Search Results (4,416)

Abdominal aortic aneurysm refers to the irreversible abnormal dilation of the aorta at the abdominal level, and it is acknowledged as one of the leading causes of mortality on a global scale. Most abdominal aortic aneurysms are asymptomatic until they approach the point of rupture; thus, it is essential to establish an efficient workflow for the accurate detection of this condition to enhance clinical outcomes. The incorporation of artificial intelligence learning algorithms into healthcare workflows holds the prospect of significantly improving the accuracy of decision-making related to patient mortality risk. Since the potential surgical repair of an aortic aneurysm depends upon the maximum external diameter of the aneurysm, this study aims to develop an end-to-end algorithmic method for classifying low-risk and high-risk cases based on abdominal aortic aneurysm data. To perform the predictive analysis, we adopt neuro-fuzzy systems, ensembles of neuro-fuzzy systems, and hybrid evolutionary-based fuzzy classifiers. The models are trained using features extracted from the radiomics framework and exhibit high generalisation performance, as measured by the adopted metrics, and estimated on a K-fold cross-validation basis. Numerical studies further reveal that the hybrid evolutionary-based fuzzy system exhibits exceptional accuracy in distinguishing between the two identified classes. Full article

Dilated cardiomyopathy (DCM) is a major contributor to heart failure and cardiac transplantation. This study investigated the metabolic potential of human myocardium-derived mesenchymal stem/stromal cells (hmMSCs) and subsequently cardiac sphere-derived cells (SDCs) obtained from healthy (non-dilated) and pathological (dilated) myocardial tissues. hmMSCs were isolated using the explant outgrowth method and expanded in monolayer culture. Small round cells loosely attached on hmMSCs were harvested and cultivated as cardiac spheroids for 1–3 days, subsequently obtaining SDCs. The cell morphology, proliferation rate, mitochondrial activity, and intracellular calcium levels were analyzed using flow cytometry, Seahorse metabolic assays, and spectrophotometry, while expression of cell progenitor and cardiac commitment genes were analyzed by quantitative PCR. Both healthy and pathological SDCs demonstrated significantly enhanced mitochondrial function—reflected by increased maximal respiration, ATP production, and coupling efficiency—along with reduced steady-state intracellular calcium levels compared with hmMSCs. SDCs from both healthy and dilated myocardium showed marked upregulation of several cardiac progenitor and lineage-commitment genes relative to hmMSCs. SDCs derived from both healthy and dilated myocardiums possess a more favorable metabolic, progenitor and cardiac commitment profile than conventional hmMSCs. hmMSCs and SDCs from dilated myocardium retain residual metabolic potential, which may be further enhanced under 3D culture conditions. Full article

Recently, deep learning methods have seen major advancements and are preferred for medical image analysis. Clinically, deep learning techniques for cancer image analysis are among the main applications for early diagnosis, detection, and treatment. Consequently, segmentation of breast histology images is a key step towards diagnosing breast cancer. However, the use of deep learning methods for image analysis is constrained by challenging features in the histology images. These challenges include poor image quality, complex microscopic tissue structures, topological intricacies, and boundary/edge inhomogeneity. Furthermore, this leads to a limited number of images required for analysis. The U-Net model was introduced and gained significant traction for its ability to produce high-accuracy results with very few input images. Many modifications of the U-Net architecture exist. Therefore, this study proposes the watershed encoder–decoder neural network (WEDN) to segment cancerous lesions in supervised breast histology images. Pre-processing of supervised breast histology images via augmentation is introduced to increase the dataset size. The augmented dataset is further enhanced and segmented into the region of interest. Data enhancement methods such as thresholding, opening, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted parts from the image. Consequently, further segmentation via the connected component analysis method is used to combine image pixel components with similar intensity values and assign them their respective labeled binary masks. The watershed filling method is then applied to these labeled binary mask components to separate and identify the edges/boundaries of the regions of interest (cancerous lesions). This resultant image information is sent to the WEDN model network for feature extraction and learning via training and testing. Residual convolutional block layers of the WEDN model are the learnable layers that extract the region of interest (ROI), which is the cancerous lesion. The method was evaluated on 3000 images–watershed masks, an augmented dataset. The model was trained on 2400 training set images and tested on 600 testing set images. This proposed method produced significant results of $98.53\%$ validation accuracy, $96.98\%$ validation dice coefficient, and $97.84\%$ validation intersection over unit (IoU) metric scores. Full article

This study describes the clinicopathological features of seven canine cases showing a diffuse cobblestone-like gastric mucosal pattern on endoscopy. Cases were retrospectively retrieved from endoscopic databases (2017–2025). Clinical data, treatment history, endoscopic findings, and histology were reviewed. Endoscopically, all dogs exhibited thickened, irregular, and poorly distensible gastric folds. Histopathologic examination showed mild-to-moderate foveolar hyperplasia, variable cystic dilation of the fundic glands, mild chronic lymphoplasmacytic inflammation, and interstitial fibrosis. Parietal-cell population was variably increased and predominant (hyperplasia). Because these features can overlap widely among reactive and hyperplastic gastropathies, interpretation required correlation with clinical and endoscopic findings in addition to histopathology. All dogs had a history of prolonged omeprazole administration, and most showed clinical improvement after dose reduction or treatment withdrawal. Follow-up endoscopy in two dogs documented divergent outcomes, with marked improvement in one dog and only minimal changes in the other. These findings suggest that this cobblestone-like pattern represents a benign, reactive, and potentially regressive gastropathy, possibly associated with chronic acid suppression. Recognition of this appearance may assist clinicians in differentiating reactive gastropathy from proliferative or neoplastic conditions and supports prudent use of long-term proton pump inhibitors in dogs with chronic gastrointestinal disease. Full article

Tracheostomal stenosis is a troublesome and distressing complication in laryngectomy. There are numerous techniques that describe dilatation of tracheostoma which are mostly performed under general anesthesia with the intermittent apnea technique. We report an alternative dilatation method using a Foley catheter for laryngectomee with stomal stenosis. One case was performed under high-frequency jet ventilation and the other case was carried out with a conventional anesthesia machine. The Foley catheter is used as a conduit for ventilation and the balloon on the Foley catheter was used as a dilatator. Full article

Dilation matrices are important in multiple subdivision and multiple multiresolution analysis, as they govern the process of data refinement and play a crucial role in capturing directional features. One common limitation in the existing methods is the relatively large determinant of their dilation matrices, leading to high computational and storage costs. To address this issue, this paper proposes a novel family of pairs of directional dilation matrices with determinant 3. Such dilation matrices satisfy the joint expansion property and directional sensitivity. The joint expansion property is verified via the joint spectral radius, while by connecting the action of the matrices to certain elliptic elements of $PSL(2,\mathbb{R})$, their directional adaptability can be established. Compared to most of the existing dilation matrices, the proposed ones achieve a balance between determinant and directional adaptability and provide a new insight into the construction of directional dilation matrices. This makes them suitable for addressing practical anisotropic problems. Full article

Background/Objectives: People often engage in small, repetitive movements—or “fidgeting”—while listening. This behavior has traditionally been regarded as a sign of inattention. However, recent perspectives suggest that these movements may support engagement and arousal regulation. Yet, little is known about how different types of fidgeting affect the allocation of cognitive resources during auditory processing. This study examined whether hand and leg fidgeting influence pupil-linked arousal and auditory task performance. Methods: Young, healthy adults aged 18–26 years completed four auditory processing tasks while performing either hand fidgeting (manipulating a small fidget toy) or leg fidgeting (very light ergometer pedaling). A control group did not fidget. Pupil-linked arousal was assessed using changes in pupil diameter, and listening performance was evaluated across tasks of varying difficulty. Results: Both forms of fidgeting caused pupil dilation compared to the control group, particularly in the case of Hand Fidgeting during the listening task with speech in noise and the fast speech task. Despite these physiological changes, there were no measurable differences in auditory task performance across conditions. Conclusions: Fidgeting modulates pupil-linked arousal without impairing auditory processing in young, healthy adults. Hand fidgeting may help sustain engagement during demanding listening tasks. However, because the fidgeting was intentional and task performance approached ceiling or floor levels, these findings should be interpreted as preliminary. Future studies should examine whether fidgeting supports arousal maintenance or listening performance in individuals with attentional vulnerabilities or auditory processing difficulties. Full article

Accurate wildlife detection in Unmanned Aerial Vehicle (UAV)-captured imagery is crucial for biodiversity conservation, yet it remains challenging due to the visual similarity of species, environmental disturbances, and the small size of target animals. To address these challenges, this paper introduces YOLO-WL, a wildlife detection algorithm specifically designed for UAV-based monitoring. First, a Multi-Scale Dilated Depthwise Separable Convolution (MSDDSC) module, integrated with the C2f-MSDDSC structure, expands the receptive field and enriches semantic representation, enabling reliable discrimination of species with similar appearances. Next, a Multi-Scale Large Kernel Spatial Attention (MLKSA) mechanism adaptively highlights salient animal regions across different spatial scales while suppressing interference from vegetation, terrain, and lighting variations. Finally, a Shallow-Spatial Alignment Path Aggregation Network (SSA-PAN), combined with a Spatial Guidance Fusion (SGF) module, ensures precise alignment and effective fusion of multi-scale shallow features, thereby improving detection accuracy for small and low-resolution targets. Experimental results on the WAID dataset demonstrate that YOLO-WL outperforms existing state-of-the-art (SOTA) methods, achieving 94.2% mAP@0.5 and 58.0% mAP@0.5:0.95. Furthermore, evaluations on the Aerial Sheep and AI-TOD datasets confirm YOLO-WL’s robustness and generalization ability across diverse ecological environments. These findings highlight YOLO-WL as an effective tool for enhancing UAV-based wildlife monitoring and supporting ecological conservation practices. Full article

Surface defect evaluation in steel production demands both high inference speed and accuracy for efficient production. However, existing methods face two critical challenges: (1) the diverse dimensions and irregular morphologies of surface defects reduce detection accuracy, and (2) computationally intensive feature extraction slows inference. In response to these challenges, this study proposes an innovative network based on dual-branch feature enhancement and downsampling (DFED-Net). First, an atrous convolution and multi-scale dilated attention fusion module (AMFM) is developed, incorporating local–global feature representation. By emphasizing local details and global semantics, the module suppresses noise interference and enhances the capability of the model to separate small-object features from complex backgrounds. Additionally, a dual-branch downsampling module (DBDM) is developed to preserve the fine details related to scale that are typically lost during downsampling. The DBDM efficiently fuses semantic and detailed information, improving consistency across feature maps at different scales. A lightweight dynamic upsampling (DySample) is introduced to supplant traditional fixed methods with a learnable, adaptive approach, which retains critical feature information more flexibly while reducing redundant computation. Experimental evaluation shows a mean average precision (mAP) of 81.5% on the Northeastern University surface defect detection (NEU-DET) dataset, a 5.2% increase compared to the baseline, while maintaining a real-time inference speed of 120 FPS compared to the 118 FPS of the baseline. The proposed DFED-Net provides strong support for the development of automated visual inspection systems for detecting defects on steel surfaces. Full article

This paper presents the design and feasibility evaluation of a low-cost, head-mounted pupil dilation tracking system based on computer vision. The proposed solution employs a standard webcam and active infrared illumination, enabling stable eye image acquisition under controlled lighting conditions. The developed image processing pipeline incorporates adaptive contrast enhancement and geometric pupil detection, allowing for the estimation of relative changes in pupil diameter in real time. System evaluation was conducted in a controlled experiment involving 24 participants performing an N-back task with emotional modulation, a well-established paradigm for eliciting task-evoked pupillary responses under constant working-memory demands. The results revealed statistically significant changes in relative pupil dilation in response to stimuli with varying emotional valence during a working memory task, confirming the system’s ability to capture task-evoked pupillary responses (TEPRs). The proposed system constitutes a low-cost research tool for studies of task engagement and physiological responses in the context of human–computer interaction and psychophysiology, with a focus on the analysis of functional pupilometric changes. Full article

Background: Caffeine, dairy products and other food items may influence retinal microcirculation. Retinal microvascular indices provide quantitative biomarkers of systemic microvascular health and are increasingly used in clinical and research settings. The aim of this study was to elucidate the possible effects of these food products on structural and functional indices of the retinal microcirculation. Methods: Based on a registered protocol, we identified eligible interventional/observational studies examining the association of these factors with retinal biomarkers, including central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), arteriolar-to-venular diameter ratio (AVR), retinal vascular tortuosity, vessel diameter index (VDI) and retinal vessel flicker light-induced dilation (FID). Results: Fourteen studies were included addressing caffeine (4), dairy products (2) and other food (9) consumption. Acutely, caffeine intake was dose-dependently associated with narrowed CRAE, CRVE, decreased VDI and increased AVR and FID. Long-term, caffeine consumption was associated with larger CRVE and lower AVR, while decaffeinated coffee with larger CRAE and AVR and narrower CRVE. Low-fat dairy products, fish and fiber were associated with larger CRAE, smaller CRVE, and increased AVR, while red meat consumption was associated with narrower CRAE and lower AVR. Increased salt intake was associated with increased venular tortuosity, while almond consumption was associated with larger CRVE. Owing to substantial study heterogeneity, a meta-analysis was not feasible. Conclusions: Potentially clinically meaningful associations between food groups and retinal indices were identified. These associations should be considered when evaluating retinal microcirculation and assessing CVD risk since modification of these factors may be beneficial for the cardiovascular system. Full article

Background/Objectives: Nailfold videocapillaroscopy (NVC) is a non-invasive method for visualizing systemic micro-circulation, primarily used in rheumatology. Many ocular diseases (e.g., glaucoma, diabetic retinopathy (DR), and central serous chorioretinopathy (CSC)) involve microvascular disturbances. Since microangiopathies are often systemic, NVC findings may reflect ocular pathology. This narrative review aimed to summarize current evidence linking NVC alterations with retinal, choroidal, and optic nerve diseases. Methods: A literature search of PubMed, Scopus, and Web of Science (2000–2025) was conducted using the keywords “nailfold videocapillaroscopy,” “ocular diseases,” “retinopathy,” and “glaucoma”. Results: Most available studies were cross-sectional and exploratory. In glaucoma, NVC abnormalities suggesting systemic hypoperfusion (reduced capillary density, avascular areas, tortuosity, and microhemorrhages) were frequently reported. CSC was associated with capillary dilation patterns (megacapillaries and aneurysmal dilations), supporting a congestive rather than ischemic microvascular profile. In DR, NVC abnormalities (reduced density and neoangiogenesis) correlated with DR severity. Associations were also found for AMD and idiopathic macular telangiectasia type 2 (MacTel2, also known as IMT). However, only a limited number of prospective studies assessed diagnostic performance, and data on sensitivity, specificity, or ROC-based validation remain scarce. Conclusions: Current evidence suggests that NVC reflects systemic microvascular alterations associated with several ocular diseases. While NVC shows potential as an adjunctive tool for risk assessment and phenotyping, its diagnostic validity has not yet been established. Limitations include the predominantly observational nature of the studies, heterogeneity of methodologies, and the lack of standardized diagnostic thresholds. Prospective trials integrating NVC with ocular imaging modalities, such as OCT angiography, are needed to determine its clinical utility. Full article

The mechanical response of geomembranes in hydraulic structures is strongly influenced by temperature variations, which alter both material stiffness and interface shear strength behavior. This study develops a non-linear, temperature-dependent tensile behavior constitutive model for a polyvinyl chloride (PVC) geomembrane and evaluates its implications for the stability of geomembrane-lined reservoir slopes. The empirical relationship was calibrated using tensile tests reported in literature for temperatures between 10 °C and 60 °C, reproducing the observed non-linear softening and modulus reduction with increasing temperature. A classical thermal dilation formulation was incorporated to simulate cyclic thermal expansion and contraction. The constitutive and thermal formulations were implemented in FLAC2D and applied to a 2H:1V covered geomembrane slope representative of dam lining systems. The results show that temperature-induced softening significantly increases tensile strain within the geomembrane. The model also shows that the lower surface interface friction angle of the geomembrane plays a significant role in the slope stability. Thermal cycle analysis demonstrates the accumulation of efforts resulting from the fatigue of the geomembrane. The proposed model provides a practical framework for incorporating thermo-mechanical coupling in design analyses and highlights the necessity of accounting for realistic thermal conditions in assessing the long-term stability of geomembrane-lined reservoirs. Full article

Decisions made by pilots and drivers suffering from depression can endanger the lives of hundreds of people, as demonstrated by the tragedies of Germanwings flight 9525 and Air India flight 171. Since the detection of depression is currently based largely on subjective self-reporting, there is an urgent need for fast, objective, and reliable detection methods. In our study, we present an artificial intelligence-based system that combines iris-based identification with the analysis of pupillometric and eye movement biomarkers, enabling the real-time detection of physiological signs of depression before driving or flying. The two-module model was evaluated based on data from 242 participants: the iris identification module operated with an Equal Error Rate of less than 0.5%, while the depression-detecting CNN-LSTM network achieved 89% accuracy and an AUC value of 0.94. Compared to the neutral state, depressed individuals responded to negative news with significantly greater pupil dilation (+27.9% vs. +18.4%), while showing a reduced or minimal response to positive stimuli (−1.3% vs. +6.2%). This was complemented by slower saccadic movement and longer fixation time, which is consistent with the cognitive distortions characteristic of depression. Our results indicate that pupillometric deviations relative to individual baselines can be reliably detected and used with high accuracy for depression screening. The presented system offers a preventive safety solution that could reduce the number of accidents caused by human error related to depression in road and air traffic in the future. Full article

Soil organic matter (SOM) is a key indicator for evaluating soil fertility and ecological functions, and hyperspectral technology provides an effective means for its rapid and non-destructive estimation. However, in practical soil systems, the spectral response of SOM is often highly covariant with mineral composition, moisture conditions, and soil structural characteristics. Under small-sample conditions, hyperspectral SOM modeling results are usually highly sensitive to spectral preprocessing methods, sample perturbations, and model architecture and parameter configurations, leading to fluctuations in predictive performance across independent runs and thereby limiting model stability and practical applicability. To address these issues, this study proposes a multi-strategy collaborative deep learning modeling framework for small-sample conditions (SE-EDCNN-DA-LWGPSO). Under unified data partitioning and evaluation settings, the framework integrates spectral preprocessing, data augmentation based on sensor perturbation simulation, multi-scale dilated convolution feature extraction, an SE channel attention mechanism, and a linearly weighted generalized particle swarm optimization algorithm. Subtropical red soil samples from Guangxi were used as the study object. Samples were partitioned using the SPXY method, and multiple independent repeated experiments were conducted to evaluate the predictive performance and training consistency of the model under fixed validation conditions. The results indicate that the combination of Savitzky–Golay filtering and first-derivative transformation (SG–1DR) exhibits superior overall stability among various preprocessing schemes. In model structure comparison and ablation analysis, as dilated convolution, data augmentation, and channel attention mechanisms were progressively introduced, the fluctuations of prediction errors on the validation set gradually converged, and the performance dispersion among different independent runs was significantly reduced. Under ten independent repeated experiments, the final model achieved R² = 0.938 ± 0.010, RMSE = 2.256 ± 0.176 g·kg⁻¹, and RPD = 4.050 ± 0.305 on the validation set, demonstrating that the proposed framework has good modeling consistency and numerical stability under small-sample conditions. Full article