Type 1 Diabetes (T1D) is a chronic metabolic disease characterized by elevated blood glucose (BG) concentrations, resulting from the immune-mediated destruction of insulin-producing $\beta$-cells in the pancreas. Effective management of T1D greatly benefits from constant monitoring of BG levels, achievable in real-time using minimally invasive continuous glucose monitoring (CGM) devices. These devices provide data streams that can be leveraged by forecasting algorithms to predict BG levels minutes in advance, enabling timely therapeutic interventions to prevent adverse events, such as hypo/hyperglycemia. With the increasing availability of data, deep learning (DL) algorithms have emerged as the state-of-the-art for BG forecasting, owing to their ability to autonomously learn complex nonlinear relationships, such as those underlying the glucoregulatory system. Despite a growing body of research, a comprehensive review specifically focusing on DL applications for BG prediction is still lacking. To address this gap, a systematic review was conducted following the PRISMA guidelines, involving extensive searches across PubMed, Scopus, and Web of Science databases. A total of 26 studies satisfied the inclusion criteria and were evaluated based on dataset characteristics, model inputs, training paradigm, prediction horizon, model architecture, evaluation metrics, performance, and baseline comparators. While DL models show great promise, several challenges persist-particularly in ensuring physiological fidelity and interpretability, both essential for clinical adoption. To overcome these barriers, future research should prioritize the integration of explainable AI (XAI) techniques to improve model reliability and safety, ultimately supporting the effective deployment of DL models in real-time T1D management.

Continuous Glucose Monitoring sensors (CGMs) have revolutionized type 1 diabetes (T1D) management. In particular, in several cases, the retrospective analysis of CGM recordings allows clinicians to review and adjust patients' therapy. However, in this set-up, the artifacts that are often present in CGM data could lead to incorrect therapeutic actions. To mitigate this risk, we investigate how to detect one of the most common of these artifacts, the so-called pressure induced sensor attenuations, by means of anomaly detection algorithms. Specifically, these methods belong to the class of unsupervised techniques, which is particularly appealing since it does not require a labeled dataset, hardly available in practice. After having designed five features to highlight the anomalous state of the sensor, 8 different methods (e.g. Isolation Forest and Histogram-based Outlier Score) are assessed both in silico using the UVa/Padova Type 1 Diabetes Simulator and on real data of 36 subjects monitored for about 10 days. In the in silico scenario, the best results are achieved with Isolation Forest, which recognized the 74% of the failures generating on average only 2 false alerts during the whole monitoring time. In real data, Isolation Forest is confirmed to be effective in the detection of failures, achieving a recall of 55% and generating 3 false alarms in 10 days. By allowing to detect more than 50% of the artifacts while discarding only a few portions of correct data in several days of monitoring, the proposed approach could effectively improve the quality of CGM data used by clinicians to retrospectively evaluate and adjust T1D therapy.

Individuals with type 1 diabetes (T1D) require lifelong insulin replacement to compensate for deficient endogenous insulin secretion, which would otherwise result in abnormal blood glucose levels. In recent years, significant investments have been made to improve T1D management, leading to the widespread adoption of accurate technology such as continuous glucose monitoring (CGM) sensors and automated insulin delivery systems. However, malfunctions in these devices, particularly pump systems, can cause undesirable interruptions of insulin delivery posing significant safety risks if not promptly addressed. Due to the low frequency of these episodes, developing accurate algorithms to identify insulin pump faults remains a challenge. To address these issues, this paper proposes a novel approach for detecting insulin pump faults (IPFs) by combining the ability of a long short-term memory (LSTM) autoencoder to extract features, with the strength of random forest to distinguish between anomalous and normal patterns. This method was developed and evaluated using data from 100 subjects, simulated over 90 days with the UVa/Padova T1D Simulator, an FDA-approved nonlinear computer simulator of T1D physiology. In the test set, the proposed algorithm identified the 93% of the total faults, while raising 2 false alarms in 3 months on average. These findings suggest that deep learning algorithms can enhance the safety and reliability of insulin pump systems, contributing to more effective therapeutic technologies.

Continuous Glucose Monitors are minimally-invasive portable sensors that are revolutionizing the management of Type 1 Diabetes (T1D). A common issue encountered in their daily use is related to the presence of pressure-induced sensor attenuations (PISAs), temporary faults of the devices, resulting in false low blood glucose readings that can impact and compromise the reliability of CGMs. In this work, we explore the application of matched filters (MFs), a powerful pattern recognition technique, for the retrospective identification of PISAs failures. A MF is designed for the detection of a signal with a specific shape, associated with the occurrence of a PISA episode. The proposed algorithm is tested in-silico on a dataset generated with a state-of-art T1D patient simulator. MFs achieve a recall of 0.75 with about 1 false alarm every 5 days, outperforming other state-of-art algorithms proposed for the same purpose, including one based on a Random Forest classifier (RF). Moreover, when embedded as additional feature within a RF it improves the performance by granting a recall of 0.83 and 1 false alarm raised in 10 days. The encouraging outcomes in the simulated scenario pave the way for future investigations involving real-world data, as well as potential enhancements in detecting different types of sensors’ failures.

BACKGROUND: The ability to rapidly adapt to adverse environmental conditions represents the key of success of many pathogens and, in particular, of Mycobacterium tuberculosis. Upon exposition to heat shock, antibiotics or other sources of stress, appropriate responses in terms of genes transcription and proteins activity are activated leading part of a genetically identical bacterial population to express a different phenotype, namely to develop persistence. When the stress response network is mathematically described by an ordinary differential equations model, development of persistence in the bacterial population is associated with bistability of the model, since different emerging phenotypes are represented by different stable steady states. RESULTS: In this work, we develop a mathematical model of SigE stress response network that incorporates interactions not considered in mathematical models currently available in the literature. We provide, through involved analytical computations, accurate approximations of the system's nullclines, and exploit the obtained expressions to determine, in a reliable though computationally efficient way, the number of equilibrium points of the system. CONCLUSIONS: Theoretical analysis and perturbation experiments point out the crucial role played by the degradation pathway involving RseA, the anti-sigma factor of SigE, for coexistence of two stable equilibria and the emergence of bistability. Our results also indicate that a fine control on RseA concentration is a necessary requirement in order for the system to exhibit bistability.

This paper considers the classification problem using support vector (SV) machines and investigates how to maximally reduce the size of the training set without losing information. Under separable data set assumptions, we derive the exact conditions stating which observations can be discarded without diminishing the overall information content. For this purpose, we introduce the concept of potential SVs, i.e., those data that can become SVs when future data become available. To complement this, we also characterize the set of discardable vectors (DVs), i.e., those data that, given the current data set, can never become SVs. Thus, these vectors are useless for future training purposes and can eventually be removed without loss of information. Then, we provide an efficient algorithm based on linear programming that returns the potential and DVs by constructing a simplex tableau. Finally, we compare it with alternative algorithms available in the literature on some synthetic data as well as on data sets from standard repositories.