Background: Androgens bind to the androgen receptor (AR) in prostate cells and are essential survival factors for healthy prostate epithelium. Most untreated prostate cancers retain some dependence upon the AR and respond, at least transiently, to androgen ablation therapy. However, the relationship between endogenous androgen levels and cancer etiology is unclear. High levels of androgens have traditionally been viewed as driving abnormal proliferation leading to cancer, but it has also been suggested that low levels of androgen could induce selective pressure for abnormal cells. We formulate a mathematical model of androgen regulated prostate growth to study the effects of abnormal androgen levels on selection for pre-malignant phenotypes in early prostate cancer development.
Results: We find that cell turnover rate increases with decreasing androgen levels, which may increase the rate of mutation and malignant evolution. We model the evolution of a heterogeneous prostate cell population using a continuous state-transition model. Using this model we study selection for AR expression under different androgen levels and find that low androgen environments, caused either by low serum testosterone or by reduced 5α-reductase activity, select more strongly for elevated AR expression than do normal environments. High androgen actually slightly reduces selective pressure for AR upregulation. Moreover, our results suggest that an aberrant androgen environment may delay progression to a malignant phenotype, but result in a more dangerous cancer should one arise.
Conclusions: The model represents a useful initial framework for understanding the role of androgens in prostate cancer etiology, and it suggests that low androgen levels can increase selection for phenotypes resistant to hormonal therapy that may also be more aggressive. Moreover, clinical treatment with 5α-reductase inhibitors such as finasteride may increase the incidence of therapy resistant cancers.
Objective: The purpose of this randomized controlled trial was to determine whether adherence to a weight loss app providing intake limits and more food group detail (the Food Lists app) facilitated more weight loss and better diet quality than adherence to a weight loss app based on the MyPlate platform. An additional objective was to examine whether higher app adherence would lead to greater weight loss.
Design: Thirty seven adults from a campus population were recruited, randomized, and instructed to follow either the Food Lists app (N=20) or the MyPlate app (N=17) for eight weeks. Subjects received one 15 minute session of diet and app training at baseline, and their use of the app was tracked daily. Body mass was measured at baseline and post-test.
Participants/setting: Healthy adults from a university campus population in downtown Phoenix, Arizona with BMI 24 to 40, medically stable, and who owned a smartphone.
Main outcome measures: Outcome measures included weight change, days of adherence, and diet quality change. Secondary measures included BMI, fat %, and waist circumference.
Statistical analysis: Descriptive statistics (means and standard errors); Repeated measures ANOVAs analyzing weight, diet quality, and BMI; Pearson and Spearman correlations analyzing adherence and weight loss.
Results: Repeated measures ANOVAs and correlations revealed no significant mean differences in primary outcome variables of weight loss, adherence, or diet quality (P=0.140; P=0.790; P=0.278). However, there was a significant mean reduction of BMI favoring the group using the Food Lists app (P=0.041).
Conclusion: The findings strengthen the idea that intake limits and food group detail may be associated with weight loss. Further investigation is warranted to determine whether longer use of the Food Lists app can produce more significant dieting successes and encourage healthier behavioral outcomes.
appears in a smaller region inside of the tumor. Simulations show that if the aggressive strain focuses its efforts on proliferating and does not contribute to angiogenesis signaling when in a hypoxic state, a hypertumor will form. More importantly, this resultant aggressive tumor is paradoxically prone to extinction and hypothesize is the cause of necrosis in many vascularized tumors.
Predicting the timing of a castrate resistant prostate cancer is critical to lowering medical costs and improving the quality of life of advanced prostate cancer patients. We formulate, compare and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). We accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). While these models are simplifications of a previously published model, they fit data with similar accuracy and improve forecasting results. Both models describe the progression of androgen resistance. Although Model 1 is simpler than the more realistic Model 2, it can fit clinical data to a greater precision. However, we found that Model 2 can forecast future PSA levels more accurately. These findings suggest that including more realistic mechanisms of androgen dynamics in a two population model may help androgen resistance timing prediction.
Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.
Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.
Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.
