Receipt of Guideline-Recommended Surveillance in a Population-Based Cohort of Prostate Cancer Patients Undergoing Active Surveillance.

PURPOSE: Prospective clinical trials have demonstrated the safety and efficacy of active surveillance for men with localized prostate cancer but also suggested that inadequate surveillance may risk missing an opportunity for cure. METHODS AND MATERIALS: We used data from a population-based cohort of active-surveillance patients to examine the rigor of surveillance monitoring in the general population. RESULTS: Among 1419 patients enrolled from 2011 to 2013 throughout the state of North Carolina in collaboration with the state cancer registry and followed prospectively, 346 pursued active surveillance. Only 13% received all guideline-recommended surveillance testing (including prostate-specific antigen, digital rectal examination, and prostate biopsy) within the first 2 years. Furthermore, adherence was <20% in all patient subgroups. CONCLUSIONS: These findings suggest that "active surveillance" as implemented in the general population may not represent the rigorous monitoring regimens used in the studies that demonstrated the safety of this management approach. More real-world studies on active surveillance are needed.

Is preeclampsia an independent predictor of diastolic dysfunction? A retrospective cohort study.

OBJECTIVE: To determine if preeclampsia is an independent predictor of diastolic dysfunction and what factors among patients with preeclampsia are associated with diastolic dysfunction. METHODS: This is a retrospective cohort study of patients who delivered between 2008 and 2013 at a single institution who had a maternal echocardiogram during their pregnancy or within 5months of delivery. Patients with structural heart disease, ejection fraction less than 45%, pulmonary embolus, or age over 45years were excluded. Medical records were reviewed for medical and obstetric complications and echocardiogram findings. Demographic characteristics and rate of diastolic dysfunction were compared between patients with preeclampsia and without preeclampsia. Multivariate logistic regression was performed controlling for age, ethnicity, gestational age at delivery, diabetes, preeclampsia, intrauterine growth restriction (IUGR), antihypertensive use and magnesium sulfate administration. RESULTS: Sixty-six patients were identified, of which 39 (59%) had preeclampsia. Past history of preeclampsia, IUGR in the current pregnancy, antihypertensive use and magnesium sulfate use were higher in the preeclampsia group. Fifteen patients (39%) in the preeclampsia group were African-American compared to 2 (3%) in the control group (p<0.01). Seventeen (44%) of the patients with preeclampsia were found to have diastolic dysfunction compared to 3 (11%) controls (OR=6.18, 95% CI 1.59,24.02; p=0.006). Logistic regression analysis did not reveal other independent predictors of diastolic dysfunction. In the patients with preeclampsia, history of preeclampsia with severe features and IUGR were not associated with diastolic dysfunction. CONCLUSIONS: Our study supports previous findings that preeclampsia is associated with diastolic dysfunction.

Modeling spatial population dynamics of stem cell lineage in wound healing and cancerogenesis.

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 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 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.

Modeling spatial population dynamics of stem cell lineage in tissue growth.

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.