The Effects of Environmental Factors on General Human Health: A Scoping Review.

The external environment constantly influences human health through many factors, including air quality, access to green spaces, exposure to pollutants, and climate change. Contamination poses a substantial threat to human well-being; conversely, environmental factors also positively impact health. The purpose of this study is to provide a comprehensive review of the complex relationship between various environmental factors and human health.

An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems.

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e.

Generative deep learning for the development of a type 1 diabetes simulator.

Background: Type 1 diabetes (T1D) simulators, crucial for advancing diabetes treatments, often fall short of capturing the entire complexity of the glucose-insulin system due to the imprecise approximation of the physiological models. This study introduces a simulation approach employing a conditional deep generative model. The aim is to overcome the limitations of existing T1D simulators by synthesizing virtual patients that more accurately represent the entire glucose-insulin system physiology.

An ensemble machine learning approach for the detection of unannounced meals to enhance postprandial glucose control.

Hybrid automated insulin delivery systems enhance postprandial glucose control in type 1 diabetes, however, meal announcements are burdensome. To overcome this, we propose a machine learning-based automated meal detection approach; METHODS:: A heterogeneous ensemble method combining an artificial neural network, random forest, and logistic regression was employed. Trained and tested on data from two in-silico cohorts comprising 20 and 47 patients.

Optimizing Noninvasive Vagus Nerve Stimulation for Systemic Lupus Erythematosus: Protocol for a Multicenter Randomized Controlled Trial.

Background: Systemic lupus erythematosus is a chronic, multisystem, inflammatory disease of autoimmune etiology occurring predominantly in women. A major hurdle to the diagnosis, treatment, and therapeutic advancement of this disease is its heterogeneous nature, which presents as a wide range of symptoms such as fatigue, fever, musculoskeletal involvement, neuropsychiatric disorders, and cardiovascular involvement with varying severity. The current therapeutic approach to this disease includes the administration of immunomodulatory drugs that may produce unfavorable secondary effects.

Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections.

Background And Objectives: Recent advances in Automated Insulin Delivery systems have been shown to dramatically improve glycaemic control and reduce the risk of hypoglycemia in people with type 1 diabetes. However, they are complex systems that require specific training and are not affordable for most. Attempts to reduce the gap with closed-loop therapies using advanced dosing advisors have so far failed, mainly because they require too much human intervention.

Bolus Insulin calculation without meal information. A reinforcement learning approach.

In continuous subcutaneous insulin infusion and multiple daily injections, insulin boluses are usually calculated based on patient-specific parameters, such as carbohydrates-to-insulin ratio (CR), insulin sensitivity-based correction factor (CF), and the estimation of the carbohydrates (CHO) to be ingested. This study aimed to calculate insulin boluses without CR, CF, and CHO content, thereby eliminating the errors caused by misestimating CHO and alleviating the management burden on the patient. A Q-learning-based reinforcement learning algorithm (RL) was developed to optimise bolus insulin doses for in-silico type 1 diabetic patients.

Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models.

In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes.

A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin.

Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied.

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges.

(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death.

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor.

(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks.

Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques.

Background: Diabetic patients treated with intensive insulin therapies require a tight glycemic control and may benefit from advanced tools to predict blood glucose (BG) concentration levels and hypo/hyperglycemia events. Prediction systems using machine learning techniques have mainly focused on applications for sensor augmented pump (SAP) therapy. In contrast, insulin bolus calculators that rely on BG prediction for multiple daily insulin (MDI) injections for patients under self-monitoring blood glucose (SMBG) are scarce because of insufficient data sources and limited prediction capability of forecasting models.

Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning.

Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia.

Risk-based postprandial hypoglycemia forecasting using supervised learning.

Background: Predicting insulin-induced postprandial hypoglycemic events is critical for the safety of type 1 diabetes patients because an early warning of hypoglycemia facilitates correction of the insulin bolus before its administration. The postprandial hypoglycemic event counts can be lowered by reducing the size of the bolus based on a reliable prediction but at the cost of increasing the average blood glucose.

Methods: We developed a method for predicting postprandial hypoglycemia using machine learning techniques personalized to each patient.

Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study.

Background: Technology has long been used to carry out self-management as well as to improve adherence to treatment in people with diabetes. However, most technology-based apps do not meet the basic requirements for engaging patients.

Objective: This study aimed to evaluate the effect of use frequency of a diabetes management app on glycemic control.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.

Background: Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.

Objective: The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.

Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models.

The large patient variability in human physiology and the effects of variables such as exercise or meals challenge current prediction modeling techniques. Physiological models are very precise but they are typically complex and specific physiological knowledge is required. In contrast, data-based models allow the incorporation of additional inputs and accurately capture the relationship between these inputs and the outcome, but at the cost of losing the physiological meaning of the model.

Adaptive Workflows for Diabetes Management: Self-Management Assistant and Remote Treatment for Diabetes.

Diabetes self-management is a crucial element for all people with diabetes and those at risk for developing the disease. Diabetic patients should be empowered to increase their self-management skills in order to prevent or delay the complications of diabetes. This work presents the proposal and first development stages of a smartphone application focused on the empowerment of the patients with diabetes.

Profiling intra-patient type I diabetes behaviors.

Background: The large intra-patient variability in type 1 diabetic patients dramatically reduces the ability to achieve adequate blood glucose control. A novel methodology to identify different blood glucose dynamics profiles will allow therapies to be more accurate and tailored according to patient's conditions and to the situations faced by patients (exercise, week-ends, holidays, menstruation, etc).

Materials And Methods: A clustering methodology based on the normalized compression distance is applied to identify different profiles for diabetic patients.

Ischemic preconditioning and spinal cord function monitoring in the descending thoracic aorta approach.

Objectives: To evaluate the effectiveness of acute ischemic preconditioning (IP), based on somatosensory evoked potentials (SSEP) monitoring, as a method of spinal cord protection and to asses SSEP importance in spinal cord neuromonitoring.

Methods: Twenty-eight dogs were submitted to spinal cord ischemic injury attained by descending thoracic aorta cross-clamping. In the C45 group, the aortic cross-clamping time was 45 min (n=7); in the IP45 group, the dogs were submitted to IP before the aortic cross-clamping for 45 min (n=7).

Immediate ischemic preconditioning based on somatosensory evoked potentials seems to prevent spinal cord injury following descending thoracic aorta cross-clamping.

Objective: Delayed ischemic preconditioning has demonstrated neuroprotective effects in spinal cord ischemia. We investigated the effects of immediate ischemic preconditioning based on somatosensory evoked potentials monitoring in a model of spinal cord injury due descending thoracic aorta occlusion in dogs.

Methods: Twenty-one dogs were submitted to spinal cord ischemia induced by descending thoracic aorta cross-clamping for 45 min.

Endomyocardial fibrosis in infancy.

The patient was a 4-month-old infant, who underwent persistent ductus arteriosus interruption with titanium clips at the age of 13 days and, since the age of 2 months, had crises of hypoxia and hypertonicity. After clinical investigation, the presence of pulmonary hypertension was confirmed and left ventricular inflow tract obstruction was suspected. The patient underwent surgical treatment at the age of 4 months, during which right and left ventricular endocardial fibrosis was identified.