Bioethics is an important aspect of the core competency of biology of understanding the relationship between science and society, but because of the controversial nature of the topics covered in bioethics courses, different groups of students may experience identity conflicts or discomfort when learning about them. However, no previous studies have investigated the impact of undergraduate bioethics students’ experiences in bioethics courses on their opinions and comfort. To fill this gap in knowledge, we investigated undergraduate bioethics students’ attitudes about and comfort when learning abortion, gene editing, and physician assisted suicide, as well as how their gender, religious, and political identity influence their attitudes and changes in their attitudes after instruction. We found that religious students were less supportive of gene editing, abortion, and physician assisted suicide than nonreligious students, non-liberal students were less supportive of abortion and physician assisted suicide than liberal students, and women were less supportive of abortion than men. Additionally, we found that religious students were less comfortable than nonreligious students when learning about gene editing, abortion, and physician assisted suicide, and non-liberal students were less comfortable than liberal students when learning about abortion. When asked how their comfort could have been improved, those who felt that their peers or instructors could have done something to increase their comfort most commonly cited that including additional unbiased materials or incorporating materials and discussions that cover both sides of every controversial issue would have helped them to feel more comfortable when learning about gene editing, abortion, and physician assisted suicide. Finally, we found that students who were less comfortable learning about abortion and physician assisted suicide were less likely to participate in discussions regarding those topics. Our findings show that students in different groups not only tend to have different support for controversial topics like gene editing, abortion, and physician assisted suicide, but they also feel differentially comfortable when learning about them, which in turn impacts their participation. We hope that this work helps instructors to recognize the importance of their students’ comfort to their learning in bioethics courses, and from this study, they can take away the knowledge that students feel their comfort could be most improved by the incorporation of additional inclusive materials and course discussions regarding the controversial topics covered in the course.

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.