Search

We have attempted to estimate blood glucose levels based on facial images measured in the near-infrared band, which is highly biopermeable, to establish a remote minimally invasive blood glucose measurement method. We measured facial images in the near-infrared wavelength range of 760–1650 nm, and constructed a general model for blood glucose level estimation by linear regression using the weights of spatial features of the measured facial images as explanatory variables. The results showed that the accuracy values of blood glucose estimation in the generalization performance evaluation were 43.02 mg/dL for NIR-I (760–1100 nm) and 43.61 mg/dL for NIR-II (1050–1650 nm) in the RMSE of the general model. Since biological information is nonlinear, it is necessary to explore suitable modeling methods for blood glucose estimation, including not only linear regression but also nonlinear regression. The purpose of this study is to explore suitable regression methods among linear and nonlinear regression methods to construct a blood glucose estimation model based on facial images with wavelengths from 760 to 1650 nm. The results showed that model using Random Forest had the best estimation accuracy with an RMSE of 36.02 mg/dL in NIR-I and the MR model had the best estimation accuracy with RMSE of 36.70 mg/dL in NIR-II under the current number of subjects and measurement data points. The independent components selected for the model have spatial features considered to be simply individual differences that are not related to blood glucose variation. [ABSTRACT FROM AUTHOR]

Simple Summary: Although some of the novel systemic treatments, especially the group of tyrosine kinase inhibitors, have shown a durable central nervous system response, ionizing radiation remains the mainstay in the management of brain metastases (BM). Recent technological advancements have enabled the replacement of whole-brain radiotherapy with localized stereotactic radiotherapy (SRT) for treating up to 10 BM, either as a primary or combined treatment, reducing neurotoxicity and improving local control (LC). The delivered target dose and patient selection play a crucial role in enhancing treatment efficacy. However, there is still limited evidence supporting which factors most affect LC and which patients derive the greatest benefit from SRT. This retrospective single-institutional study evaluated treatment outcomes in a heterogeneous patient population treated with Linac-based SRT, with the aim of identifying potential dosimetric and clinical prognostic factors to better inform the decision-making process. Background/Objectives: To report on predictive factors in Linac-based SRT for single and multiple BM. Methods: Consecutive patients receiving either one or three fractions of single-isocenter coplanar VMAT SRT were retrospectively included. The GTV-PTV margin was 1–2 mm. The delivered target dose was estimated by recalculating the original plans on roto-translated CT according to errors recorded by post-treatment CBCT. The Kaplan–Meier method estimated local progression-free survival (LPFS), intracranial progression-free survival (IPFS), and overall survival (OS). Log-rank and Wilcoxon–Mann–Whitney tests evaluated inter-group differences, whereas Cox regression analysis assessed prognostic factors. Results: Fifty females and fifty males, with a median age of 69 years, received 107 SRTs. A total of 213 BM (range, 1–10 per treatment) with a median volume of 0.22 cc were irradiated with a median minimum BED of 59.5 Gy. The median delivered GTV D95 reduction was −0.3%. The median follow-up was 11 months. Nineteen LP events and a 1-year LC rate of 90.1% were observed. The GTV coverage did not correlate with LC, while the GTV volume was a risk factor for LP, with the 1-year rate dropping to 73% for volumes ≥ 0.88 cc. The median LPFS, IPFS, and OS were 6, 5, and 7 months, respectively. Multivariate analysis showed that patients with melanoma histology and those receiving a second or subsequent systemic therapy line had the worst outcomes, whereas patients with adenocarcinoma histology and mutations showed better results. Conclusions: The accuracy and efficacy of the Linac-based SRT approach for BM were confirmed, but the dose distribution alone failed to predict the treatment response, suggesting that other factors must be considered to maximize SRT outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Distinguishing radionecrosis from local recurrence after cranial radiotherapy remains a challenging diagnostic dilemma due to their overlapping clinical manifestations. Although new MRI techniques and metabolic imaging have been developed, diagnostic performance remains variable. Recently, our institution acquired a PET-MRI, enabling us to investigate the diagnostic performance of multiparametric MRI when used in dual-phase 18F-FDG PET-mpMRI to enhance diagnostic accuracy. Brain metastases or primary brain tumours had poor prognosis until the use of high dose radiotherapy. However, radionecrosis is a complex challenge in the post-radiotherapy management of these patients due to the difficulty of distinguishing this complication from local tumour recurrence. MRI alone has a variable specificity and sensibility, as does PET-CT imaging. We aimed to investigate the diagnostic performance of dual-phase 18F-FDG PET-mpMRI to distinguish cerebral radionecrosis from local tumour recurrence after cranial radiotherapy. A retrospective analysis was conducted between May 2021 and September 2022. Inclusion criteria encompassed patients with inconclusive MRI findings post-radiotherapy and history of cerebral radiotherapy for primary or metastatic brain lesions. Lesions are assessed qualitatively and semi-quantitatively. The gold standard to assess radionecrosis was histopathology or a composite criterion at three months. The study evaluated 24 lesions in 23 patients. Qualitative analysis yielded 85.7% sensitivity and 75% specificity. Semi-quantitative analysis, based on contralateral background noise, achieved 100% sensitivity and 50% specificity. Moreover, using contralateral frontal lobe background noise resulted in higher performances with 92% sensitivity and 63% specificity. Stratification by lesion type demonstrated 100% sensitivity and specificity rates for metastatic lesions. The diagnostic performance of dual-phase 18F-FDG PET-mpMRI shows promising results for metastatic lesions. [ABSTRACT FROM AUTHOR]

Introduction: Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components. Method: A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy. Results: The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31–60 min in 34%, 61–90 min in 11%, 91–120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%). Conclusion: The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance. [ABSTRACT FROM AUTHOR]

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. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70–180 mg/dL was 71.2 % and 76.2 % , < 70 mg/dL was 0.9 % and 0.1 % , and > 180 mg/dL was 26.7 % and 21.1 % , respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future. [ABSTRACT FROM AUTHOR]

Along with the developing technology, obesity and diabetes are increasing rapidly among people. The identification of diabetes diseases, modeling, predicting their behavior, conducting simulations, studying control and treatment methods using mathematical methods has become of great importance. In this paper, we have obtained numerical solutions by considering the glucose-insulin fractional model. This model consists of three compartments: the blood glucose concentration (G), the blood insulin concentration (I) and the ready-to-absorb glucose concentration (D) in the small intestine. The fractional derivative is used in the sense of Caputo. By performing mathematical analyzes for the Glucose-Insulin fractional mathematical model, numerical results were obtained with the help of the Euler method and graphs were drawn. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The majority of hybrid closed-loop systems still require carbohydrate counting (CC) but the evidence for its justification remains limited. Here, we evaluated glucose control with simplified meal announcement (SMA) vs CC in youth and young adults with type 1 diabetes using the mylife CamAPS FX system., Methods: We conducted a two-centre, randomised crossover, non-inferiority trial in two University Hospitals in Switzerland in 46 participants (aged 12-20 years) with type 1 diabetes using multiple daily injections (n=35), sensor-augmented pump (n=4) or hybrid closed-loop (n=7) therapy before enrolment. Participants underwent two 3 month periods with the mylife CamAPS FX system (YpsoPump, Dexcom G6) to compare SMA (individualised carbohydrate meal sizes) with CC, in a randomly assigned order using computer-generated sequences. The primary endpoint was the proportion of time glucose was in target range (3.9-10.0 mmol/l) with a non-inferiority margin of 5 percentage points. Secondary endpoints were other sensor glucose and insulin metrics, usability and safety endpoints., Results: Forty-three participants (18 women and girls) completed the trial. In the intention-to-treat analysis, time in range (mean±SD) was 69.9±12.4% with SMA and 70.7±13.0% with CC (estimated mean difference -0.6 percentage points [95% CI -2.4, 1.1], demonstrating non-inferiority). Time <3.9 mmol/l (median [IQR] 1.8 [1.2-2.2]% vs 1.9 [1.6-2.5]%) and >10.0 mmol/l (28.2±12.6% vs 27.2±13.4%) was similar between periods. Total daily insulin dose was higher with SMA (54.0±14.7 U vs 51.7±12.1 U, p=0.037). Three participants experienced serious adverse events, none of which were intervention-related., Conclusions/interpretation: Glucose control using the CamAPS FX algorithm with SMA was non-inferior to its use with CC in youth and young adults with type 1 diabetes., Trial Registration: ClinicalTrials.gov NCT05481034., Funding: The study was supported by the Swiss Diabetes Foundation and by a YTCR grant from the Bangerter-Rhyner Foundation and the Swiss Academy of Medical Sciences. Dexcom and Ypsomed provided product support., Competing Interests: Acknowledgements We are grateful to study volunteers for their participation. We acknowledge support by L. Cavalli, E. Leu, C. Gisler (UDEM, Inselspital, Bern, Switzerland), M. Vajner and S. Rocha (Division of Paediatric Endocrinology and Diabetology, University Children's Hospital, Zurich, Switzerland) who contributed to participant care, logistical and administrative management of the study.An abstract of study results was presented in March 2024 at the Advanced Technologies & Treatments for Diabetes conference in Florence, Italy and at the Swiss Society of Paediatrics annual meeting in June 2024 in Lucerne, Switzerland. Data availability De-identified subject level dataset will be made available on a case-by-case basis on reasonable request to the corresponding author for research purposes 6 months after publication. Funding Open access funding provided by University of Bern. The study was supported by the Swiss Diabetes Foundation and by a YTCR grant from the Bangerter-Rhyner Foundation and the Swiss Academy of Medical Sciences. Dexcom and Ypsomed provided product support. Authors’ relationships and activities CIL received a travel grant from the Swiss Society of Endocrinology and Diabetology to present the present work at the Advanced Technologies & Treatments for Diabetes conference in Florence, Italy (Abstract no. 181). KB-K has received research funding from Dexcom, Embecta, Novo Nordisk and Eli Lilly, as well as honoraria from Tandem, Dexcom, Sanofi and Roche. MEW reports patents related to closed-loop (US9402953B2, US9579456B2), and being a consultant at CamDiab. RH reports having received speaker honoraria from Eli Lilly, Dexcom and Novo Nordisk, receiving license fees from BBraun, patents related to closed-loop, and being a director at CamDiab. LB reports having received speaker and advisory board honoraria from Eli Lilly, Dexcom, Novo Nordisk, Ypsomed, Roche, Sanofi and Oviva. Contribution statement CIL, CP, FN, NS, KB-K, LK, DH, DK and LB were involved in the conception, design and conduct of the study and the analysis and interpretation of the results. CIL, CP, FN, NS, LK, KB-K, TZ, DH, DK and LB were involved in the conception of the study and data acquisition. DH, CTN, CIL, KB-K, MEW, RH and LB were involved in the analysis and interpretation of the results. CIL, DH and LB wrote the first draft of the manuscript, and all authors edited, reviewed and approved the final version of the manuscript. CIL, DH, DK and LB are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis., (© 2024. The Author(s).)

Regulating blood glucose level (BGL) for type-1 diabetic patient (T1DP) accurately is very important issue, an uncontrolled BGL outside the standard safe range between 70 and 180 mg/dl results in dire consequences for health and can significantly increase the chance of death. So the purpose of this study is to design an optimized controller that infuses appropriate amounts of exogenous insulin into the blood stream of T1DP proportional to the amount of obtained glucose from food. The nonlinear extended Bergman minimal model is used to present glucose-insulin physiological system, an interval type-2 fuzzy logic controller (IT2FLC) is utilized to infuse the proper amount of exogenous insulin. Superiority of IT2FLC in minimizing the effect of uncertainties in the system depends primarily on the best choice of footprint of uncertainty (FOU) of IT2FLC. So a comparison includes four different optimization methods for tuning FOU including hybrid grey wolf optimizer-cuckoo search (GWOCS) and fuzzy logic controller (FLC) method is constructed to select the best controller approach. The effectiveness of the proposed controller was evaluated under six different scenarios of T1DP using Matlab/Simulink platform. A 24-h scenario close to real for 100 virtual T1DPs subjected to parametric uncertainty, uncertain meal disturbance and random initial condition showed that IT2FLC accurately regulate BGL for all T1DPs within the standard safe range. The results indicated that IT2FLC using GWOCS can prevent side effect of treatment with blood-sugar-lowering medication. Also stability analysis for the system indicated that the system operates within the stability region of nonlinear system. [ABSTRACT FROM AUTHOR]

Macronutrients play a crucial role in management of type 2 diabetes. This is seen in their ability to modulate plasma glucose concentrations. However, the ideal proportions of macronutrients to be consumed in order to maintain ideal plasma glucose concentrations remains elusive. Therefore, this paper set out to conduct a review to investigate the relationship between macronutrients and plasma glucose concentrations from a physiological perspective. The review was conducted using papers obtained from various databases such as MEDLINE (Pubmed), Open Access Journals Elsevier, Free Medical Journals and Google Scholar. The research papers included general reviews, systemic reviews, meta-analyses, and randomized control trials that examined the effect of macronutrients on plasma glucose concentration as well as papers on mathematical models describing the relationship between macronutrient bioavailability and plasma glucose concentration. The review assessed the effect of various macronutrients on postprandial plasma glucose concentration, post-prandial plasma insulin, post-prandial glucose-dependent insulinotropic peptide plasma concentration, and post-prandial glucose-like peptide-1 plasma concentration. The results of the review showed that carbohydrates influence plasma glucose concentration in a dose dependant manner but this is dependent on their bioavailability. This bioavailability was shown to be subject to fluctuations determined by food processing techniques, food structure, and food matrix. The results also showed that some specific types of fats and proteins indirectly influence plasma glucose concentration through their effect on incretin hormones. The effect of fats and proteins on incretin hormones was through different mechanisms and pathways. In-lieu of the findings, the review concludes that the macronutrient composition of diets designed for type 2 diabetic patients should take into consideration the physiological relationship between the macronutrients and plasma glucose concentrations. In this way, diet proportions can be made in such a manner as to determine the exact amounts that will realize near normal plasma glucose concentrations for a type 2 diabetic patient. [ABSTRACT FROM AUTHOR]

People with type 1 diabetes are required to adhere to their treatment rigorously to ensure maximum benefits. Diabetes tracking tools have played an important role in this regard. Type 1 diabetes monitoring has evolved and matured with the advent of blood glucose monitor sensors, insulin pens, and insulin pump automation. However, carbohydrate monitoring has seen little progress despite carbohydrates representing a major potential disruption. Relying on the modeling of carbohydrate intake using the rate of exogenous glucose appearance, we first present a methodology capable of identifying the type of carbohydrates ingested by classifying them into fast and non-fast carbohydrates. Second, we test the ability of the methodology to identify the correct synchrony between the actual mealtime and the time labeled as such in diabetes records. A deep neural network is trained with processed input data that consist of different values to estimate the parameters in a series of experiments in which, firstly, we vary the response of ingested carbohydrates for subsequent identification and, secondly, we shift the learned carbohydrate absorption curves in time to estimate when the meals were administered to virtual patients. This study validates that the identification of different carbohydrate classes in the meal records of people with type 1 diabetes could become a valuable source of information, as it demonstrates the potential to identify inaccuracies in the recorded meal records of these patients, suggesting the potential abilities of the next generation of type 1 diabetes management tools. [ABSTRACT FROM AUTHOR]

Patients with type 1 diabetes must continually decide how much insulin to inject before each meal to maintain blood glucose levels within a healthy range. Recent research has worked on a solution for this burden, showing the potential of reinforcement learning as an emerging approach for the task of controlling blood glucose levels. In this paper, we test and evaluate several deep Q-learning algorithms for automated and personalized blood glucose regulation in an in silico type 1 diabetes patient with the goal of estimating and delivering proper insulin doses. The proposed algorithms are model-free approaches with no prior information about the patient. We used the Hovorka model with meal variation and carbohydrate counting errors to simulate the patient included in this work. Our experiments compare different deep Q-learning extensions showing promising results controlling blood glucose levels, with some of the proposed algorithms outperforming standard baseline treatment. [ABSTRACT FROM AUTHOR]

The development of automated insulin delivery (AID) systems, which connect continuous glucose monitoring (CGM) systems with algorithmic insulin delivery from an insulin pump (continuous subcutaneous insulin infusion, [CSII]), has led to improved glycaemia and quality of life benefits in those with insulin-treated diabetes. This review summarizes the benefits gained by the connectivity between insulin pumps and CGM devices. It details the technical requirements and advances that have enabled this, and highlights the clinical and user benefits of such systems. Clinical trials and real-world outcomes from the use of AID systems in people with type 1 diabetes (T1D) will be the focus of this article; outcomes in people with type 2 diabetes (T2D) and other diabetes subtypes will also be discussed. We also detail the limitations of current technological approaches for connectivity between insulin pumps and CGM devices. While recognizing the barriers, we discuss opportunities for the future., (© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Background and Purpose: Our previous study reported that erythroferrone (ERFE), a newly identified hormone produced by erythroblasts, responded to recombinant human erythropoietin (rHuEPO) sensitively but its dynamics was complicated by double peaks and circadian rhythm. This study intends to elucidate the underlying mechanisms for the double peaks of ERFE dynamics and further determine whether early ERFE measurements can predict haemoglobin responses to rHuEPO., Experimental Approach: By using the purified recombinant rat ERFE protein and investigating its deposition in rats, the production of ERFE was deconvoluted. To explore the role of iron in ERFE production, we monitored short-term changes of iron status after injection of rHuEPO or deferiprone. Pharmacokinetic/pharmacodynamic (PK/PD) modelling was used to confirm the mechanisms and examine the predictive ability of ERFE for long-term haemoglobin responses., Key Results: The rRatERFE protein was successfully purified. The production of ERFE was deconvoluted and showed two independent peaks (2 and 8 h). Transient iron decrease was observed at 4 h after rHuEPO injection and deferiprone induced significant increases of ERFE. Based on this mechanism, the PK/PD model could characterize the complex dynamics of ERFE. In addition, the model predictions further revealed a stronger correlation between ERFE and haemoglobin peak values than that for observed values., Conclusions and Implications: The complex dynamics of ERFE should be composited by an immediate release and transient iron deficiency-mediated secondary production of ERFE. The early peak values of ERFE, which occur within a few hours, can predict haemoglobin responses several weeks after ESA treatment., (© 2024 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Type I diabetes mellitus is a serious autoimmune condition impacting a large population around the world that need a daily infusion of insulin substitutes to regulate blood glucose levels within healthy limits. The purpose of the study was to design a robust μ -controller based on an uncertain linear-time invariant (LTI) representation of the Hovorka model for glucose–insulin metabolism. The model set was obtained using linearization around an equilibrium point and adding parametric uncertainty to account for the time delay variation between plasma glucose concentration and its subcutaneous measurement. As a result, the robust stability and performance of the closed loop were proved using the structured singular value μ. The performance of the designed controller was also checked with a numerical simulation in connection with the nonlinear model. [ABSTRACT FROM AUTHOR]

Comparing the technical reliability and insulin dosing of a "do-it-yourself artificial pancreas" with a commercial hybrid closed-loop system in a "shadow-mode" scenario: An exploratory study Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. Keywords: CSII; insulin pump therapy; subcutaneous injection; type 1 diabetes EN CSII insulin pump therapy subcutaneous injection type 1 diabetes 2780 2783 4 08/03/23 20230901 NES 230901 INTRODUCTION Do-it-yourself artificial pancreas systems (DIYAPSs) are compound devices that autonomously adjust insulin dosage in people with diabetes. However, the DIYAPS was not delivering insulin, which might have caused an overcompensation of insulin adaptions if delivered insulin doses were lower than calculated by the DIYAPS. [Extracted from the article]

In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented. [ABSTRACT FROM AUTHOR]

In view of the poor timeliness of dynamic blood glucose data and a delay of insulin effect for blood glucose control, and considering the nonlinearity and nonstationarity of the glucose data, a new blood Glucose Prediction algorithm combined Correlation coefficient-based complete ensemble empirical mode decomposition with adaptive noise and back propagation neural network (GPCEMBP) was proposed to increase the prediction time and improve the prediction accuracy. It refined the mode decomposition algorithm and integrated the correlation mode filter function to extract the characteristic intrinsic mode functions from the original signal. A new neural network prediction model was constructed by optimizing the number of hidden layer neurons, the number of hidden layers, activation functions, the number of inputs, structure, and other parameters. Finally, a predicted blood glucose value was calculated by phase space reconstruction technology. Through ablation and comparison experiments, it was demonstrated that the GPCEMBP algorithm had better prediction accuracy, convergence, and robustness in blood glucose prediction within 84 min. In addition, it has good adaptability to deal with different quality glucose data. [ABSTRACT FROM AUTHOR]

Mathematical modeling of mass transfer and absorption in the small intestine has been a challenging task. Systematic review and analysis of existing efforts indicate the need to pursue a reliable predictive model that is physically sound and computationally efficient. With the consideration of 3D intestinal inner wall structure, this work rigorously derives an absorption model that can be used as a source term in a 1D distributed model, conventionally called the diffusion–convection–reaction model. Moreover, computational fluid dynamics simulations are carried out to generate in silico experimental data for quantification of the mass‐transfer coefficient in the absorption model. This model facilitates a better understanding of the intricate influence of intestinal morphology and motility on mass transfer and absorption in the intestine. Rat duodenum featuring a villous structure and pendular movement is selected as an example to demonstrate the capability of this approach. [ABSTRACT FROM AUTHOR]

Most advanced technologies for the treatment of type 1 diabetes, such as sensor-pump integrated systems or the artificial pancreas, require accurate glucose predictions on a given future time-horizon as a basis for decision-making support systems. Seasonal stochastic models are data-driven algebraic models that use recent history data and periodic trends to accurately estimate time series data, such as glucose concentration in diabetes. These models have been proven to be a good option to provide accurate blood glucose predictions under free-living conditions. These models can cope with patient variability under variable-length time-stamped daily events in supervision and control applications. However, the seasonal-models-based framework usually needs of several months of data per patient to be fed into the system to adequately train a personalized glucose predictor for each patient. In this work, an in silico analysis of the accuracy of prediction is presented, considering the effect of training a glucose predictor with data from a cohort of patients (population) instead of data from a single patient (individual). Feasibility of population data as an input to the model is asserted, and the effect of the dataset size in the determination of the minimum amount of data for a valid training of the models is studied. Results show that glucose predictors trained with population data can provide predictions of similar magnitude as those trained with individualized data. Overall median root mean squared error (RMSE) (including 25% and 75% percentiles) for the predictor trained with population data are { 6.96 [ 4.87 , 8.67 ] , 12.49 [ 7.96 , 14.23 ] , 19.52 [ 10.62 , 23.37 ] , 28.79 [ 12.96 , 34.57 ] , 32.3 [ 16.20 , 41.59 ] , 28.8 [ 15.13 , 37.18 ] } mg/dL, for prediction horizons (PH) of { 15 , 30 , 60 , 120 , 180 , 240 } min, respectively, while the baseline of the individually trained RMSE results are { 6.37 [ 5.07 , 6.70 ] , 11.27 [ 8.35 , 12.65 ] , 17.44 [ 11.08 , 20.93 ] , 22.72 [ 14.29 , 28.19 ] , 28.45 [ 14.79 , 34.38 ] , 25.58 [ 13.10 , 36.60 ] } mg/dL, both training with 16 weeks of data. Results also show that the use of the population approach reduces the required training data by half, without losing any prediction capability. [ABSTRACT FROM AUTHOR]

This paper proposes a simulation linear model created in the MATLAB environment, which provides a process for regulating blood sugar levels. The controller is built for the need for any type of diabetes to control and normalize the blood sugar content of the patient in order to eliminate the differences in the quality of life of a diabetic patient and a healthy person. The linearization of the nonlinear model was performed, and the adequacy of the linearized model was verified and confirmed using the MATLAB simulation. The choice of the PID controller and the CHR method for its adjustment was justified and MATLAB tools were used to show the implementation of these methods. The model of the patient with the controller has been built; the algorithm for the automatic adjustment of the PID controller parameters has been developed and realized. The directions for continuation of the work on this problem regarding regulation in the system under study are proposed. [ABSTRACT FROM AUTHOR]

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future., (© 2024. International Federation for Medical and Biological Engineering.)

In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F 1 , and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place. [ABSTRACT FROM AUTHOR]