Search Results (5,147)

In the current study, we focus on analyzing the relationship between changes in micro- and macrocirculation and different stages of metabolic memory. We hypothesized that early poor glycemic control induces lasting endothelial changes detectable in pediatric type 1 diabetes (T1D) microcirculation. We assessed microcirculation structure and function using capillaroscopy, transcutaneous oxygen pressure (TcPO₂), and optical coherence tomography (OCT). We evaluated macrovascular circulation using pulsatility index (PI), ankle-brachial index (ABI) and pulse pressure (PP). We also examined the relationship between circulation parameters, the age at onset, and diabetes duration. The study included 67 patients with uncomplicated type 1. We divided all patients into four groups based on their HbA_1c levels at T1D onset and their average HbA_1c after one and two years. We assessed the concentrations of TNF-α, IL-35, IL-4, IL-10, IL-18, IL-12, serum angiogenin, VEGF, sVCAM-1, ICAM-1, sP-Selectin, AGEs, and sRAGE. We compared subgroups with different levels of metabolic memory but comparable T1D duration and age at diagnosis. Micro- and macrovascular parameters were similar between the groups. Our comparison of subgroups with identical metabolic memory but different durations and ages at diagnosis revealed clear differences. The subgroup with a shorter T1D duration showed higher capillary density and a smaller inter-capillary distance compared to those with a longer diabetes duration. This subgroup with shorter duration had significantly lower AGE levels and a reduced TNF-α/IL-35 ratio, along with higher levels of IL-35, IL-4, and IL-12, compared to the longer-duration group. Our findings indicate that in youths with uncomplicated T1D, disease duration—not metabolic memory—plays a dominant role in early microvascular alterations. Full article

Post-traumatic lymphedema (PTL) is a chronic and often under-recognized sequela of soft tissue trauma, leading to persistent swelling, functional impairment, and increased risk of infection. While lymphedema is traditionally associated with oncologic interventions, growing evidence highlights the significant burden of PTL in trauma patients. This review provides a comprehensive analysis of the current understanding of PTL, including epidemiology, risk factors, pathophysiology, diagnostic modalities, and treatment strategies. PTL often occurs after high-impact musculoskeletal injuries (such as open fractures with significant soft tissue loss) or burns (especially if deep or circumferential). This risk is increased if injury occurs at critical areas of increased lymphatic density (such as anteromedial leg, medial knee, medial thigh, medial elbow, or medial arm). Advances in imaging techniques, including indocyanine green lymphography and magnetic resonance lymphangiography, have improved early detection and classification of PTL. Management approaches range from conservative therapies, such as complete decongestive therapy (CDT), to surgical interventions, including lymphaticovenous anastomosis (LVA), vascularized lymph node transfer (VLNT), and vascularized lymph vessel transfer (VLVT)/lymph-interpositional-flap transfer (LIFT). We report on our experience with two patients. At our center, we diagnose and stage PTL with ICG lymphography and trial CDT for 6 months. If there is no significant improvement, we recommend LVA. If there is insufficient improvement after 12 months, we recommend LIFT/repeat LVA/VLNT. We also treat open fractures with significant soft tissue defects with LIFT, as prophylaxis against PTL. PTL remains an underdiagnosed condition, necessitating increased awareness and intervention to prevent long-term disability. Full article

Cardiovascular diseases pose a major global health threat, making early automated detection through heart sound analysis crucial for their prevention. However, existing deep learning-based heart sound detection methods have shortcomings in feature extraction, and current attention mechanisms perform inadequately in capturing key heart sound features. To address this, we first introduce a Multi-Scale Feature Extraction Network composed of Multi-Scale Inverted Residual (MIR) modules and Dynamically Gated Convolution (DGC) modules to extract heart sound features effectively. The MIR module can efficiently extract multi-scale heart sound features, and the DGC module enhances the network’s representation ability by capturing feature interrelationships and dynamically adjusting information flow. Subsequently, a Multi-Scale Attention Prediction Network is designed for heart sound feature classification, which includes a multi-scale attention (MSA) module. The MSA module effectively captures subtle pathological features of heart sound signals through multi-scale feature extraction and cross-scale feature interaction. Additionally, pitch-shifting techniques are applied in the preprocessing stage to enhance the model’s generalization ability, and multiple feature extraction techniques are used for initial feature extraction of heart sounds. Evaluated via five-fold cross-validation, the model achieved accuracies of 98.89% and 98.86% on the PhysioNet/CinC 2016 and 2022 datasets, respectively, demonstrating superior performance and strong potential for clinical application. Full article

Dollar spot is a severe and widespread turfgrass disease. Ultraviolet-C (UV-C) light treatment offers a promising management strategy, and its integration into autonomous mowers could reduce fungicide use, promoting sustainable and efficient turfgrass management. To ensure effectiveness and optimize intervention timing, monitoring is essential and hyperspectral sensing could represent a valuable resource. This study aimed to develop an innovative approach for the early detection and integrated management of dollar spot in bermudagrass by evaluating (i) the efficacy of an autonomous mower equipped with UV-C lamps in mitigating infections, and (ii) the potential of full-range hyperspectral sensing (350–2500 nm) for disease detection and monitoring. The autonomous mower enabled UV-C treatment with a field capacity of 0.04 ha h⁻¹, requiring 1.3 machines to treat 1 ha day⁻¹, and a primary energy consumption of 55.06 kWh ha⁻¹ for a complete weekly treatment. Full-range canopy hyperspectral data (400–2400 nm) enabled rapid, non-destructive field detection. Permutational multivariate analysis of variance (PERMANOVA) detected significant effects of Clarireedia jacksonii (Cj; dollar spot pathogen) and the Cj × UV-C interaction. Partial least-squares discriminant analysis (PLS-DA) separated Cj+/UV+ and Cj+/UV− plots (Accuracy validation ≈ 0.73; K ≈ 0.69). Investigated spectral indices confirmed Cj × UV-C interactions. Future research should explore how to optimize UV-C application regimes, improve system scalability, and enhance the robustness of hyperspectral models across diverse turfgrass genotypes, growth stages, and environmental conditions. Full article

Reliable object detection is crucial for autonomous driving, particularly in highway construction zones where early hazard recognition ensures safety. This paper introduces an enhanced YOLOv8s-based detection system incorporating a motion-temporal attention module (MTAM) to improve robustness under high-speed and dynamic conditions. The proposed architecture integrates a cross-stage partial (CSP) backbone, feature pyramid network-path aggregation network (FPN-PAN) feature fusion, and advanced loss functions to achieve high accuracy and temporal consistency. MTAM leverages temporal convolutions and attention mechanisms to capture motion cues, enabling effective detection of blurred or partially occluded objects. A custom dataset of 34,240 images, expanded through extensive data augmentation and 9-Mosaic transformations, is used for training. Experimental results demonstrate strong performance with mAP(IoU[0.5]) of 90.77 ± 0.68% and mAP(IoU[0.5:0.95]) of 70.20 ± 0.33%. Real-world highway tests confirm recognition rates of 96% for construction vehicles, 92% for roadside warning signs, and 84% for flag bearers. The results validate the framework’s suitability for real-time deployment in intelligent transportation systems. Full article

Gastric cancer (GC) often presents a poor prognosis due to its asymptomatic phenotype at early stages. Upper endoscopy, which is the current gold standard to diagnose GC, is invasive with limited sensitivity for detecting gastric preneoplasia. Non-invasive biomarkers, such as blood circulating proteins, offer a promising alternative for the early detection of gastric lesions. In this prospective study, we identified plasma protein biomarkers for gastric preneoplasia and cancer using mass spectrometry-based proteomics in an exploratory cohort (n = 39). Fifteen promising protein candidates emerged to distinguish patient categories and were further confirmed by enzyme-linked immunosorbent assays (ELISA) in plasma samples from a validation cohort of 138 participants. Our predictive models demonstrated high classification performance with a minimal set of biomarkers. A four-protein panel (ARG1, CA2, F13A1, S100A12) achieved 94.1–98.2% AUROC (95% CI) for distinguishing cancer from non-cancer cases, while a five-protein panel (ARG1, CA2, HPT, MAN2A1, LBP) reached 97.3–99.5% AUROC (95% CI) for distinguishing cancer or preneoplasia from healthy or non-atrophic gastritis cases on the full cohort. Leveraging simple blood sampling, this strategy holds promise to detect high-risk gastric lesions, even at asymptomatic stages. Such an approach could significantly improve early detection and clinical management of GC, offering direct benefit for patients. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common chronic liver disorders worldwide and can lead to inflammation, fibrosis, and liver cancer. To better understand the impact of an unbalanced hypercaloric diet on liver phenotype in impaired autophagy, the study compared C57BL/6J wild type (WT) and MAPK15-ERK8 knockout (KO) male mice with C57BL/6J background fed for 17 weeks with “Western-type” (WD) or standard diet (SD). Liver features were monitored in vivo by high-frequency ultrasound (HFUS) using a semi-quantitative and parametric assessment of pathological changes in the parenchyma complemented by computer-aided diagnosis (CAD) methods. Liver histology was considered the reference standard. WD induced liver steatosis in both genotypes, although KO mice showed more pronounced dietary effects than WT mice. Overall, HFUS reliably detected steatosis-related parenchymal changes over time in the two mouse genotypes examined, consistent with histology. Furthermore, this study demonstrated the feasibility of extracting quantitative features from conventional B-mode ultrasound images of the liver in murine models at early clinical stages of MASLD using a computationally efficient and vendor-independent CAD method. This approach may contribute to the non-invasive characterization of genetically engineered mouse models of MASLD according to the principles of replacement, reduction, and refinement (3Rs), with interesting translational implications. Full article

Background: Diabetes mellitus is a significant primary global health concern that requires accurate diagnosis at an early stage to prevent severe complications. However, accurate prediction remains challenging due to limited, noisy, and imbalanced datasets. This study proposes a novel machine learning framework for improved diabetes prediction, addressing key challenges such as inadequate feature selection, class imbalance, and data preprocessing. Methods: This proposed work systematically evaluates five feature selection algorithms—Recursive Feature Elimination, Grey Wolf Optimizer, Particle Swarm Optimizer, Genetic Algorithm, and Boruta—using cross-validation and SHAP analysis to enhance feature interpretability. Classification is performed using two boosting algorithms: the light gradient boosting machine algorithm (LGBM) and the extreme gradient boosting algorithm (XGBoost). Results: The proposed framework, using the five most important features selected by the Boruta feature selection algorithm, outperformed other configurations with the LightGBM classifier, achieving an accuracy of 85.16%, an F1-score of 85.41%, and a 54.96% reduction in training time. Conclusions: Additionally, we have benchmarked our approach against recent studies and validated its effectiveness on both the Pima Indian Diabetes Dataset and the newly released DiaHealth dataset, demonstrating robust and accurate early diabetes detection across diverse clinical datasets. This approach offers a cost-effective, interpretable, and clinically relevant solution for early diabetes detection by reducing the number of input features, providing transparent feature importance, and achieving high predictive accuracy with efficient model training. Full article

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization. Full article

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article

Background: Operational cycle detection underpins a range of important tasks, such as predictive maintenance, energy consumption prediction, and energy management for mobile equipment in mining. Yet, no review has investigated the landscape of methods that segment mobile mining vehicle telemetry into discrete operating modes—a task termed operational cycle detection. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Scoping Review extension (PRISMA-ScR) framework, we searched The Lens database on 27 June 2025, for records published between 2000 and 2025 that apply cycle detection to mobile mining vehicles. After de-duplication and two-stage screening, 20 empirical studies met all criteria (19 diesel, 1 electric-drive). Due to the sparse research involving battery electric vehicles (BEVs) in mining, three articles performing cycle detection on heavy-duty vehicles in a similar operational context to mining are synthesized. Results: Early diesel work used single-sensor thresholds, often achieving >90% site-specific accuracy, while recent studies increasingly employ neural networks using multivariate datasets. While the cycle detection research on mining BEVs, even supplemented with additional heavy-duty BEV studies, is sparse, similar approaches are favored. Conclusions: Persisting gaps in the literature include the absence of public mining datasets, inconsistent evaluation metrics, and limited real-time validation. Full article

The diagnosis of behavioral variant of frontotemporal dementia (bvFTD)—a common cause of early-onset dementia—remains challenging due to a lack of determined biomarkers. 18F-fluorodeoxyglucose-positron emission tomography (FDG–PET) scan detects early glucose metabolism alterations in specific brain regions. The detection of distinct hypometabolic patterns in early stages of bvFTD has established FDG–PET as an indispensable adjunctive diagnostic tool in inconclusive cases, as well as in distinguishing between different types of dementia. Moreover, its role in the differential diagnosis of the often overlapping bvFTD and primary psychiatric disorders (PPD) is being studied by exploring disease-specific hypometabolic areas. Finally, the identification of early metabolic alterations and even earlier alterations in distinct metabolic brain networks may assist the diagnosis of presymptomatic carriers of disease-related gene mutations and lead to the development of novel biomarkers. The aim of our review is to underscore the role of FDG–PET as an approved yet promising tool that may lead to a new era in the diagnosis of bvFTD by establishing novel biomarkers and integrating AI as an assistant modality to inform diagnosis and decision-making. Full article

Background/Objectives: Representing the most common malignancy worldwide, skin cancer requires timely detection to improve prognosis. Both educational level of the patients and health literacy are important variables in terms of prevention and diagnosis in early stages of the disease, but data from Central and Eastern Europe are limited. Methods: We realized a prospective observational study that included 76 patients who were diagnosed with skin cancer and treated at the “Prof. Dr. I. Chiricuță” Institute of Oncology in Cluj-Napoca, Romania. Demographic, clinical, histopathological, and psychosocial data were collected in a standardized form. The primary aim was the measurement of diagnostic delay, defined as the interval since symptom onset until diagnosis. Secondary variables included education level, place of residence, participation in awareness campaigns and understanding capacity. Statistical analyses were applied. Results: The mean age in the cohort was 58.3 years; 52.6% were male and 84.2% were urban residents. The most frequent histological type was melanoma (47.4%), followed by basal cell carcinoma (36.8%), and squamous cell carcinoma (10.5%). The median delay in diagnostic was equal to 3 weeks. Education level was significantly related to earlier presentation (Kruskal–Wallis, p = 0.043), with shorter delays noticed in patients with university or postgraduate degrees (compared to those with secondary education). However, there were no significant differences between patients with rural and urban provenience (p = 0.483). Patients’ capacity of understanding showed no correlation with diagnostic delay, but their prior participation in awareness campaigns was strongly associated with higher comprehension (p < 0.001). Also, skin self-examination did not significantly impact time to diagnosis (p = 0.86). Conclusions: Higher levels of education and patients’ exposure to awareness campaigns might represent predictors of shorter diagnostic delay, highlighting the impact of public health initiatives and targeted educational strategies to improve early detection of skin cancers in Romania. However, the findings must be interpreted in light of the study’s limitations, namely the relatively small sample size and single-center design. Full article

Background/Objectives: Chronic kidney disease (CKD) is a progressive condition that affects the body’s ability to remove waste and regulate fluid and electrolytes. Early detection is crucial for delaying disease progression and initiating timely interventions. Machine learning (ML) techniques have emerged as powerful tools for automating disease diagnosis and prognosis. This study aims to evaluate the predictive performance of individual and ensemble ML algorithms for the early classification of CKD. Methods: A clinically annotated dataset was utilized to categorize patients into CKD and non-CKD groups. The models investigated included Logistic Regression, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Ridge Classifier, Naïve Bayes, K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Ensemble learning strategies. A systematic preprocessing pipeline was implemented, and model performance was assessed using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Results: The empirical findings reveal that ML-based classifiers achieved high predictive accuracy in CKD detection. Ensemble learning methods outperformed individual models in terms of robustness and generalization, indicating their potential in clinical decision-making contexts. Conclusions: The study demonstrates the efficacy of ML-based frameworks for early CKD prediction, offering a scalable, interpretable, and accurate clinical decision support approach. The proposed methodology supports timely diagnosis and can assist healthcare professionals in improving patient outcomes. Full article

Background/Objectives: The neonatal and infant periods represent a critical window for brain development, characterized by rapid and heterogeneous processes such as myelination and cortical maturation. Accurate assessment of these changes is essential for understanding normative trajectories and detecting early abnormalities. While conventional MRI provides valuable insights, automated classification remains challenging due to overlapping developmental stages and sex-specific variability. Methods: We propose SEPoolConvNeXt, a novel deep learning framework designed for fine-grained classification of neonatal brain development using T1- and T2-weighted MRI sequences. The dataset comprised 29,516 images organized into four subgroups (T1 Male, T1 Female, T2 Male, T2 Female), each stratified into 14 age-based classes (0–10 days to 12 months). The architecture integrates residual connections, grouped convolutions, and channel attention mechanisms, balancing computational efficiency with discriminative power. Model performance was compared with 19 widely used pre-trained CNNs under identical experimental settings. Results: SEPoolConvNeXt consistently achieved test accuracies above 95%, substantially outperforming pre-trained CNN baselines (average ~70.7%). On the T1 Female dataset, early stages achieved near-perfect recognition, with slight declines at 11–12 months due to intra-class variability. The T1 Male dataset reached >98% overall accuracy, with challenges in intermediate months (2–3 and 8–9). The T2 Female dataset yielded accuracies between 99.47% and 100%, including categories with perfect F1-scores, whereas the T2 Male dataset maintained strong but slightly lower performance (>93%), especially in later infancy. Combined evaluations across T1 + T2 Female and T1 Male + Female datasets confirmed robust generalization, with most subgroups exceeding 98–99% accuracy. The results demonstrate that domain-specific architectural design enables superior sensitivity to subtle developmental transitions compared with generic transfer learning approaches. The lightweight nature of SEPoolConvNeXt (~9.4 M parameters) further supports reproducibility and clinical applicability. Conclusions: SEPoolConvNeXt provides a robust, efficient, and biologically aligned framework for neonatal brain maturation assessment. By integrating sex- and age-specific developmental trajectories, the model establishes a strong foundation for AI-assisted neurodevelopmental evaluation and holds promise for clinical translation, particularly in monitoring high-risk groups such as preterm infants. Full article