Accumulation of high magnitude acceleration events predicts cerebrovascular reactivity changes in female high school soccer athletes.

Mitigating the effects of repetitive exposure to head trauma has become a major concern for the general population, given the growing body of evidence that even asymptomatic exposure to head accelerations is linked with increased risk for negative life outcomes and that risk increases as exposure is prolonged over many years. Among women's sports, soccer currently exhibits the highest growth in participation and reports the largest number of mild traumatic brain injuries annually, making female soccer athletes a relevant population in assessing the effects of repetitive exposure to head trauma. Cerebrovascular biomarkers may be useful in assessing the effects of repetitive head trauma, as these are thought to contribute directly to neurocognitive symptoms associated with mild traumatic brain injury. Here we use fMRI paired with a hypercapnic breath hold task along with monitoring of head acceleration events, to assess the relationship between cerebrovascular brain changes and exposure to repetitive head trauma over a season of play in female high school soccer athletes. We identified longitudinal changes in cerebrovascular reactivity that were significantly associated with prolonged accumulation to high magnitude (> 75th percentile) head acceleration events. Findings argue for active monitoring of athletes during periods of exposure to head acceleration events, illustrate the importance of collecting baseline (i.e., pre-exposure) measurements, and suggest modeling as a means of guiding policy to mitigate the effects of repetitive head trauma.

Cerebrovascular reactivity changes in asymptomatic female athletes attributable to high school soccer participation.

As participation in women's soccer continues to grow and the longevity of female athletes' careers continues to increase, prevention and care for mTBI in women's soccer has become a major concern for female athletes since the long-term risks associated with a history of mTBI are well documented. Among women's sports, soccer exhibits among the highest concussion rates, on par with those of men's football at the collegiate level. Head impact monitoring technology has revealed that "concussive hits" occurring directly before symptomatic injury are not predictive of mTBI, suggesting that the cumulative effect of repetitive head impacts experienced by collision sport athletes should be assessed. Neuroimaging biomarkers have proven to be valuable in detecting brain changes that occur before neurocognitive symptoms in collision sport athletes. Quantifying the relationship between changes in these biomarkers and head impacts experienced by female soccer athletes may prove valuable to developing preventative measures for mTBI. This study paired functional magnetic resonance imaging with head impact monitoring to track cerebrovascular reactivity changes throughout a season and to test whether the observed changes could be attributed to mechanical loading experienced by female athletes participating in high school soccer. Marked cerebrovascular reactivity changes were observed in female soccer athletes, relative both to non-collision sport control measures and pre-season measures and were localized to fronto-temporal aspects of the brain. These changes persisted 4-5 months after the season ended and recovered by 8 months after the season. Segregation of the total soccer cohort into cumulative loading groups revealed that population-level changes were driven by athletes experiencing high cumulative loads, although athletes experiencing lower cumulative loads still contributed to group changes. The results of this study imply a non-linear relationship between cumulative loading and cerebrovascular changes with a threshold, above which the risk, of injury likely increases significantly.

Collegiate women's soccer players suffer greater cumulative head impacts than their high school counterparts.

Soccer is the source of the highest concussion rates among female athletes and is associated with neurological deficits at many levels of play. Despite its importance to our understanding of head trauma in female athletes, little is known about the number and magnitude of head impacts experienced by female soccer players. Head impacts experienced by high school and collegiate athletes were quantified using xPatch sensors (X2 Biosystems) affixed behind the right ear of each player. The average peak translational acceleration (PTA) sustained by players at the high school level was significantly lower than that of the collegiate players, but the average peak angular accelerations (PAA) were not significantly different. Given that the collegiate players took many more impacts throughout the season, their mean cumulative exposure to translational (cPTA) and angular accelerations (cPAA) were significantly higher than those of the high school players. Additional research is required to determine whether the differences in cumulative exposure are responsible for the elevated risk of concussion in collegiate soccer players or if there are additional risk factors.

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization.

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.

Optimal chemotherapy for leukemia: a model-based strategy for individualized treatment.

Acute Lymphoblastic Leukemia, commonly known as ALL, is a predominant form of cancer during childhood. With the advent of modern healthcare support, the 5-year survival rate has been impressive in the recent past. However, long-term ALL survivors embattle several treatment-related medical and socio-economic complications due to excessive and inordinate chemotherapy doses received during treatment. In this work, we present a model-based approach to personalize 6-Mercaptopurine (6-MP) treatment for childhood ALL with a provision for incorporating the pharmacogenomic variations among patients. Semi-mechanistic mathematical models were developed and validated for i) 6-MP metabolism, ii) red blood cell mean corpuscular volume (MCV) dynamics, a surrogate marker for treatment efficacy, and iii) leukopenia, a major side-effect. With the constraint of getting limited data from clinics, a global sensitivity analysis based model reduction technique was employed to reduce the parameter space arising from semi-mechanistic models. The reduced, sensitive parameters were used to individualize the average patient model to a specific patient so as to minimize the model uncertainty. Models fit the data well and mimic diverse behavior observed among patients with minimum parameters. The model was validated with real patient data obtained from literature and Riley Hospital for Children in Indianapolis. Patient models were used to optimize the dose for an individual patient through nonlinear model predictive control. The implementation of our approach in clinical practice is realizable with routinely measured complete blood counts (CBC) and a few additional metabolite measurements. The proposed approach promises to achieve model-based individualized treatment to a specific patient, as opposed to a standard-dose-for-all, and to prescribe an optimal dose for a desired outcome with minimum side-effects.

Adding exercise to rosuvastatin treatment: influence on C-reactive protein, monocyte toll-like receptor 4 expression, and inflammatory monocyte (CD14+CD16+) population.

Statin treatment and exercise training can reduce markers of inflammation when administered separately. The purpose of this study was to determine the effect of rosuvastatin treatment and the addition of exercise training on circulating markers of inflammation including C-reactive protein (CRP), monocyte toll-like receptor 4 (TLR4) expression, and CD14+CD16+ monocyte population size. Thirty-three hypercholesterolemic and physically inactive subjects were randomly assigned to rosuvastatin (R) or rosuvastatin/exercise (RE) groups. A third group of physically active hypercholesterolemic subjects served as a control (AC). The R and RE groups received rosuvastatin treatment (10 mg/d) for 20 weeks. From week 10 to week 20, the RE group also participated in an exercise training program (3d/wk). Measurements were made at baseline (Pre), week 10 (Mid), and week 20 (Post), and included TLR4 expression on CD14+ monocytes and CD14+CD16+ monocyte population size as determined by 3-color flow cytometry. Serum CRP was quantified by enzyme-linked immunosorbent assay. TLR4 expression on CD14+ monocytes was higher in the R group at week 20. When treatment groups (R and RE) were combined, serum CRP was lower across time. Furthermore, serum CRP and inflammatory monocyte population size were lower in the RE group compared with the R group at the Post time point. When all groups (R, RE, and AC) were combined, TLR4 expression was greater on inflammatory monocytes (CD14+CD16+) compared with classic monocytes (CD14+CD16⁻) at all time points. In conclusion, rosuvastatin may influence monocyte inflammatory response by increasing TLR4 expression on circulating monocytes. The addition of exercise training to rosuvastatin treatment further lowered CRP and reduced the size of the inflammatory monocyte population, suggesting an additive anti-inflammatory effect of exercise.

Using adaptive model predictive control to customize maintenance therapy chemotherapeutic dosing for childhood acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is a common childhood cancer in which nearly one-quarter of patients experience a disease relapse. However, it has been shown that individualizing therapy for childhood ALL patients by adjusting doses based on the blood concentration of active drug metabolite could significantly improve treatment outcome. An adaptive model predictive control (MPC) strategy is presented in which maintenance therapy for childhood ALL is personalized using routine patient measurements of red blood cell mean corpuscular volume as a surrogate for the active drug metabolite concentration. A clinically relevant mathematical model is developed and used to describe the patient response to the chemotherapeutic drug 6-mercaptopurine, with some model parameters being patient-specific. During the course of treatment, the patient-specific parameters are adaptively identified using recurrent complete blood count measurements, which sufficiently constrain the patient parameter uncertainty to support customized adjustments of the drug dose. While this work represents only a first step toward a quantitative tool for clinical use, the simulated treatment results indicate that the proposed mathematical model and adaptive MPC approach could serve as valuable resources to the oncologist toward creating a personalized treatment strategy that is both safe and effective.

Adding exercise training to rosuvastatin treatment: influence on serum lipids and biomarkers of muscle and liver damage.

Statin treatment and exercise training can improve lipid profile when administered separately. The efficacy of exercise and statin treatment combined, and its impact on myalgia and serum creatine kinase (CK) have not been completely addressed. The purpose of this study was to determine the effect of statin treatment and the addition of exercise training on lipid profile, including oxidized low-density lipoprotein (oxLDL), and levels of CK and alanine transaminase. Thirty-one hypercholesterolemic and physically inactive subjects were randomly assigned to rosuvastatin (R) or rosuvastatin/exercise (RE) group. A third group of physically active hypercholesterolemic subjects served as an active control group (AC). The R and RE groups received rosuvastatin treatment (10 mg/d) for 20 weeks. From week 10 to week 20, the RE group also participated in a combined endurance and resistive exercise training program (3 d/wk). Lipid profile was determined for all subjects at week 0 (Pre), week 10 (Mid), and week 20 (Post). The CK and alanine transaminase levels were measured at the same time points in the RE and R groups and 48 hours after the first and fifth exercise bout in the RE group. Each RE subject was formally queried about muscle fatigue, soreness, and stiffness before each training session. Total, LDL, and oxLDL cholesterol was lower in the RE and R groups at Mid and Post time points when compared with Pre. Oxidized LDL was lower in the RE group compared with the R group at the Post time point. When treatment groups (R and RE) were combined, high-density lipoprotein levels were increased and triglycerides decreased across time. Creatine kinase increased in the RE group 48 hours after the first exercise bout, but returned to baseline levels 48 hours after the fifth exercise bout. Rosuvastatin treatment decreased total, LDL, and oxLDL cholesterol. The addition of an exercise training program resulted in a further decrease in oxLDL. There was no abnormal sustained increase in CK or reports of myalgia after the addition of exercise training to rosuvastatin treatment.

Estimation of likely cancer cure using first- and second-order product densities of population balance models.

The objective of chemotherapy is to eradicate all cancerous cells. However, due to the stochastic behavior of cells, the elimination of all cancerous cells must be discussed probabilistically. We hypothesize, and demonstrate in the results, that the mean and standard deviation of a cancer cell population, derived through the probabilistic interpretation of population balance equations, are sufficient to estimate the likelihood of cancer eradication. Our analysis of a binary cell division model reveals that an expected cancer population that is six standard deviations less than one cell provides a good estimate for the treatment durations that nearly ensures treatment successes. This approximation is evaluated and tested on two other physiologically likely scenarios: variable patient response to chemotherapy and the presence of a dormant population. We find that early identification of individual patient susceptibility to the chemotherapeutic agent is extremely important to all patients as treatment adjustments for non-responders greatly enhances their likelihood of cure while responders need not be subjected to needlessly harsh treatments. Presence of a dormant population increases both the required treatment duration and population variability, but the same estimation method holds. This work is a step toward using stochastic models for a quantitative evaluation of chemotherapy.