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3D paper-based cultures (PBCs) are easy-to-use and provide a biologically representative microenvironment. By stacking a sheet of cell-laden paper below sheets containing cell-free hydrogel, we form an assay capable of segmenting cells by the distance they invaded from the original cell-seeded layer. These invasion assays are limited to end-point analyses with fluorescence-based readouts due to the highly scattering nature of the paper scaffolds. Here we demonstrate that optical coherence tomography (OCT) can distinguish living cells from the surrounding extracellular matrix (ECM) or paper fibers based upon their intracellular motility amplitude (M).Mis computed from fluctuation statistics of the sample, rejects shot noise, and is invariant to OCT signal attenuation. Using OCT motility analysis, we tracked the invasion of breast cancer cells over a 3-day period in 4-layer PBCs (160–300 µm thick)in situ. The cell population distributions determined with OCT are highly correlated with those obtained by fluorescence imaging, with an intraclass correlation coefficient (ICC) of 0.903. The ability of OCT motility analysis to visualize live cells and quantify cell distributions in PBC assaysin situand longitudinally provides a novel means for understanding how chemical gradients within the tumor microenvironment affect cellular invasion. 

Interval‐censored failure time data commonly arise in epidemiological and biomedical studies where the occurrence of an event or a disease is determined via periodic examinations. Subject to interval‐censoring, available information on the failure time can be quite limited. Cost‐effective sampling designs are desirable to enhance the study power, especially when the disease rate is low and the covariates are expensive to obtain. In this work, we formulate the case‐cohort design with multiple interval‐censored disease outcomes and also generalize it to nonrare diseases where only a portion of diseased subjects are sampled. We develop a marginal sieve weighted likelihood approach, which assumes that the failure times marginally follow the proportional hazards model. We consider two types of weights to account for the sampling bias, and adopt a sieve method with Bernstein polynomials to handle the unknown baseline functions. We employ a weighted bootstrap procedure to obtain a variance estimate that is robust to the dependence structure between failure times. The proposed method is examined via simulation studies and illustrated with a dataset on incident diabetes and hypertension from the Atherosclerosis Risk in Communities study.