Background Type-1 diabetes is a condition caused by the lack of insulin hormone, which leads to an excessive increase in blood glucose level. The glucose kinetics process is difficult to control due to its complex and nonlinear nature and with state variables that are difficult to measure. Methods This paper proposes a method for automatically calculating the basal and bolus insulin doses for patients with type-1 diabetes using reinforcement learning with feedforward controller. The algorithm is designed to keep the blood glucose stable and directly compensate for the external events such as food intake. Its performance was assessed using simulation on a blood glucose model. The usage of the Kalman filter with the controller was demonstrated to estimate unmeasurable state variables. Results Comparison simulations between the proposed controller with the optimal reinforcement learning and the proportional-integral-derivative controller show that the proposed methodology has the best performance in regulating the fluctuation of the blood glucose. The proposed controller also improved the blood glucose responses and prevented hypoglycemia condition. Simulation of the control system in different uncertain conditions provided insights on how the inaccuracies of carbohydrate counting and meal-time reporting affect the performance of the control system. Conclusion The proposed controller is an effective tool for reducing postmeal blood glucose rise and for countering the effects of external known events such as meal intake and maintaining blood glucose at a healthy level under uncertainties.

Activation of the hypothalamic-pituitary-adrenal (HPA) axis is important for maintenance of homeostasis during stress. Recent studies have shown a connection between the HPA axis and adipose tissue. The present study investigated the effect of acute heterotypic stress on plasma levels of adrenocorticotropic hormone (ACTH), corticosterone (CORT), leptin, and ghrelin in adult male rats with respect to neonatal maternal social and physical stressors. Thirty rat mothers and sixty of their male progeny were used. Pups were divided into three groups: unstressed control (C), stressed by maternal social stressor (S), stressed by maternal social and physical stressors (SW). Levels of hormones were measured in adult male progeny following an acute swimming stress (10 min) or no stress. ELISA immunoassay was used to measured hormones. The ACTH and CORT levels were significantly increased in all groups of adult progeny after acute stress; however, CORT levels were significantly lower in both neonatally stressed groups compared to controls. After acute stress, plasma leptin levels were decreased in the C and SW groups but increased in the S group. The data suggest that long-term neonatal stressors lead to lower sensitivity of ACTH receptors in the adrenal cortex, which could be a sign of stress adaptation in adulthood. Acute stress in adult male rats changes plasma levels of leptin differently relative to social or physical neonatal stressors.

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Background Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. Objective The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. Methods We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). Results Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. Conclusions We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

BACKGROUND Type 1 diabetes mellitus is a blood glucose (BG) metabolic disorder, which is caused by deficiencies of insulin secretion from pancreatic cells. People with type 1 diabetes often experience prolonged hyperglycemia episodes upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings to what extent the key parameters of BG dynamics are affected during infection incidences to support the effort towards developing a digital infectious disease detection system. OBJECTIVE The objective of the study is to retrospectively analyze the effect of infection incidences and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm. Moreover, to provide a general framework regarding how a digital infectious disease detection system can be designed using self-recorded data from people with type 1 diabetes as a secondary source of information. METHODS We retrospectively analyzed high precision self-recorded data of 10 patient years captured within the longitudinal records of 3 people with type 1 diabetes. Getting such a rich and big dataset from large number of participants are extremely expensive and difficult to acquire, if not impossible. The participants were 2 males and 1 female with an average age of 34  13.2 years. The dataset incorporates BG levels, insulin, carbohydrate and self-reported events of infections. We investigated the temporal evolution and probability distribution of BG levels, injected insulin, carbohydrate intake, and insulin to carbohydrate ratio within a specified timeframe (weekly, daily and hourly). All the experiments were carried out using MATLAB® 2018a (Mathworks, Inc, Natwick, MA). RESULTS Our analysis demonstrated that upon infection incidences, there is a dramatic shift in the operating point of the individual BG dynamics in all the timeframes (weekly, daily and hourly), which clearly violate the usual norm of BG dynamics. During regular/normal situations, BG levels usually lower when there is a significant increase in insulin injection and reduction in carbohydrate consumption. However, in all of the individual’s infection cases as opposed to the regular/normal days, there were prolonged period with elevated BG levels despite injecting higher insulin and consuming less carbohydrate. For instance, in all the infection week on average, BG levels were elevated by 6.1% and 16%, insulin (bolus) were increased by 42% and 39.3%, carbohydrate consumption were reduced by 19% and 28.1%, and insulin to carbohydrate ratio were raised by 108.7% as compared to pre-infection and post-infection week respectively. CONCLUSIONS Despite the fact that patients increasingly gather data about themselves, there are no solid findings to what extent the key parameters of BG dynamics are affected during infection incidences to support the effort towards developing a digital infectious disease detection system. We presented the effect of infection incidences on key parameters of the BG dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The result demonstrated that as compared to the regular/normal days, infection incidence substantially alters the norm of BG dynamics, which are quite significant changes that could possibly be detected through a personalized modelling, e.g. prediction models and anomalies detection algorithms. Generally, we foresee that these findings can benefit the efforts towards building the next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.