Ali Cinar

• Ph.D. in Chemical Engineering
• Illinois Institute of Technology

Chronic kidney disease (CKD) is a complication of diabetes that affects circulating drug concentrations and elimination of drugs from the body. Multiple drugs may be prescribed for treatment of diabetes and co-morbidities, and CKD complicates the pharmacotherapy selection and dosing regimen. Characterizing variations in renal drug clearance using m...

Acute Psychological Stress Detection Using Abstract: Acute psychological stress (APS) is a complex and multifactorial phenomenon that affects metabolism, necessitating real-time detection and interventions to mitigate its effects on glycemia in people with type 1 diabetes. This study investigates the detection of APS using physiological variables m...

Multivariable Automated Insulin Delivery (AID) Systems: Evolution and Challenges

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. Whil...

Background Hybrid closed-loop control of glucose levels in people with type 1 diabetes mellitus (T1D) is limited by the requirements on users to manually announce physical activity (PA) and meals to the artificial pancreas system. Multivariable automated insulin delivery (mvAID) systems that can handle unannounced PAs and meals without any manual a...

The artificial pancreas (AP) systems based on model predictive control (MPC) are expected to provide effective and safe regulation of blood glucose concentration (BGC) for people with type 1 diabetes. However, the development of AP systems based on MPC is challenged by the high computational burden of MPC facing limited computational resources and...

Impaired glucagon secretion is a major component of glucose intolerance in type 2 diabetes mellitus (T2D). Glucagon secretion exhibits heterogenous patterns in individuals and across glucose tolerance diagnoses. Characterization of the range of glucagon secretion patterns can help clinicians personalize diabetes care based on glucagon characteristi...

Wearable sensor data can be integrated and interpreted to improve the treatment of chronic conditions, such as diabetes, by enabling adjustments in treatment decisions based on physical activity and psychological stress assessments. The challenges in using biological analytes to frequently detect physical activity (PA) and acute psychological stres...

This work considers the problem of adaptive prior-informed model predictive control (MPC) formulations that explicitly incorporate prior knowledge in the model development and is robust to missing data in the output measurements. The proposed prediction model is based on a latent variables model to extract glycemic dynamics from highly-correlated d...

Introduction: Diabetic kidney disease (DKD) affects 33% of adults with diabetes mellitus (DM) and contributes to wide variability in plasma glucose (PG) levels. Sodium-glucose cotransporter-2 inhibitor (SGLT2I) treatment has been shown to improve renal outcomes in people with DKD while improving PG levels. Identifying the extent of DKD and SGLT2I e...

Renal glucose handling is an important component of blood glucose maintenance. In type 2 diabetes (T2D), the renal threshold for glucose (𝑅𝑇𝐺) and transport maximum for glucose (𝑇𝑀𝐺) are both elevated, leading to an increase in glucose reabsorption (𝑅𝑅𝑒𝑎𝑏). This is associated with an increase in estimated average glucose level (eAG). SGLT2 in...

Detection and classification of acute psychological stress (APS) and physical activity (PA) in daily lives of people with chronic diseases can provide precision medicine for the treatment of chronic conditions such as diabetes. This study investigates the classification of different types of APS and PA, along with their concurrent occurrences, usin...

Background and Objective : The glucose response to physical activity for a person with type 1 diabetes (T1D) depends upon the intensity and duration of the physical activity, plasma insulin concentrations, and the individual physical fitness level. To accurately model the glycemic response to physical activity, these factors must be considered. Me...

Artificial intelligence (AI) algorithms can provide actionable insights for clinical decision-making and managing chronic diseases. The treatment and management of complex chronic diseases, such as diabetes, stands to benefit from novel AI algorithms analyzing the frequent real-time streaming data and the occasional medical diagnostics and laborato...

Background Predicting carbohydrate intake and physical activity in people with diabetes is crucial for improving blood glucose concentration regulation. Patterns of individual behavior can be detected from historical free-living data to predict meal and exercise times. Data collected in free-living may have missing values and forgotten manual entri...

Introduction: Diagnosis of T2DM necessitates clinical tests that are time-consuming and expensive. Machine learning (ML) techniques can accelerate the diagnosis and classification of T2DM and allow clinicians to personalize treatments based on blood glucose concentrations (BGC) , physical fitness (PF) , and diabetes distress patterns observed in da...

Objective: The interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) system...

Athletic competitions and the associated psychological stress are a challenge for people with type 1 diabetes (T1D). This study aims to understand the influence of anticipatory and early race competition stress on blood glucose concentrations and to identify personality, demographic, or behavioral traits indicative in the scope of the impact. Ten r...

This paper demonstrates the use of model predictive control (MPC) formulations for uncertain time-varying biopharmaceutical and biomedical systems implemented using measured data without prior knowledge of an accurate model. Furthermore, we demonstrate how prior knowledge can be incorporated in the identification of the model either through constra...

Background: Adaptive model predictive control (MPC) algorithms that recursively update the glucose prediction model are shown to be promising in the development of fully automated multivariable artificial pancreas systems. However, the recursively updated glycemic prediction models do not explicitly consider prior knowledge in the identification o...

Many data-driven modeling techniques identify locally valid, linear representations of time-varying or nonlinear systems, and thus the model parameters must be adaptively updated as the operating conditions of the system vary, though the model identification typically does not consider prior knowledge. In this work, we propose a new regularized par...

Sodium-glucose cotransporter-2 inhibitors (SGLT2Is) are a class of medications prescribed for managing type 2 diabetes (T2D). SGLT2Is lower blood glucose concentration (BGC) by increasing urinary glucose excretion (UGE). This insulin-independent mechanism is unique and provides benefits to both renal and cardiovascular health. SGLT2Is are typically...

Aims/hypothesis Suboptimal subjective sleep quality is very common in adults with type 1 diabetes. Reducing glycaemic variability is a key objective in this population. To date, no prior studies have examined the associations between objectively measured sleep quality and overnight glycaemic variability in adults with type 1 diabetes. We aimed to t...

Mobile health technology is gaining popularity as an effective way to improve individual health by aiding personalized decision making. Health, diet, and lifestyle data collected by smartphones and wearable devices may be interpreted by computational techniques to extract useful information. Hyperglycemia is a state of high blood glucose common in...

Clinical practice guidelines are a critical medium for the standardization of practices within the overall medical community. However, several studies have shown that, in general, there is a significant delay in the adoption of recommendations in such guidelines. Surveys have identified multiple barriers, including clinical inertia, organizational...

Objective: Continuous glucose monitoring (CGM) enables prediction of the future glucose concentration (GC) trajectory for making informed diabetes management decisions. The glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to...

Automated drug delivery can reduce many repetitive manual tasks and human error in the treatment of chronic diseases. The automation can range from duplication of the procedures conducted by the human user to leveraging machine learning (ML) and advanced control techniques to provide a comprehensive automated system that can personalize the treatme...

A model predictive control (MPC) system based on latent variables (LV) model generated by using partial least squares (PLS) method is developed. The difference in the performance of MPCs that use recursively updated LV models based on autoregressive time series modeling (with exogenous inputs - ARX) and PLS is studied. The effect of signal noise on...

A model predictive control (MPC) formulation for a mammalian cell fed-batch bioreactor processes is developed. A nonlinear fundamental model for the bioreactor is used to generate a database of historical runs comprising of the measurement variables and the manipulated input feed flow rate to the bioreactor. The database is used with subspace ident...

Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of...

An adaptive-learning model predictive control (AL-MPC) framework is proposed for incorporating disturbance prediction, model uncertainty quantification, pattern learning, and recursive subspace identification for use in controlling complex dynamic systems with periodically recurring large random disturbances. The AL-MPC integrates online learning f...

Background and objective: In this work, we address the problem of detecting and discriminating acute psychological stress (APS) in the presence of concurrent physical activity (PA) using wristband biosignals. We focused on signals available from wearable devices that can be worn in daily life because the ultimate objective of this work is to provi...

This paper addresses the problem of heart rate (HR) monitoring from photoplethysmography(PPG) sensors, where artifacts caused by body movements drastically affect the quality of the measurement signal. The PPG signal is windowed into consecutive segments, and for each time-windows, a Butterworth bandpass filter is utilized to attenuate high-frequen...

In this work, an adaptive-learning model predictive control (AL-MPC) framework that integrates disturbance forecasting, uncertainty quantification, learning, and recursive subspace identification is developed. The proposed technique can be used for continuous systems affected by repetitive disturbances with unknown periods. The AL-MPC Integrates on...

Vascularization is critical for engineering mineralized tissues. It has been previously shown that biomaterials containing preformed endothelial networks anastomose to host vasculature following implantation. However, the networks alone may not increase regeneration. In addition, the a clinically applicable source of cells for vascularization are n...

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different phy...

Nineteen people with T1D (10 male, 9 female. Age: 31 +/- 9 yrs, A1c: 6.6 +/- 0.8 and duration of diabetes: 19 +/- 11 years) had their sleep monitored with an EEG-based device (Zmachine Insight+) for a total of 154 nights. Their blood glucose levels (BGL) were measured with a CGM. From the CGM data, the participant’s average BGL, standard deviation...

People with type 1 diabetes (T1D) have personal strategies for maintaining euglycemia while exercising which generally serves them well during training. Their strategy often yields wildly differing results during a competition. The influence of competition stress (notably, epinephrine causing prompt elevations in glucose) and high-intensity and/or...

The accuracy of blood glucose concentration (BGC) estimation affects insulin dosing decisions. Wristband biosignals (Empatica E4) are used to estimate physical activity type and intensity, and psychological stress to enhance BGC prediction accuracy. Three cases are compared: (1) BGC prediction by using only continuous glucose monitor (CGM) data, wh...

Background: Multivariable artificial pancreas (mAP) systems are developed to supplement the continuous glucose monitoring (CGM) data with physiological measurements from wearable devices and automatically incorporate physical activity information in proactive insulin dose decisions. The wearable devices incorporated into mAP systems collect data in...

People with type 1 diabetes (T1D) have an increased risk for autonomic dysfunction and those with poorly controlled blood glucose levels have an increased risk for cardiovascular disease. The purpose of this study was to determine heart rate variability (HRV) and glucose variability (GV) parameters in less than optimally controlled patients (Group...

The effects of acute psychological stress, heart rate variability (HRV), an indicator of autonomic function and glycemic variability (GV) have not been extensively researched in type 1 diabetes (T1D). The aim of this study is to explore the effect of acute psychosocial stress on HRV and GV in people with T1D. Methods: Eleven participants with T1D (...

Objective: Evidence suggests that patients with type 1 diabetes (T1D) performing aerobic exercise with their insulin pump connected (pump on) vs pump disconnected (pump off) have an increased risk of hypoglycemia. It has not been examined whether this risk remains during high-intensity exercise. This study compared the effects of pump on (50% basa...

Closed-loop control of glucose concentrations in type 1 diabetes mellitus has progressed significantly over the last decade. Automated insulin delivery systems, or artificial pancreas systems, have advanced to incorporate model-based predictive controllers developed on the basis of adaptive and personalized glucose-insulin models. This chapter revi...

A personalized multivariable, multimodel artificial pancreas (PMM-AP) system is developed to automate and personalize insulin treatment of type 1 diabetes. The proposed PMM-AP is a fully automated insulin delivery system that works with no meal and physical activity announcements. An adaptive-personalized plasma insulin concentration (PIC) estimato...

In this work, an adaptive-learning model predictive control (AL-MPC) framework that integrates disturbance forecasting, uncertainty quantification, learning, and recursive subspace identification is developed. The proposed technique can be used for continuous systems affected by repetitive disturbances with unknown periods. The AL-MPC integrates on...

This paper addresses the problem of heart rate (HR) monitoring from photo-plethysmography(PPG) sensors, where artifacts caused by body movements drastically affect the quality of the measurement signal. The PPG signal is windowed into consecutive segments, and for each time-windows, a Butterworth bandpass filter is utilized to attenuate high-freque...

This paper presents the development of virtual patients to enable the simulation evaluation and assessment of multivariable control algorithms for biomedical systems. The virtual patients are generated by fitting the parameters of the models to clinical experimental data, followed by the estimation of the multivariate distribution of the actual pat...

Linear empirical dynamic models have been widely used for blood glucose (BG) prediction and risks prevention in people with type 1 diabetes. More accurate BG prediction models with longer prediction horizon (PH) are desirable to enable warnings to patients about imminent BG changes with enough time to take corrective actions. In this study, a BG pr...

Insufficient vascularization limits the volume and complexity of biomaterials-based tissue engineering approaches. The formation of new blood vessels (neovascularization) is regulated by a complex interplay of cellular interactions with biochemical and biophysical signals provided by the extracellular matrix (ECM) which necessitates the development...

Background Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time. Methods A controller performance assessment an...

An artificial pancreas system is implemented as a mobile application which connects a glucose sensor, an insulin pump and wearable physical activity sensors. It automatically delivers the optimal insulin amounts, based on a multivariable control algorithm. The algorithm, previously tested on a laptop, is to be hosted on a smartphone. This requires...

An adaptive and personalized multivariable artificial pancreas (mAP) system using plasma insulin estimates is proposed to efficiently accommodate major disturbances to the blood glucose concentration, such as meal and physical activity. Accurate adaptive glycemic models are developed through a recursive subspace identification technique with wearab...

IN BRIEF Automated insulin delivery (AID; also known as artificial pancreas) has improved the regulation of blood glucose concentrations, reduced the frequency of hyperglycemic and hypoglycemic episodes, and improved the quality of life of people with diabetes and their families. Three different types of algorithms-proportional-integral-derivative...

Detection of acute psychological stress (APS) is challenging as limited noninvasive measurements are available from wearable devices. In this work, the readily obtained photoplethysmography (PPG) signal is used to detect the presence of APS. The measured PPG data is first filtered and denoised to enhance the signal. The enhanced PPG is then use to...

Computing the appropriate amount of rescue carbohydrate (CHO) suggestion during physical activity for people with diabetes remains challenging. A fuzzy-logic-based rescue CHO suggestion algorithm is proposed that uses physical activity information and glycemic indexes to suggest an appropriate CHO amount to avert low glucose concentrations. The pro...

Diabetes treatment requires accurate predictions of future glucose concentration, which is challenging because glucose variations are significantly affected by the type, intensity , and duration of the physical activity. In this work, we propose the classification of the physical state (PS) and the estimation of energy expenditure (EE) using a wris...

Diabetes treatment requires accurate predictions of future glucose values, which is challenging because the glucose variations are significantly affected by the type, intensity, and nature of the physical activity [1]. In this work, we propose the classification of the physical state (PS) and the estimation of energy expenditure (EE) using a wrist-...

Acute psychological stress (APS) induces neuro endocrine and metabolic responses that lead to increased risk of glucose dysregulation and cardiovascular complications in people with diabetes [1]. An APS detection and assessment algorithm is developed in this work to quantify the levels of APS using physiological measurements from a wearable activit...

To develop a new algorithm for AP systems that suggests the appropriate amount of CHO to people with T1D during exercise with different speeds and intensities to reduce the risk of hypoglycemia and avoid inadvertent hyperglycemia while minimizing the total CHO consumed.

Computing the appropriate amount of rescue carbohydrate (CHO) suggestion during physical activity for people with diabetes remains challenging. A fuzzy-logic-based rescue CHO suggestion algorithm is proposed that uses physical activity information and glycemic indexes to suggest an appropriate CHO amount to avert low glucose concentrations. The pro...

Detection of acute psychological stress (APS) is challenging as limited noninvasive measurements are available from wearable devices and the data generated needs to be evaluated. In this work, photoplethysmography (PPG) signals are used to detect the presence of APS. The PPG data is first filtered and denoised to enhance the signal. The enhanced PP...

An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a rec...

Introduction Disruptions in sleep quality impair daytime glucose metabolism. Normal sleep consists of non-REM sleep (stages N1, N2, and slow wave sleep [SWS]) and rapid-eye-movement (REM) sleep, which have distinct physiological characteristics. Here, we investigate how different sleep stages affect nocturnal glucose levels. Methods Sixteen health...

A controller performance assessment algorithm is developed to analyze the closed-loop behavior and modify the parameters of a control system employed in automated insulin delivery. To this end, various performance indices are dened to quantitatively evaluate the controller efficacy in real-time. The controller assessment and modication module also...

A fuzzy clustering-based local modeling framework is presented to take full advantage of seasonality for improved glucose prediction.

Background: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the...

Uncertain delays in the absorption, distribution, and utilization of insulin can cause model inconsistencies and poor glycemic control performance. To address this problem, an adaptive system identification approach able to handle the variable delays in the insulin pharmacokinetics is integrated in this work with a predictive control formulation to...

Erroneous information from sensors affect process monitoring and control. An algorithm with multiple model identification methods will improve the sensitivity and accuracy of sensor fault detection and data reconciliation (SFD&DR). A novel SFD&DR algorithm with four types of models including outlier robust Kalman filter, locally weighted partial le...

Background: Exercise challenges people with type 1 diabetes in controlling their glucose concentration (GC). A multivariable adaptive artificial pancreas (MAAP) may lessen the burden. Methods: The MAAP operates without any user input and computes insulin based on continuous glucose monitor and physical activity signals. To analyze performance, 1...

Regulatory T cells (Tregs) have an important role in self-tolerance. Understanding the functions of Tregs is important for preventing or slowing the progress of Type 1 Diabetes. We use a two-dimensional (2D) agent-based model to simulate immune response in mice and test the effects of Tregs in tissue protection. We compared the immune response with...

An adaptive model predictive control (MPC) formulation is proposed in this work for optimal insulin dosing decisions in artificial pancreas (AP) systems. To this end, a recursive subspace-based system identification approach is used to characterize the transient dynamics of biological systems, specifically the metabolic processes involved in diabet...

Background: Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging. Method: An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced...

Continuous glucose monitoring (CGM) sensors are a critical component of artificial pancreas (AP) systems that enable individuals with type 1 diabetes to achieve tighter blood glucose control. CGM sensor signals are often afflicted by a variety of anomalies, such as biases, drifts, random noises, and pressure-induced sensor attenuations. To improve...

Sensor errors limit the performance of a supervision and control system. Sensor accuracy can be affected by many factors such as extreme working conditions, sensor deterioration and interferences from other devices. It may be difficult to distinguish sensor errors and real dynamic changes in a system. A hybrid online multi-sensor error detection an...