Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.
The evolution of cooperation is a fundamental problem in biology, especially for non-relatives, where indirect fitness benefits cannot counter within-group inequalities. Multilevel selection models show how cooperation can evolve if it generates a group-level advantage, even when cooperators are disadvantaged within their group. This allows the possibility of group selection, but few examples have been described in nature. Here we show that group selection can explain the evolution of cooperative nest founding in the harvester ant Pogonomyrmex californicus. Through most of this species’ range, colonies are founded by single queens, but in some populations nests are instead founded by cooperative groups of unrelated queens. In mixed groups of cooperative and single-founding queens, we found that aggressive individuals had a survival advantage within their nest, but foundress groups with such non-cooperators died out more often than those with only cooperative members. An agent-based model shows that the between-group advantage of the cooperative phenotype drives it to fixation, despite its within-group disadvantage, but only when population density is high enough to make between-group competition intense. Field data show higher nest density in a population where cooperative founding is common, consistent with greater density driving the evolution of cooperative foundation through group selection.
Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.
A total of 26 human subjects were used in this study. Each subject was classified as either lean or obese, according to their BMI measurement. First, the subjects underwent an oral glucose tolerance test. Blood samples were taken to measure glucose levels in the blood. After the test subject characteristics for each subject was obtained. These included age, BMI, body fat percentage, fat free mass (FFM), height, total mass, waist circumference, hip circumference, and waist to hip ratio. After the subject characteristics and blood glucose were measured the blood samples taken previously were then centrifuged, and the blood plasma was extracted. The blood plasma was then used to undergo an Insulin ELISA test. After extensive analysis, the Matsuda Index of each subject was obtained. Subjects with a Matsuda value of 6.0 or under were considered insulin resistant while subjects with a Matsuda value higher than 6.0 were considered insulin sensitive. Subjects were also required to submit a dietary record over the course of three days. The food intake was then put into a food processing software which gave a daily average of the macro and micro nutrients for each subject. Both the subject and dietary values were put into a multiple regression with a significance factor of p < 0.5 to see which factors contributed most to the Matsuda value.
It was found that BMI, height, total mass, insulin and fat free mass, all of which were subject characteristics, were considered to be significant. Some of these factors an individual has no control over, such as height and insulin. However other factors such as BMI, total mass and fat free mass can be affected by both a healthy diet and frequent exercise. This study validated that diet and physical activity can greatly influence an individual’s susceptibility to insulin resistance and ultimately T2DM.
