Trade-offs in concentration sensing in dynamic environments.

When cells measure concentrations of chemical signals, they may average multiple measurements over time in order to reduce noise in their measurements. However, when cells are in an environment that changes over time, past measurements may not reflect current conditions-creating a new source of error that trades off against noise in chemical sensing. What statistics in the cell's environment control this trade-off? What properties of the environment make it variable enough that this trade-off is relevant? We model a single eukaryotic cell sensing a chemical secreted from bacteria (e.g., folic acid). In this case, the environment changes because the bacteria swim-leading to changes in the true concentration at the cell. We develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies ranging from not time averaging at all to averaging over an arbitrarily long time or having a finite optimal averaging time. The factors that primarily control the ideal averaging are the ratio of sensing noise to environmental variation and the ratio of timescales of sensing to the timescale of environmental variation. Sensing noise depends on the receptor-ligand kinetics, while environmental variation depends on the density of bacteria and the degradation and diffusion properties of the secreted chemoattractant. Our results suggest that fluctuating environmental concentrations may be a relevant source of noise even in a relatively static environment.

Investigating Racial Disparities in Drug Prescriptions for Patients with Endometriosis.

Background: Endometriosis is a chronic disease with a long time to diagnosis and several known comorbidities that requires a range of treatments including of pain management and hormone-based medications. Racial disparities specific to endometriosis treatments are unknown. Objective: We aim to investigate differences in patterns of drug prescriptions specific to endometriosis management in Black and White patients prior to diagnosis and after diagnosis of endometriosis and compare these differences to racial disparities established in the general population. Study Design: We conduct a retrospective cohort study using observational health data from the IBM MarketScan® Multi-state Medicaid dataset. We identify a cohort of endometriosis patients consisting of women between the ages of 15 and 49 with an endometriosis-related surgical procedure and a diagnosis code for endometriosis within 30 days of this procedure. Cohort is further restricted to patients with at least 3 years of continuous observation prior to diagnosis.We identify a non-endometriosis cohort of women between the ages of 15 and 49 with no endometriosis diagnosis and at least 1 year of continuous observation. We compare prevalence of prescriptions across selected drug classes for Black vs. White endometriosis patients. We further examine prevalence differences in the non-endometriosis cohort and prevalence differences pre- and post-diagnosis in the endometriosis cohort. Results: The endometriosis cohort comprised 16,372 endometriosis patients (23.3% Black, 66.0% White). Of the 28 drug classes examined, 17 were prescribed significantly less in Black patients compared to 21 in non-endometriosis cohort (n=3,663,904), and 4 were prescribed significantly more in Black patients compared to 6 in the non-endometriosis cohort. Of the 17 drugs prescribed more often in White patients, 16 have larger disparities pre-diagnosis than post-diagnosis. Conclusions: Our analysis identified significant differences in medication prescriptions between White and Black patients with endometriosis, notably in hormonal treatments, pain management, and treatments for common endometriosis co-morbidities. Racial disparities in drug prescriptions are well established in healthcare, and better understanding these disparities in the specific context of chronic reproductive conditions and chronic pain is important for increasing equity in drug prescription practices.

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients.

As clinicians are faced with a deluge of clinical data, data science can play an important role in highlighting key features driving patient outcomes, aiding in the development of new clinical hypotheses. Insight derived from machine learning can serve as a clinical support tool by connecting care providers with reliable results from big data analysis that identify previously undetected clinical patterns. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying sub-groups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triaged classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises.