Understanding the dynamics of cell population allows insight into the control mechanism of the growth and development of mammalian tissues. It is well known that the proliferation and differentiation among stem cells (SCs), intermediate progenitor cells (IPCs), and fully differentiated cells (FDCs) are under different activation and inhibition controls. Secreted factors in negative feedback loops have already been identified as major elements in regulating the numbers of different cell types and in maintaining the equilibrium of cell populations. We have developed a novel spatial dynamic model of cells. We can characterize not only overall cell population dynamics, but also details of temporal-spatial relationship of individual cells within a tissue. In our model, the shape, growth, and division of each cell are modeled using a realistic geometric model. Furthermore, the inhibited growth rate, proliferation and differentiation probabilities of individual cells are modeled through feedback loops controlled by secreted factors of neighboring cells within a proper diffusion radius. With specific proliferation and differentiation probabilities, the actual division type that each cell will take is chosen by a Monte Carlo sampling process. With simulations we found that with proper strengths of inhibitions to growth and stem cell divisions, the whole tissue is capable of achieving a homeostatic size control. We discuss our findings on control mechanisms of the stability of the tissue development. Our model can be applied to study broad issues on tissue development and pattern formation in stem cell and cancer research.
6512 Background: AS is recommended for early-stage prostate cancer, for which over-treatment has been widely described. In published studies from large academic institutions and/or controlled clinical trials, where patients are monitored rigorously, AS is safe and results in low rates of cancer-specific mortality. However, active surveillance in the community setting has not been previously examined. Methods: In collaboration with the North Carolina state cancer registry, 346 men with newly-diagnosed low- or intermediate-risk prostate cancer throughout the state from 2011–13 who pursued active surveillance were enrolled in an observational cohort; medical records and patient-reported outcomes (validated measures of prostate cancer anxiety [MAX-PC] and Clark’s prostate cancer decision regret) were collected prospectively. Guideline-adherent monitoring during active surveillance was assessed using contemporary NCCN guidelines: PSA testing every 3–6 months and prostate biopsy within 18 months of initial diagnosis. Results: 58% of patients received adequate PSA testing and 45% prostate biopsy; overall, 32% of patients received guideline-adherent monitoring. Urology follow-up in Year 1 was 97% but dropped to 67% in Year 2. Within the first 2 years, 16% of patients converted to treatment. Multivariable analysis showed MAX-PC scores (OR 1.8, p = 0.008) and younger age were significantly associated with conversion; no other sociodemographic (race, education, marital status, rural/urban) or diagnostic variable (risk group) was associated. At 2 years, 94% expressed no regret. Conclusions: In a non-controlled setting of patients pursuing AS in the community, adherence to guideline-recommended monitoring was only 32%. Few patients expressed decisional regret. Conversion to treatment was likely driven by patient anxiety but not disease-related factors. While there are continued efforts to increase AS uptake, these results highlight the importance of behavioral interventions during active surveillance to reduce anxiety and improve monitoring adherence. Whether AS in non-controlled settings is safe and effective requires further study.
Modeling the dynamics of cell population in tissues involving stem cell niches allows insight into the control mechanisms of the important wound healing process. It is well known that growth and divisions of stem cells are mainly repressed by niche cells, but can also be activated by signals released from wound. In addition, the proliferation and differentiation among three different types of cell: stem cells (SCs), intermediate progenitor cells (IPCs), and fully differentiated cells (FDCs) in stem cell lineage are under different activation and inhibition controls. We have developed a novel stochastic spatial dynamic model of cells. We can characterize not only overall cell population dynamics, but also details of temporalspatial relationship of individual cells within a tissue. In our model, the shape, growth, and division of each cell are modeled using a realistic geometric model. Furthermore, the inhibited growth rate, proliferation and differentiation probabilities of individual cells are modeled through feedback loops controlled by secreted factors and wound signals from neighboring cells. With specific proliferation and differentiation probabilities, the actual division type that each cell will take is chosen by a Monte Carlo sampling process. With simulations, we study the effects of different strengths of wound signals to wound healing behaviors. We also study the correlations between chronic wound and cancerogenesis.
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