Search Results (132,266)

Cobalamin, also known as vitamin B12, is an essential cofactor involved in one-carbon metabolism, mitochondrial function, and epigenetic regulation. As humans rely entirely on dietary intake of cobalamin paired with a highly coordinated absorption and transportation system, disruptions to this metabolic process can have profound health consequences. Breast cancer, the most frequently diagnosed malignancy among women worldwide, exhibits distinct metabolic adaptations, including altered cobalamin uptake and dependency on B12-driven biochemical pathways. This review summarizes the molecular mechanisms governing cobalamin metabolism, with a focus on absorption, transport, and intracellular processes relevant to breast cancer biology. We then examine how breast cancer cells reprogram these pathways. Finally, we evaluate emerging pharmaceutical strategies that target cobalamin metabolism, including B12-based imaging probes, cobalamin-conjugated drug delivery systems, and inhibitors of B12-dependent enzymes. Although these approaches show promise, further research is needed to define subtype-specific metabolic signatures, optimize cobalamin-mediated drug targeting, and clarify how systemic B12 status influences therapeutic response. By integrating biochemical, epidemiological, and translational perspectives, this review outlines how cobalamin-centered strategies may contribute to more precise diagnostic and therapeutic options for breast cancer. Full article

Background: The aim of this study was to develop an artificial intelligence (AI)-based decision support model for evaluating the extraction indication of permanent first molars in pediatric patients using panoramic radiographs, and to investigate the potential contribution of this model to the clinical decision-making process. Methods: This retrospective observational study analyzed 1000 panoramic radiographs obtained from children aged 8–10 years who attended the Clinics of Batman University Faculty of Dentistry for routine dental examination. Among the radiographs meeting the inclusion criteria, a total of 176 panoramic images were selected based on dental maturation assessment using the Demirjian tooth development staging system. Cases in which the permanent second molar was classified as Demirjian stages E–F were labeled as “extraction indication present”, while the remaining stages were labeled as “extraction indication absent”. A balanced dataset was created, consisting of 88 cases in each group. Image features were extracted using Gabor filters and Histogram of Oriented Gradients (HOG). The selected features were analyzed using a Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (ROC–AUC). Results: The proposed Gabor–HOG–SVM-based AI model achieved an overall classification accuracy of 77.78% with an AUC value of 0.77 in distinguishing between “extraction indication present” and “extraction indication absent” cases. For the extraction-indicated group, the sensitivity was 0.81 and the F1-score was 0.79, whereas for the non-indicated group, the sensitivity and F1-score were 0.74 and 0.77, respectively. No statistically significant differences were observed between the groups in terms of age or sex distribution (p > 0.05). Conclusions: This study demonstrates that artificial intelligence-based analysis of panoramic radiographic images can provide an objective and reproducible decision support approach for evaluating extraction indications of permanent first molars in pediatric patients. The proposed model should be considered as an adjunctive tool to reduce observer-dependent variability rather than a replacement for clinical judgment, and its clinical applicability should be further validated through multicenter and multi-parametric studies. Full article

Grounded in Narrative Transportation Theory, this study examines how AI-enabled cultural storytelling influences tourists’ visit intentions through the mediating roles of perceived authenticity and destination image. Drawing on a quantitative, cross-sectional design, data were collected from 415 tourists who had experienced AI-driven storytelling. PLS-SEM was employed to examine the relationships among AI-enabled cultural storytelling, perceived authenticity, destination image, and visit intention. The results indicate that AI-enabled cultural storytelling significantly enhances tourists’ perceived authenticity, destination image, and intention to visit. Both perceived authenticity and destination image were found to positively influence visit intention and act as significant mediators in the relationship between AI-enabled cultural storytelling and visit intention. These findings suggest that AI-driven narrative experiences not only enrich tourists’ perception of authenticity and overall image of the destination but also play a crucial role in shaping their future behavioral intentions. The study contributes to the understanding of technology-mediated cultural tourism experiences and provides practical insights for destination marketers seeking to leverage AI storytelling to attract and engage visitors. Full article

Hepatic neuroendocrine carcinoma (NEC) in dogs is a rare malignancy with limited therapeutic guidance and no established standard of care. This report describes an adaptive, clinically guided multimodal treatment approach in a dog with advanced hepatic NEC with regional lymph node involvement. Sequential systemic therapies—including doxorubicin, mitoxantrone with lomustine and prednisolone, and subsequently toceranib—were administered based on clinical response assessment using imaging (VCOG RECIST), hematologic monitoring, and quality-of-life evaluation. Ex vivo drug sensitivity testing (DST) was performed to provide functional reference information but was interpreted as supportive rather than predictive. Notably, discordance was observed between strong in vitro sensitivity to doxorubicin and early clinical progression, underscoring the limitations of monoculture-based assays in recapitulating in vivo tumor biology. Sustained stable disease was observed following transition to toceranib with continued adjunct immunomodulatory therapy; however, the independent contribution of each treatment component cannot be determined. This case highlights the feasibility of iterative treatment refinement in rare canine malignancies and emphasizes that DST findings should be integrated cautiously within a broader clinical decision-making framework. Full article

In the context of fierce competition among tourist destinations and increasing difficulty of differentiation, developing a strong destination image is particularly important. A comprehensive understanding of how tourists perceive destinations through user-generated images can help destination management organizations (DMOs) design more effective marketing strategies. This is especially relevant for destinations with spatially and temporally dispersed tourism resources and strong seasonal dynamics. This paper analyses inbound tourist photographs by combining deep learning techniques with spatial analysis to examine the spatial and temporal distribution of photo scenes and shifts in scene preferences among tourists. The study focuses on three distinct types of destinations in Slovenia—urban (Ljubljana), nature-based/alpine (Bled), and coastal (Piran, Izola, Koper)—providing insights into how image-based spatial scene analysis can inform destination marketing strategies. The results reveal significant spatial and temporal heterogeneity of scenes across micro destinations. Nature-based destinations exhibit lower topic entropy and fewer topic changes per user, whereas urban destinations show higher variability, with users changing topics on average five times per day. Seasonal effects are moderate: nature-based destinations display lower topic entropy in winter and higher in autumn and spring, coastal destinations show less pronounced seasonal variation, and urban destinations show almost none. These findings provide valuable insights into the spatial and temporal distribution of tourist interests and offer practical guidance for DMOs in strategic marketing planning. Full article

Metadata extraction from natural history collection labels is a pivotal task for the online publication of digitized specimens. However, given the scale of these collections—which are estimated to host more than 2 billion specimens worldwide, including ca. 400 million herbarium specimens—manual metadata extraction is an extremely time-consuming task. Thus, automated data extraction from digital images of specimens and their labels therefore is a promising application of state-of-the-art computer vision techniques. Extracting information from herbarium specimen labels normally involves three main steps: text segmentation, multilingual and handwriting recognition, and data parsing. The primary bottleneck in this workflow lies in the limitations of Optical Character Recognition (OCR) systems. This study explores how the general knowledge embedded in multimodal Transformer models can be transferred to the specific task of herbarium specimen label digitization. The final goal is to develop an easy-to-use, end-to-end solution to mitigate the limitations of classic OCR approaches while offering greater flexibility to adapt to different label formats. Donut-base, a pre-trained visual document understanding (VDU) transformer, was the base model selected for fine-tuning. A dataset from the University of Pisa served as a test bed. The initial attempt achieved an accuracy of 85%, measured using the Tree Edit Distance (TED), demonstrating the feasibility of fine-tuning for this task. Cases with low accuracies were also investigated to identify limitations of the approach. In particular, specimens with multiple labels, especially if combining handwritten and typewritten text, proved to be the most challenging. Strategies aimed at addressing these weaknesses are discussed. Full article

While advanced imaging techniques and ordered porous materials like MOFs have gained prominence, gas sorption remains the indispensable tool for characterizing the multiscale heterogeneity of industrially important disordered solids, such as catalysts and shales. This review examines recent developments in gas sorption methodologies specifically tailored for rigid, disordered porous media. We discuss experimental advances, including the choice of adsorbate and the utility of the overcondensation method for probing macroporosity and ensuring saturation. Furthermore, we critically evaluate theoretical approaches for determining pore size distributions (PSDs), contrasting classical methods with Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulations. Special emphasis is placed on the impact of pore-to-pore cooperative effects, such as advanced condensation, cavitation, and pore-blocking, on the interpretation of sorption isotherms. We highlight how complementary techniques, including integrated mercury porosimetry, NMR, and computerized X-ray tomography (CXT), are essential for deconvolving these complex network effects and validating void space descriptors. We conclude that, while “brute force” molecular simulations on image-based reconstructions are progressing, “minimalist” pore network models, which incorporate cooperative mechanisms, currently offer the most empirically adequate approach. Ultimately, gas sorption remains unique in its ability to statistically characterize void spaces from Angstroms to millimeters in a single experiment. Full article

Medical image segmentation is essential for clinical decision-making, treatment planning, and disease monitoring. However, ambiguous boundaries and complex anatomical structures continue to pose challenges for accurate segmentation. To address these issues, we propose DCANet (Disentangled and Category-Aware Network), a novel framework that effectively integrates local and global feature representations while enhancing category-aware feature interactions. In DCANet, features from convolutional and Transformer layers are fused using the Feature Coupling Unit (FCU), which aligns and combines local and global information across multiple semantic levels. The Decoupled Feature Module (DFM) then separates high-level representations into multi-class foreground and background features, improving discriminability and mitigating boundary ambiguity. Finally, the Category-Aware Integration Aggregator (CAIA) guides multi-level feature fusion, emphasizes critical regions, and refines segmentation boundaries. Extensive experiments on four public datasets—Synapse, ACDC, GlaS, and MoNuSeg—demonstrate the superior performance of DCANet, achieving Dice scores of 84.80%, 94.07%, 94.60%, and 79.85%, respectively. These results confirm the effectiveness and generalizability of DCANet in accurately segmenting complex anatomical structures and resolving boundary ambiguities across diverse medical image segmentation tasks. Full article

Finding diabetic foot ulcers (DFUs) early and accurately is essential for improving patients’ quality of life and lowering the risk of amputation. RGB images, commonly used in automated DFU detection, have limitations such as lighting variations, color inconsistencies, and inability to directly reflect physiological information. Background/Objectives: Although thermal images can capture temperature anomalies associated with inflammation and circulatory disorders, they cannot provide consistent performance due to their low spatial resolution and limited availability in clinical datasets. Furthermore, the lack of paired RGB–thermal image pairs makes it difficult to develop effective multimodal deep learning models. Methods: This study proposes a two-stage multimodal deep learning approach to overcome these limitations. In the first stage, an RGB2T-cGAN (RGB to Thermal cGAN) model based on pix2pix was designed to generate synthetic thermal representations from RGB images that resemble clinical patterns, thereby addressing the missing modality problem. In the second stage, the Multimodal Dual-Stream Multi-Head Cross-Attention (MDS-MHCA) classifier model was developed, which processes DFU RGB and generated synthetic thermal images through separate streams, enabling the dynamic modeling of complementary information across modalities. Results: The proposed MDS-MHCA model achieved 99.06% accuracy, 99.09% recall, and 99.06% F1-score on the test set, demonstrating a clear advantage over models based solely on RGB (91.51% accuracy) or thermal (96.23% accuracy) modalities. Furthermore, patient-based 10-fold GroupKFold cross-validation results demonstrate that the model offers high generalization capability across different patient groups, with an average accuracy of 96.49 ± 1.04 and an AUC value of 0.9927 ± 0.0067. Conclusions: The findings reveal that the proposed approach, through the integration of synthetic thermal information and cross-attention-based multimodal fusion, overcomes the fundamental limitations of single-modality-based systems and offers a DFU detection system that is more robust and reliable and holds potential for integration into clinical decision support systems. Full article

In the internal force analysis of plane frames, traditional mechanics solutions require the cumbersome derivation of equations and complex numerical calculations, a process that is both time-consuming and error-prone. While general-purpose Finite Element Analysis (FEA) software offers rapid and precise calculations, it is limited by tedious modeling pre-processing and a steep learning curve, making it difficult to meet the demand for rapid and intelligent solutions. To address these challenges, this paper proposes a deep learning-based automatic solution method for plane frames, enabling the extraction of structural information from printed plane structural schematics and automatically completing the internal force analysis and visualization. First, images of printed plane frame schematics are captured using a smartphone, followed by image pre-processing steps such as rectification and enhancement. Second, the YOLOv8 algorithm is utilized to detect and recognize the plane frame, obtaining structural information including node coordinates, load parameters, and boundary constraints. Finally, the extracted data is input into a static analysis program based on the Matrix Displacement Method to calculate the internal forces of nodes and elements, and to generate the internal force diagrams of the frame. This workflow was validated using structural mechanics problem sets and the analysis of a double-span portal frame structure. Experimental results demonstrate that the detection accuracy of structural primitives reached 99.1%, and the overall solution accuracy of mechanical problems in the final test set exceeded 90%, providing a more convenient and efficient computational method for the analysis of plane frames. Full article

This study presents a fully automatic hybrid framework for circle detection and geometric feature extraction from anteroposterior (AP) X-ray images. Detecting circular structures in X-ray imagery is challenging due to low contrast, noise, and metal-induced artifacts, which often limit the robustness of purely learning-based or purely geometric approaches. To address these challenges, a hybrid deep learning and computer vision pipeline is proposed that combines data-driven region localization with robust geometric fitting. A YOLOv5-based detector is first employed to identify a compact region of interest (ROI) containing circular components. Within this ROI, edge-based processing using Canny detection is applied, followed by an Edge-Snap refinement stage and robust RANSAC-based circle fitting with a Hough-transform fallback to ensure anatomically plausible circle estimation. The resulting circle centers and radii provide stable geometric parameters that can be consistently extracted across images with varying contrast, noise levels, and prosthesis appearances. The applicability of the proposed framework is demonstrated through a case study on hip prosthesis wear analysis, where the automatically detected circle parameters are used to compute medial, superior, and resultant displacement components using established two-dimensional radiographic formulations. Experimental evaluation on AP hip radiographs shows that the YOLOv5 detector achieves high ROI localization performance (mAP@0.5 = 0.971) and that the hybrid pipeline produces consistent circle parameters across longitudinal image sequences. Overall, the proposed method provides an end-to-end automatic solution for robust circle detection in X-ray imagery, with hip prosthesis wear presented solely as a case study without clinical or diagnostic claims. Full article

Background/Objectives: Pre-eruptive intracoronal resorption (PEIR) in impacted or unerupted teeth often remains undiagnosed. The aim of this study was to investigate the prevalence of PEIR with the aid of cone beam computed tomography (CBCT) and propose a new three-dimensional (3D) classification for the analysis of the lesions. Methods: A total of 164 unerupted teeth diagnosed in CBCT scans, derived from an equivalent number of patients, were examined for the presence of PEIR, tooth type, angulation and position. A novel 3D classification system was proposed and all PEIR lesions were further classified. The classification system was used to stage PEIR lesions according to their extend from the enamel level apically, the circumferential spread and their proximity to the pulp chamber. Descriptive statistics were used to assess the prevalence and type of resorption. The association between PEIR, demographics, tooth type, position and angulation were studied. The estimation of the multivariate relationship between PEIR, patient’s demographics and tooth characteristics was conducted with the multiple binary logistic regression model. Results: The prevalence of PEIR was 33.5%, affecting mostly maxillary canines, and maxillary and mandibular molars. The prevalence of PEIR in ages over 45 years was significantly higher (p < 0.001). The presence of PEIR was significantly associated with buccal position (p = 0.002) and buccal angulation (p = 0.016) of the tooth. Conclusions: Due to the high prevalence of PEIR, CBCT may improve detection and 3D characterization when imaging is already clinically indicated, and influence treatment planning in selected cases. Full article

Background/Objectives: Acute heart failure (AHF) is a common critical condition in emergency departments, and traditional diagnostic methods have limitations, including high subjectivity and limited accuracy. This study aimed to develop an integrated machine learning model based on lung ultrasound (LUS) radiomics and clinical data for diagnosing AHF in patients presenting with acute dyspnea. Methods: A total of 301 patients were included and randomly split into training (n = 210) and testing (n = 91) sets. Using PyRadiomics 3.0, 107 radiomics features were extracted from standardized 6-zone LUS images, combined with 52 clinical features. Three random forest models were developed: clinical-only, radiomics-only, and integrated models. Results: The integrated model achieved optimal performance on the testing set with an AUC of 0.976 (95% CI: 0.950–0.994), accuracy of 90.1%, sensitivity of 91.1%, and specificity of 89.1%, significantly outperforming the radiomics model (AUC 0.940, p = 0.046) and clinical model (AUC 0.931, p = 0.111). Feature importance analysis revealed that radiomics features contributed 75.6% of the model’s predictive power, with gray level run length matrix (GLRLM) features dominating the top-ranked features. Conclusions: As a proof-of-concept study, this research demonstrates the potential value of multimodal data fusion strategies for AHF diagnosis in the emergency department; however, external validation and prospective studies are required to further confirm its clinical applicability. Full article

Laryngeal squamous cell carcinoma (SCC) remains a major clinical challenge due to substantial mortality and limited preoperative risk stratification. Narrow-Band Imaging (NBI) enables real-time visualization of mucosal microvasculature, yet the molecular correlates of high-risk NBI phenotypes in vocal fold lesions are incompletely defined. In a prospective cohort of 145 patients with vocal fold lesions, NBI microvascular patterns were graded using the Ni classification and dichotomized using a pre-specified high-risk threshold (Ni ≥ 4 vs. Ni ≤ 3). Histopathology was classified according to WHO 2017. Epithelial expression of IL-6, LIF, LIFR and CXCL9 was quantified by immunohistochemistry using the immunoreactive score (IRS). Associations were tested using non-parametric methods and logistic regression, and diagnostic performance was assessed by ROC analysis. SCC was diagnosed in 63/145 cases. The Ni category showed a strong stepwise association with WHO 2017 histopathological severity. Using Ni ≥ 4, diagnostic performance for SCC was balanced (sensitivity 82.5%, specificity 82.9%; accuracy 82.8%). LIF and LIFR expression decreased with increasing histopathological severity and higher-NBI-risk categories, whereas CXCL9 increased with more suspicious NBI patterns; epithelial IL-6 did not differ across lesion categories. In multivariable logistic regression, Ni ≥ 4 was the strongest independent predictor of SCC (adjusted OR 8.90), while higher LIF (adjusted OR 0.73) and LIFR (adjusted OR 0.78) were independently associated with lower odds of SCC (model AUC 0.943). Multivariable analysis confirmed NBI as the strongest independent predictor of carcinoma, while epithelial LIF and LIFR expression showed inverse associations with histological malignancy and high-risk NBI vascular patterns. LIF/LIFR and CXCL9 show distinct, biologically plausible associations with NBI risk phenotypes, suggesting that selected tissue markers may complement NBI for refined SCC risk stratification. Full article

Background: Sex-related differences in cardiovascular disease outcomes are well recognized. Their impact on short-term outcomes in acute pulmonary embolism (PE) remains unclear. This study aimed to assess the association between sex and in-hospital outcomes in patients with acute PE. Methods: We performed a retrospective observational cohort study including 322 consecutive adult patients with acute PE admitted to a university hospital. Clinical, hemodynamic, laboratory, and imaging data were collected at presentation. The primary outcome was a composite poor outcome defined as intensive care unit (ICU) admission, systemic thrombolysis, or in-hospital mortality. Multivariable logistic regression analysis was used to evaluate whether sex independently predicted adverse outcomes after adjustment for established prognostic factors. Results: This study included 322 patients with acute pulmonary embolism (mean age 64.4 ± 13.1 years), of whom 50.0% were women. The composite poor outcome occurred more frequently in women than in men (34.0% vs. 22.7%, p = 0.032). Female sex was associated with increased odds of poor outcome in univariate analysis (odds ratio (OR) 1.76; 95% confidence interval (CI) 1.08–2.88). This association remained significant after multivariable adjustment (adjusted OR 1.69; 95% CI 1.02–2.82; p = 0.042). No significant sex differences were observed for individual components of the composite endpoint. Conclusions: Female sex was independently associated with a higher risk of adverse in-hospital outcomes in acute PE, suggesting that sex-specific factors may influence early prognosis and should be considered in future risk stratification models. Full article