Bladder cancer is a common human malignancy. Conventional ultrasound and white-light cystoscopy are often used for bladder cancer diagnosis and resection, but insufficient specificity results in a high bladder cancer recurrence rate. New strategies for the diagnosis and resection of bladder cancer are needed. In this study, we developed a highly specific peptide-based probe for bladder cancer photoacoustic imaging (PAI) diagnosis and near-infrared (NIR)-imaging-guided resection after instillation. A bladder cancer–specific peptide (PLSWT7) was selected by in vivo phage-display technology and labeled with IRDye800CW to synthesize a bladder cancer–specific dual-modality imaging (DMI) probe (PLSWT7-DMI). The feasibility of PLSWT7-DMI–based dual-modality PAI-NIR imaging was assessed in vitro, in mouse models, and ex vivo human bladders. An air-pouch bladder cancer (APBC) model suitable for probe instillation was established to evaluate the probe-based bladder cancer PAI diagnosis and NIR-imaging–guided resection. Human bladders were used to assess whether the PLSWT7-DMI–based DMI strategy is a translatable approach for bladder cancer detection and resection. The probe exhibited excellent selectivity and specificity both in vitro and in vivo. Postinstillation of the probe, tumors <3 mm were detectable by PAI, and NIR-imaging–guided tumor resection decreased the bladder cancer recurrence rate by 90% and increased the survival in the mouse model. Additionally, ex vivo NIR imaging of human bladders indicated that PLSWT7-DMI–based imaging would potentially allow precise resection of bladder cancer in clinical settings. This PLSWT7-DMI–based DMI strategy was a translatable approach for bladder cancer diagnosis and resection and could potentially lower the bladder cancer recurrence rate. Mol Cancer Ther; 17(10); 2100–11. ©2018 AACR.

Computational fluid dynamics-discrete element method is frequently used for modeling particulate flows due to its high efficiency and satisfactory accuracy. The particle volume fraction is a crucial parameter that significantly affects the computation accuracy. It may be extremely large when the particulate flows contain coarse particles because it is determined by the ratio of particle volume to cell volume. In this paper, the performance of different methods, such as the divided particle volume method (DPVM), the big particle method, and the diffusion-based method, for computing the particle volume fraction is thoroughly reviewed, implemented, and investigated. It turns out that the DPVM must not be used when the particle size is larger than cell size due to significant fluctuation of the particle volume fraction field. The big particle method is optimized for simulation accuracy and code implementation. The optimized big particle method is similar to the diffusion-based method by diffusing the particle effects to the surrounding cells. It demonstrates greater consistency with experimental observations compared to the diffusion-based method, primarily attributed to its incorporation of polydisperse effects.

The Eulerian–Lagrangian approach is commonly employed in particulate flows, which can be classified into two subcategories: unresolved computational fluid dynamics-discrete element method (CFD–DEM) and resolved CFD–DEM. When the particle sizes are comparable to the cell sizes, the interphase coupling is not straightforward anymore and both the conventional unresolved CFD–DEM and resolved CFD–DEM are not applicable. In this paper, we propose a simple and novel coupling method for projecting and reconstructing the particle and interphase quantities, which is also called semi-resolved CFD–DEM. The particle quantities are uniformly distributed to the surrounding cells by expanding the fluid domain. The fluid phase quantities at the particle location are also reconstructed from the surrounding cells. Then, the relative velocity and the local void fraction for calculating the drag force are corrected. The expanding factor for the fluid domain is determined by comparing the drag force of the semi-resolved CFD–DEM with the resolved CFD–DEM results. It is found that the expanding factor increases linearly with the autocorrelation length. The developed method is validated by the simulation of a particle sedimentation and a sediment transport process, which proves that the present semi-resolved CFD–DEM fills the gap between the resolved and unresolved CFD–DEM. The difference between the implicit and explicit treatment of momentum exchange term is also discussed, and the explicit treatment shows better performance for large particles.

The proppant transportation is a typical two-phase flow process in a complex cross fracture network during hydraulic fracturing. In this paper, the proppant transportation in cross fractures is investigated by the computational fluid dynamics (CFD) method. The Euler–Euler two-phase flow model and the kinetic theory of granular flow (KTGF) are adopted. The dimensionless controlling parameters are derived by dimensional analysis. The equilibrium proppant height (EPH) and the ratio of the proppant mass (RPM) in the secondary fracture to that in the whole cross fracture network are used to describe the movement and settlement of proppants in the cross fractures. The main features of the proppant transportation in the cross fractures are given, and several relative suggestions are presented for engineering application in the field. The main controlling dimensionless parameters for relative EPH are the proppant Reynolds number and the inlet proppant volume fraction. The dominating dimensionless parameters for RPM are the relative width of the primary and the secondary fracture. Transportation of the proppants with a certain particle size grading into the cross fractures may be a good way for supporting the hydraulic fractures.