Search Results (481)

Background/Objectives: Type 2 diabetes (T2D) affects more than 38 million Americans and remains a leading public health challenge. Behavioral self-management is central to glycemic control but is often undermined by dysregulated and addictive-like eating behaviors. Continuous glucose monitoring (CGM) offers immediate feedback that may strengthen self-regulation, yet the psychological processes linking CGM use, food addiction (FA), and behavior change are poorly understood. This secondary mixed-methods study examined how CGM-supported group medical visits (GMVs) influence glycemic outcomes and FA symptoms in adults with diabetes. Methods: Adults with T2D participated in a 14-week GMV program integrating CGM review with education on nutrition, physical activity, sleep, stress, and intermittent fasting. Thirteen participants had paired CGM summaries and psychosocial data. Quantitative outcomes included mean glucose, glycemic variability, time-in-range (TIR), and symptoms of food addiction using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Qualitative data came from open-ended surveys analyzed using reflexive thematic analysis. Integration followed a convergent design, merging individual change trajectories with thematic interpretations and case vignettes. Results: Mean glucose decreased by 21 mg/dL and TIR improved by 9 percentage points. Among six participants with baseline FA symptoms, all showed reductions in self-reported mYFAS 2.0 symptom counts. Four moved from mild to no symptoms, one from moderate to no symptoms, and one from severe to no symptoms. Across the full sample, the mean change was a reduction of 1.2 in the mYFAS 2.0 symptom counts per participant. Thematic analysis identified four interrelated psychological mechanisms: enhanced awareness of food–glucose relationships, increased accountability through shared tracking, motivation via gamified self-monitoring, and relief from cognitive burden associated with dietary uncertainty. Conclusions: Integrating CGM feedback into GMVs was associated with improvements in glycemic metrics and reductions in addictive-like eating symptoms in this pilot sample. These findings position CGM as a behavioral intervention tool that complements its traditional monitoring role and highlight the value of combining real-time biofeedback with group-based support in diabetes care. Full article

Background/Objectives: Our objective was to assess the role of clinical and continuous glucose monitoring (CGM) parameters in predicting the risk of hypoglycemia in pediatric patients with type 1 diabetes. Methods: Pediatric patients with type 1 diabetes (n = 71) at the Oradea County Clinical Emergency Hospital, Romania, who underwent CGM during their initial visit and were followed for at least 6 months with in-clinic visits every 3 months were enrolled in this study. Age, body mass index, time in range, the mean daily glucose (MDG) concentration, and the coefficient of variation (%CV) were considered as potential predictors of the risk of hypoglycemia, which was defined as the percentage of time spent below two glycemic thresholds of 3.9 and 3.0 mmol/L, corresponding to mild and clinically significant hypoglycemia, respectively. Results: Among a total of 142 glycemic profiles, the MDG concentration was significantly lower in those with hypoglycemia compared to those without, whereas %CV was significantly higher (p < 0.0001). Regression tree models identified %CV as the dominant variable for both thresholds, whereas classification tree models identified %CV as the dominant variable for clinically significant hypoglycemia and MDG for mild hypoglycemia. In profiles with a %CV of less than 36.15% and an MDG concentration greater than 7.16 mmol/L, the mean percentage of time spent below the 3.9 mmol/L threshold was 4.8%, which is close to that recommended by the American Diabetes Association guidelines. Patients younger than 7 years presented the highest frequency for both mild and clinically significant hypoglycemic episodes. Conclusions: Our study supports %CV and the MDG concentration as key factors in predicting hypoglycemia risk. Minimizing the risk of hypoglycemia in pediatric patients requires a %CV of less than 36%. Full article

Background/Objectives: Migraine and diabetes mellitus are highly prevalent chronic diseases, and their comorbidity presents management challenges, particularly when wearable medical technologies are used concurrently. Remote electrical neuromodulation (REN; Nerivio^®) is an FDA-cleared non-pharmacological migraine therapy, and continuous glucose monitoring (CGM) systems are widely used in diabetes care. However, the safety and compatibility of simultaneous co-use have not yet been evaluated. This technical compatibility study aimed to assess whether REN operation affects CGM performance or interferes with glucose measurement integrity in diabetic adults. Methods: Twenty-one adults with diabetes using Dexcom G6/G7 or FreeStyle Libre 2/3 participated in a single-arm interventional study. During a 45 min session, participants operated the REN and CGM devices simultaneously on their smartphones, and the REN device was paused three times to compare CGM readings between REN ON and RED OFF conditions. The primary outcome was the mean absolute relative difference (MARD_{REN ON/OFF}), evaluated against a prespecified 5% threshold. Statistical analysis included the Wilcoxon test, with subgroup analysis by the CGM device family. Results: The median MARD_{REN ON/OFF} across all participants was 1.61% (IQR 0.84–2.44%), significantly below the 5% threshold (p < 0.001). All participants achieved MARD_{REN ON/OFF} < 5%. Subgroup analyses were consistent: the median MARD_{REN ON/OFF} was 1.70% (IQR 0.90–2.45%) for Dexcom and 1.05% (IQR 0.83–1.50%) for Abbott. No technical interference, Bluetooth disruptions, missed data transmission, or adverse events were observed. Conclusions: Simultaneous use of Nerivio^® REN and CGM systems in adults with diabetes is compatible and safe, with no evidence of interference or significant deviations in glucose readings. These findings support the integrated and reliable use of REN and CGM wearables in adults with diabetes managing comorbid conditions. Full article

Cross-scene hyperspectral image (HSI) classification under single-source domain generalization (DG) is a crucial yet challenging task in remote sensing. The core difficulty lies in generalizing from a limited source domain to unseen target scenes. We formalize this through the causal theory, where different sensing scenes are viewed as distinct interventions on a shared physical system. This perspective reveals two fundamental obstacles: interventional distribution shifts arising from varying acquisition conditions, and confounding biases induced by spurious correlations driven by domain-specific factors. Taking the above considerations into account, we propose CauseHSI, a causality-inspired framework that offers new insights into cross-scene HSI classification. CauseHSI consists of two key components: a Counterfactual Generation Module (CGM) that perturbs domain-specific factors to generate diverse counterfactual variants, simulating cross-domain interventions while preserving semantic consistency, and a Causal Disentanglement Module (CDM) that separates invariant causal semantics from spurious correlations through structured constraints under a structural causal model, ultimately guiding the model to focus on domain-invariant and generalizable representations. By aligning model learning with causal principles, CauseHSI enhances robustness against domain shifts. Extensive experiments on the Pavia, Houston, and HyRANK datasets demonstrate that CauseHSI outperforms existing DG methods. Full article

Cemented granular materials (CGMs) represent a transitional class of geomaterials where mechanical behavior is governed by the interplay between a discrete granular skeleton and a continuous cementitious matrix. While previous studies have focused on idealized spherical particles, this study aims to quantify the influence of the cement filling ratio (ranging from 10% to 100%) on the mechanical constitutive behavior of CGMs fabricated with large, irregular granitic aggregates (14–20 mm). Unconfined compressive tests and splitting tensile tests were conducted to evaluate the evolution of strength, stiffness, and failure modes. The results reveal a distinct mechanical transition governed by the cement filling ratio (${\rho }_m$). The elastic modulus and splitting tensile strength exhibited a linear increase with ${\rho }_m$ (R2 > 0.95), indicating a direct dependence on the volume fraction of the binding phase. In contrast, the unconfined compressive strength (UCS) and peak strain displayed a bilinear growth pattern with a critical inflection point at ${\rho }_m$ = 80%. For the specific irregular granitic aggregate skeleton investigated, this threshold marks the transition from contact-dominated stability to matrix-dominated continuum behavior. Below this threshold, strength gain is limited by the stability of discrete particle contacts; above 80%, the material behaves as a continuum, with UCS increasing rapidly to a maximum of 41.78 MPa at 100% filling. Furthermore, the dispersion of stress–strain responses significantly decreased as ${\rho }_m$ exceeded 50%, attributed to the homogenization of stress distribution within the specimen. These findings provide a quantitative basis for optimizing cement usage in ground reinforcement applications, identifying 80% as a critical design threshold. Full article

Background/Objectives: Performing Hajj, the annual Islamic pilgrimage to Mecca and one of the world’s largest mass gatherings, involves considerable physical exertion in high temperatures and presents unique challenges for people with type 1 diabetes (PWT1D). We examined the feasibility, safety, and user experience of automated insulin delivery (AID) systems during Hajj. Methods: This mixed-methods study evaluated six PWT1D who used an AID pump (2 MiniMed 780G, 2 Medtrum, 1 OmniPod 5, and 1 Open-source AID) while performing Hajj in 2024–2025. Pump and CGM-derived metrics were compared across pre-Hajj, during Hajj, and post-Hajj periods. A structured survey captured participants’ experiences, challenges, and recommendations for AID use during Hajj. Results: The average percent time in range (TIR) remained stable from pre- to during Hajj (54.98 to 54.18, p > 0.05) and significantly increased post-Hajj (62.62, p < 0.05). The percent time above range (TAR > 180) and Glycemia Risk Index significantly decreased from pre- to post-Hajj (28.34 to 26.28 and 50.3 to 19.3, respectively, both p < 0.05). The percent time below range (TBR) remained low (<1%) across the three periods with no incidence of acute diabetes-related complications. Participants emphasized increased confidence and peace of mind with AID use and reported challenges related to heat exposure, prolonged walking, and lack of awareness regarding diabetes technology among HCPs. Conclusions: The use of AID during Hajj appeared to be safe and effective for PWT1D in our study, maintaining stable glycemic control under physically demanding conditions. As the first study to evaluate AID use during Hajj, our findings call for larger studies to explore the integration of diabetes technology into Hajj care protocols and highlight the need for structured pre-Hajj education for PWT1D and HCPs. Full article

In this study, a three-dimensional Inverse Conjugate Heat Transfer Problem (ICHTP) is numerically and experimentally investigated to estimate the heat-source strength of multiple chips mounted on a printed circuit board (PCB) using the Conjugate Gradient Method (CGM) and infrared thermography. The interfaces between the PCB and the surrounding air domain are assumed to exhibit perfect thermal contact, establishing a fully coupled conjugate heat transfer framework for the inverse analysis. Unlike the conventional Inverse Heat Conduction Problem (IHCP), which typically only accounts for conduction within solid domains, the present ICHTP formulation requires the simultaneous solution of the governing continuity, momentum, and energy equations in the air domain, along with the heat conduction equation in the chips and PCB. This coupling introduces substantial computational complexity due to the nonlinear interaction between convective and conductive heat transfer mechanisms, as well as the sensitivity of the inverse solution to measurement uncertainties. The numerical simulations are conducted first with error-free measurement data and an inlet velocity of u_in = 4 m/s; the recovered heat-sources exhibit excellent agreement with the true values. The computed average errors for the estimated temperatures ERR1 and estimated heat sources ERR2 are as low as 0.0031% and 1.87%, respectively. The accuracy of the estimated heat sources is then experimentally validated under various prescribed inlet air velocities. During experimental verification at an inlet velocity of 4 m/s, the corresponding ERR1 and ERR2 values are obtained as 0.91% and 3.34%, while at 6 m/s, the values are 0.86% and 2.81%, respectively. Compared with the numerical results, the accuracy of the experimental estimations decreases noticeably. This discrepancy arises because the numerical simulations are free from measurement noise, whereas experimental data inherently include uncertainties due to thermal picture resolutions, environmental fluctuations, and other uncontrollable factors. These results highlight the inherent challenges associated with inverse problems and underscore the critical importance of obtaining precise and reliable temperature measurements to ensure accurate heat source estimation. Full article

Maize (Zea mays L.) is a major economic crop highly susceptible to Colletotrichum graminicola, the causal agent of anthracnose leaf blight, which causes substantial annual yield losses. This fungal pathogen employs numerous effectors to manipulate plant immunity, yet the functions of many secreted proteins during biphasic infection remain poorly characterized. In this study, we identified CgMas2, a candidate secreted protein in C. graminicola and a homolog of Magnaporthe oryzae MoMas2. Deletion of CgMAS2 in the wild-type strain CgM2 did not affect fungal vegetative growth or conidial morphology but significantly impaired virulence on maize leaves. Leaf sheath infection assays revealed that CgMas2 is required for biotrophic invasive hyphal growth, as the mutant showed defective spreading of invasive hyphae to adjacent cells. Subcellular localization analysis indicated that CgMas2 localizes to the cytoplasm of conidia and to the primary infection hyphae. Furthermore, DAB staining demonstrated that disrupt of CgMAS2 leads to host reactive oxygen species (ROS) accumulation. Comparative transcriptome analysis of maize infected with ΔCgmas2 versus CgM2 revealed enrichment of GO terms related to peroxisome and defense response, along with up-regulation of benzoxazinoid biosynthesis genes (benzoxazinone biosynthesis 3, 4 and 5) at 60 h post-inoculation (hpi). Conversely, six ethylene-responsive transcription factors (ERF2, ERF3, ERF56, ERF112, ERF115 and ERF118) involved in ethylene signaling pathways were down-regulated at 96 hpi. These expression patterns were validated by RT-qPCR. Collectively, our results demonstrate that CgMas2 not only promotes invasive hyphal growth during the biotrophic stage but may also modulate phytohormone signaling and defense compound biosynthesis during the necrotrophic phase of infection. Full article

The limitations of conventional diabetes management are increasingly evident. As a result, both type 1 and 2 diabetes in pediatric populations have become major global health concerns. As new technologies emerge, particularly artificial intelligence (AI), they offer new opportunities to improve diagnostic accuracy, treatment outcomes, and patient self-management. A PRISMA-based systematic review was conducted using PubMed, Web of Science, and BIREME. The research covered studies published up to February 2025, where twenty-two studies met the inclusion criteria. These studies examined machine learning algorithms, continuous glucose monitoring (CGM), closed-loop insulin delivery systems, telemedicine platforms, and digital educational interventions. AI-driven interventions were consistently associated with reductions in HbA1c and extended time in range. Furthermore, they reported earlier detection of complications, personalized insulin dosing, and greater patient autonomy. Predictive models, including digital twins and self-learning neural networks, significantly improved diagnostic accuracy and early risk stratification. Digital health platforms enhanced treatment adherence. Nonetheless, the barriers included unequal access to technology and limited long-term clinical validation. Artificial intelligence is progressively reshaping pediatric diabetes care toward a predictive, preventive, personalized, and participatory paradigm. Broader implementation will require rigorous multiethnic validation and robust ethical frameworks to ensure equitable deployment. Full article

This study aimed to develop a non-invasive blood glucose estimation method by integrating wearable multimodal signals, including photoplethysmography (PPG), electrodermal activity (EDA), and skin temperature (ST), with food log–derived nutritional features, and to validate its clinical reliability. We analyzed data from 16 adults who underwent continuous glucose monitoring (CGM) while multimodal physiological signals were collected over 8–10 consecutive days, yielding more over 20,000 paired samples. Features from food logs and physiological signals were extracted, followed by feature selection using Boruta and minimum Redundancy Maximum Relevance (mRMR). Five machine learning models were trained and evaluated using five-fold cross-validation. Food log features alone demonstrated stronger predictive power than unimodal physiological signals. The fusion of nutritional, physiological, and temporal features achieved the best accuracy using LightGBM, reducing the RMSE to 12.9 mg/dL, with a MARD of 7.9%, a MAE of 8.82 mg/dL, and R² of 0.69. SHapley Additive exPlanations (SHAP) analysis revealed that 24-h carbohydrate and sugar intake, time since last meal, and short-term EDA features were the most influential predictors. By integrating multimodal wearable and dietary information, the proposed framework significantly enhances non-invasive glucose estimation. The interpretable LightGBM model demonstrates promising clinical utility for continuous monitoring and early dysglycemia management. Full article

Background: Dysglycemia remains a persistent challenge in hospital care. Despite advances in outpatient diabetes technology, inpatient insulin management largely depends on intermittent point-of-care glucose testing, static insulin dosing protocols and rule-based decision support systems. Artificial intelligence (AI) offers potential to transform this care through predictive modeling and adaptive insulin control. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a scoping review was conducted to characterize AI algorithms for insulin dosing and glycemic management in hospitalized patients. An interdisciplinary team of clinicians and engineers reached consensus on AI definitions to ensure inclusion of machine learning, deep learning, and reinforcement learning approaches. A librarian-assisted search of five databases identified 13,768 citations. After screening and consensus review, 26 studies (2006–2025) met the inclusion criteria. Data were extracted on study design, population, AI methods, data inputs, outcomes, and implementation findings. Results: Studies included ICU (N = 13) and general ward (N = 9) patients, including patients with diabetes and stress hyperglycemia. Early randomized trials of model predictive control demonstrated improved mean glucose (5.7–6.2 mmol/L) and time in target range compared with standard care. Later machine learning models achieved strong predictive accuracy (AUROC 0.80–0.96) for glucose forecasting or hypoglycemia risk. Most algorithms used data from Medical Information Mart for Intensive Care (MIMIC) databases; few incorporated continuous glucose monitoring (CGM). Implementation and usability outcomes were seldom reported. Conclusions: Hospital AI-driven models showed strong algorithmic performance but limited clinical validation. Future co-designed, interpretable systems integrating CGM and real-time workflow testing are essential to advance safe, adaptive insulin management in hospital settings. Full article

Hypoglycaemia in patients with type 1 diabetes mellitus (T1DM) remains a major clinical burden and, beyond its metabolic complications, can cause serious cardiac arrhythmias. Multiple mechanisms lead to different types of arrhythmias during hypoglycaemia. However, existing studies often involve mixed diabetes populations, small cohorts, or limited monitoring during nocturnal periods, leaving a critical gap in understanding the links between glucose fluctuations and arrhythmic events. This review provides an updated combination of experimental and clinical evidence describing how autonomic dysfunction and ionic imbalances lead to electrophysiological instability and structural remodelling of the myocardium during hypoglycaemia. Continuous glucose monitoring (CGM) combined with electrocardiographic or wearable rhythm tracking may enable early detection of glycemic and cardiac disturbances and help identify high-risk individuals. Future prospective studies using combined CGM–ECG monitoring, particularly during sleep, are essential to clarify the relationship between hypoglycaemia and arrhythmic events. Full article

Accurate prediction of Blood Glucose (BG) levels is essential for effective diabetes management and the prevention of adverse glycemic events. This study introduces a novel designed hybrid Gated Recurrent Unit-Transformer (GRU-Transformer) model tailored to forecast BG levels at 15, 30, 45, and 60 min horizons using only Continuous Glucose Monitoring (CGM) data as input. The proposed approach integrates advanced CGM feature extraction step. The extracted features are statistically the mean, the median, the maximum, the entropy, the autocorrelation and the Detrended Fluctuation Analysis (DFA). In addition, in order to define more enhanced and specific features, the custom 3-points monotonicity score, the sinusoidal time encoding, and the workday/weekend binary features are proposed in this work. This approach enables the model to capture physiological dynamics and contextual temporal patterns of Type 1 Diabetes (T1D) with great accuracy. To thoroughly assess the performance of the proposed method, we relied on several well-established metrics, including Root Mean Squared Error (RMSE), Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Percentage Error (RMSPE). Experimental results demonstrate that the proposed method achieves superior predictive accuracy for both short-term (15–30 min) and long-term (45–60 min) forecasting. Specifically, the model attained the lowest average RMSE values, with 4.00 mg/dL, 6.65 mg/dL, 7.96 mg/dL, and 8.91 mg/dL and yielding consistently high R² scores for the respective prediction horizons. This new method distinguishes itself by continuously exceeding current prediction models, reinforcing its potential for real-time CGM and clinical decision support. Its high accuracy and adaptability make it a favorable tool for improving diabetes management and personalized glycemic control. Full article

Background: The use of continuous glucose monitoring (CGM) offers several benefits. Compared to point-of-care (POC) capillary glucose tests, user acceptability is greater, and time in the target glucose range is improved. If these advantages can be transferred from outpatient to in-patient settings, CGM could assist clinicians in making timely, proactive treatment decisions. Scope of the review: This scoping review focuses on clinical studies of CGM use in hospital settings among non-pregnant adults, with a particular focus on studies from 2023 to 2025. It examines the latest evidence and guidelines and sets out the clinical and analytical considerations involved in implementing in-patient CGM. Main findings: In-hospital CGM facilitates hypoglycemia detection, especially asymptomatic and nocturnal episodes. Data on the impact of CGM use on clinical outcomes are scarce, and most studies focus on the reliability of CGM technology rather than clinical outcomes. Several factors affect CGM accuracy in hospitals, such as medications, fluid management, and hemodynamic disturbances. Despite between-device and settings-related variability, CGM devices generally show reasonable accuracy, with Mean Absolute Relative Differences (MARDs) ranging from 10% to 23%. In-hospital CGM has also improved workflows and reduced personnel exposure in infectious disease settings. Key implementation challenges: The MARD thresholds for safe in-hospital CGM use without confirmatory POC testing and evidence-based protocols for CGM application in ICU and non-ICU settings are not yet established. Despite challenges related to implementation, including personnel training, integrating diabetes technology with electronic health records, and costs, the benefits of improved monitoring and in-patient safety make CGM use worthwhile to pursue. Full article

1,5-Pentanediamine (PDA) is an important monomer for the synthesis of nylon materials. However, its microbial production from glucose is severely limited by product cytotoxicity, which slows the metabolism of both precursor lysine and glucose uptake. To overcome this limitation, a PDA-responsive dynamic regulatory switch (PDRS) was constructed using the transcriptional repressor CgmR and the P_cgmA promoter. By replacing promoters and ribosome-binding sites, the response window of the PDRS was optimized to a PDA concentration range of 38.9–87 g/L. Based on this system, the PDRS was employed to enhance lysine biosynthesis and glucose uptake. Following fermentation optimization, the optimal strain Corynebacterium glutamicum YY3.6 produced 105.5 g/L PDA within 36 h, achieving a PDA productivity of 2.93 g/L/h and a yield of 0.36 g/g glucose. Collectively, these results provide an effective strategy for the microbial production of PDA from glucose. Full article