cavitation nuclei. This study opens up possibilities to explore cavitation inception in fluids containing dissolved gases.

     
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  7. Abstract

    We study the collapse and expansion of a cavitation bubble in a deformable porous medium. We develop a continuum-scale model that couples compressible fluid flow in the pore network with the elastic response of a solid skeleton. Under the assumption of spherical symmetry, our model can be reduced to an ordinary differential equation that extends the Rayleigh–Plesset equation to bubbles in soft porous media. The extended Rayleigh–Plesset equation reveals that finite-size effects lead to the breakdown of the universal scaling relation between bubble radius and time that holds in the infinite-size limit. Our data indicate that the deformability of the porous medium slows down the collapse and expansion processes, a result with important consequences for wide-ranging phenomena, from drug delivery to spore dispersion.

     
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  8. Abstract

    The molecular signaling pathways that orchestrate angiogenesis have been widely studied, but the role of biophysical cues has received less attention. Interstitial flow is unavoidable in vivo, and has been shown to dramatically change the neovascular patterns, but the mechanisms by which flow regulates angiogenesis remain poorly understood. Here, we study the complex interactions between interstitial flow and the affinity for matrix binding of different chemokine isoforms. Using a computational model, we find that changing the matrix affinity of the chemokine isoform can invert the effect of interstitial flow on angiogenesis—from preferential growth in the direction of the flow when the chemokine is initially matrix-bound to preferential flow against the flow when it is unbound. Although fluid forces signal endothelial cells directly, our data suggests a mechanism for the inversion based on biotransport arguments only, and offers a potential explanation for experimental results in which interstitial flow produced preferential vessel growth with and against the flow. Our results point to a particularly intricate effect of interstitial flow on angiogenesis in the tumor microenvironment, where the vessel network geometry and the interstitial flow patterns are complex.

     
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