Identifying important parameters in the inflammatory process with a mathematical model of immune cell influx and macrophage polarization.

In an inflammatory setting, macrophages can be polarized to an inflammatory M1 phenotype or to an anti-inflammatory M2 phenotype, as well as existing on a spectrum between these two extremes. Dysfunction of this phenotypic switch can result in a population imbalance that leads to chronic wounds or disease due to unresolved inflammation. Therapeutic interventions that target macrophages have therefore been proposed and implemented in diseases that feature chronic inflammation such as diabetes mellitus and atherosclerosis. We have developed a model for the sequential influx of immune cells in the peritoneal cavity in response to a bacterial stimulus that includes macrophage polarization, with the simplifying assumption that macrophages can be classified as M1 or M2. With this model, we were able to reproduce the expected timing of sequential influx of immune cells and mediators in a general inflammatory setting. We then fit this model to in vivo experimental data obtained from a mouse peritonitis model of inflammation, which is widely used to evaluate endogenous processes in response to an inflammatory stimulus. Model robustness is explored with local structural and practical identifiability of the proposed model a posteriori. Additionally, we perform sensitivity analysis that identifies the population of apoptotic neutrophils as a key driver of the inflammatory process. Finally, we simulate a selection of proposed therapies including points of intervention in the case of delayed neutrophil apoptosis, which our model predicts will result in a sustained inflammatory response. Our model can therefore provide hypothesis testing for therapeutic interventions that target macrophage phenotype and predict outcomes to be validated by subsequent experimentation.

A mathematical model of the effects of resistance exercise-induced muscle hypertrophy on body composition.

PURPOSE: Current diet and exercise methods used to maintain or improve body composition often have poor long-term outcomes. We hypothesize that resistance exercise (RE) should aid in the maintenance of a healthy body composition by preserving lean mass (LM) and metabolic rate. METHOD: We extended a previously developed energy balance model of human metabolism to include muscle hypertrophy in response to RE. We first fit model parameters to a hypothetical individual to simulate an RE program and then compared the effects of a hypocaloric diet only to the diet with either cardiovascular exercise (CE) or RE. We then simulated a cohort of individuals with different responses to RE by varying the parameters controlling it using Latin Hypercube Sampling (LHS). Finally, we fit the model to mean data from an elderly population on an RE program. CONCLUSION: The model is able to reproduce the time course of change in LM in response to RE and can be used to generate a simulated cohort for in silico clinical studies. Simulations suggest that the additional LM generated by RE may shift the body composition to a healthier state.

Correction to: A mathematical model of the effects of resistance exercise-induced muscle hypertrophy on body composition.

There is a typo in the original equation describing lean mass, and it has also been pointed out to the authors that the model is not strictly energy balanced.

Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility.

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.

Integrating Undergraduate Patient Partners into Diabetes self-management education: Evaluating a free clinic pilot program for the Underserved.

BACKGROUND: Diabetes self-management education (DSME) improves glycemic control and health outcomes in patients with diabetes. OBJECTIVE: A process evaluation of a two-year pilot intervention examined the feasibility and acceptability of undergraduate volunteers as Patient Partners to foster DSME participation among the underserved.Design setting, and participants. In the setting of a student-run free clinic, 22 patients enrolled in DSME were paired with 16 undergraduate volunteers. During the DSME courses, Patient Partners assisted patients during classes, called patients weekly, and accompanied patients to clinic appointments.Key process evaluation results. Average attendance at DSME classes was 79.4% and 94.7% for patients and Patient Partners, respectively. Sixty-three percent of phone calls were successful and Patient Partners attended 50% of appointments with their patients. Focus groups demonstrated resounding acceptability of the Patient Partner role. CONCLUSIONS: Volunteer undergraduate Patient Partners are a beneficial adjunct to DSME delivery in the resource-constrained environment of a student-run free clinic.