Recent studies have suggested that platelets have a crucial role in enhancing the survival of circulating tumor cells in the bloodstream and aggravating cancer metastasis. The main function of platelets is to bind to the sites of the damaged vessels to stop bleeding. However, in cancer patients, activated platelets adhere to circulating tumor cells and exacerbate metastatic spreading. Several hypotheses have been proposed about the platelet–cancer cell interactions, but the underlying mechanisms of these interactions are not completely understood yet. In this work, we quantitatively investigated the interactions between circulating tumor cells, red blood cells, platelets, plasma flow and microvessel walls via computational modelling at the cellular scale. Our highly detailed computational model allowed us to understand and quantitatively explain the role of platelets in deformation, adhesion and survival of tumor cells in their active arrest to the endothelium.

Cancer is a major cause of death worldwide and becomes particularly threatening once it begins to metastasize. During metastasis, the blood vessels serve as pathways for cancerous cell transportation and hence are crucial for understanding cancer growth. Existing medical imaging modalities can provide 3-D contrast images of the vascular tissues but with limited quality and detailedness. A much-needed tool for cancer research is thus one that can reconstruct vascular networks from low-quality clinical images. To this end, we developed a computational framework that takes 3-D medical images as input and reconstructs complete, patient-specific vascular network models using a mathematical optimization procedure. Our framework extracts major vessels from the images and uses the organ geometry to select vessel termination points. Then, it generates the remainder network based on physiological optimality principles. Using the framework, we obtained a set of network models with over 3000 terminal segments from a brain MRA scan. We analyzed the Strahler order, vessel radius, and branch length distributions of the models, which match with actual human data. We also performed fluid dynamics simulation inside the reconstructed vessels and showed that the pressure and shear stress distributions agree with existing in vivo measurements. The qualitative and quantitative agreements in vessel morphometry and hemodynamics demonstrate the effectiveness of the framework. Our method bridges the gap between image-based vessel models, accuracy of which is limited by the resolution of clinical images, and hypothetical models.

To improve the understanding of the middle-ear hearing mechanism and assist in the diagnosis of middle-ear diseases, we are developing a high-speed digital holographic (HDH) system to measure the shape and acoustically-induced transient displacements of the tympanic membrane (TM). In this paper, we performed measurements on cadaveric human ears with simulated common middle-ear pathologies. The frequency response function (FRF) of the normalized displacement by the stimulus (sound pressure) at each measured pixel point of the entire TM surface was calculated and the complex modal indicator function (CMIF) of the middle-ear system based on FRFs of the entire TM surface motions was used to differentiate different middle-ear pathologies. We also observed changes in the TM shape and the surface motion pattern before and after various middle-ear manipulations. The observations of distinguishable TM shapes and motion patterns in both time and frequency domains between normal and experimentally simulated pathological ears support the development of a quantitative clinical holography-based apparatus for diagnosing middle-ear pathologies.