Optimal and Model Free Control of Tumor Immune Interaction Dynamic to Schedule Cancer Treatments

Cancer is an intricate disease that can attack different parts of the human body. In the most common types of cancer, abnormal cells divide uncontrollably and impair body tissue. Cross disciplinary research has long aided expansion of our knowledge and ability to approach problems with a different perspective. Engineers and clinicians can collaborate to solve mysteries surrounding cancer cells function and responses. Engineers have contributed to cancer treatment, by studying new ways to diagnose and treat cancer. According to a study by John Hopkins university engineered Nano-particles can induce immune reaction and kill cancer cells. In addition, new ways of delivering cancer therapy to actuate the immune system to kill cancerous cells were found through engineering research. The goal behind modelling biological systems is to drive the states to a desirable outcome using control elements in the dynamic system. In this thesis, we explore the effects of an Intelligent Proportional Integral Derivative (iPID) controllers; using optimal and model free control to improve the state of a cancer patient using recommended safe dosages. A non-linear mathematical model of Ordinary Differential Equations (ODE) is used to simulate a virtual cancer patient using realistic valued parameters. It is important to bear in mind that the human body is complex and variable. In particular, the immune response can vary from one patient to another. The parameters used to model the cancer have been deduced by clinicians and engineers to represent the tumor immune interaction using mathematical equations. The dissertation will begin by exploring the cancer therapy\u27s mathematical model, the controllability and observability to assure that the model is controllable, and explore different control methods and compare the results

Toward simple in silico experiments for drugs administration in some cancer treatments

International audienceWe present some "in silico" experiments to design combined chemo-and immunotherapy treatment schedules. We introduce a new framework by combining flatness-based control, which is a model-based setting, along with model-free control. The flatness property of the used mathematical model yields straightforward reference trajectories. They provide us with the nominal open-loop control inputs. Closing the loop via model-free control allows to deal with the uncertainties on the injected drug doses. Several numerical simulations illustrating different case studies are displayed. We show in particular that the considered health indicators are driven to the safe region, even for critical initial conditions. Furthermore, in some specific cases there is no need to inject chemotherapeutic agents

Preliminary in silico experiments: Towards new cancer treatments?

We present some “in silico” experiments to design combined chemo- and immunotherapy treatment schedules. We introduce a new framework by combining flatness-based control along with model-free control. The flatness property of the used mathematical model yields straightforward reference trajectories. They provide us with the nominal open-loop control inputs. Closing the loop via model-free control allows to deal with the uncertainties on the injected drug doses. Several numerical simulations illustrating different case studies are displayed. We show in particular that the considered health indicators are driven to the safe region, even for critical initial conditions. Furthermore, in some specific cases there is no need to inject chemotherapeutic agents

Ability of Admissions Criteria to Predict Early Academic Performance Among Students of Health Science Colleges at King Saud University, Saudi Arabia

Abstract: The aim of this study was to evaluate the ability of admissions criteria at King Saud University (KSU), Riyadh, Saudi Arabia, to predict students' early academic performance at three health science colleges (medicine, dentistry, and pharmacy). A retrospective cohort study was conducted with data from the records of students enrolled in the three colleges from the 2008-09 to 2010-11 academic years. The admissions criteria-high school grade average (HSGA), aptitude test (APT) score, and achievement test (ACT) score-were the independent variables. The dependent variable was the average of students' first-and secondyear grade point average (GPA). The results showed that the ACT was a better predictor of the students' early academic performance than the HSGA (β=0.368, β=0.254, respectively). No significant relationship was found between the APT and students' early academic performance (β=-0.019, p>0.01). The ACT was most predictive for pharmacy students (β=0.405), followed by dental students (β =0.392) and medical students (β=0.195). Overall, the current admissions criteria explained only 25.5% of the variance in the students' early academic performance. While the ACT and HSGA were found to be predictive of students' early academic performance in health colleges at KSU, the APT was not a strong predictor. Since the combined current admissions criteria for the health science colleges at KSU were weak predictors of the variance in early academic performance, it may be necessary to consider noncognitive evaluation methods during the admission process

Association of Gastric Myoelectric Activity with Dietary Intakes, Substrate Utilization, and Energy Expenditure in Adults with Obesity

Obesity can modulate gastric myoelectric activity (GMA); however, the relationship of GMA with nutrient intakes and substrate utilization in adults with obesity is lacking. We examined the association of dietary intakes, energy expenditure, and substrate utilization with the GMA. Participants (n = 115, 18–60 y) were divided into healthy weight (HW, n = 24), overweight (OW, n = 29), obese (OB, n = 41) and morbidly obese (MO, n = 21). Two-day multi-pass 24 h recalls were conducted. The GMA was measured by multichannel electrogastrography (EGG) with water-load (WL) testing. Resting metabolic rate (RMR) and percentages of substrate utilization were measured by indirect calorimetry. In the HW, protein intake was directly correlated with average dominant frequency (ADF) and with WL volume, while in obese participants and the MO subgroup, WL volume correlated with carbohydrate intake. In participants with obesity, ADF was positively correlated with fiber intake. In participants with obesity and the OB subgroup, RMR was positively correlated with water-load volume (r = 0.39 and 0.37, p < 0.05). The ADF showed negative correlations with percent of fat utilization and positive correlations with percent of CHO utilization in non-obese groups. However, protein utilization showed inverse correlation in all obese groups. In conclusion, these distinctive associations suggest that certain dietary compositions and dieting regimens impact GMA patterns