Search Results (5,306)

Background and Objectives: This study aimed to assess the pattern and quantity of bone regeneration after mandibular setback surgery using a novel modification of the low Z plasty (NM-Low Z plasty) technique by measuring bone density (Hounsfield unit) at the osteotomy site over a 12-month postoperative period using cone-beam computed tomography (CBCT). Materials and Methods: This retrospective cohort study included six patients with skeletal Class III deformity who underwent bilateral sagittal split osteotomy (BSSO) setback using the NM-Low Z plasty technique between 2021 and 2023 at Thammasat University Hospital. CBCT images were obtained preoperatively and at 1 month, 6 months, and 12 months postoperatively. Bone density at the buccal, cancellous, and lingual aspects of the osteotomy gap was measured using Blue Sky Plan 4 software. The intraclass correlation coefficient was used to determine reliability. Descriptive statistics, repeated-measures analysis of variance and multiple linear regression analysis were performed for comparisons. Results: At 12 months postoperatively, bone density in all measured regions was not significantly different compared to the postoperative measurements, indicating sufficient bone regeneration. The cancellous and lingual cortical regions exhibited earlier recovery than the buccal cortex. No postoperative complications such as wound infection, delayed union, or non-union were reported. Conclusions: BSSO using the NM-Low Z plasty technique offers reliable bone healing outcomes with stable bone regeneration, thereby providing a viable alternative to conventional BSSO techniques. Radiographic evidence confirmed its clinical applicability and potential to reduce the incidence of postoperative complications. Full article

Photoacoustic imaging (PAI) integrates the high-contrast merits of optical imaging with the high-spatial-resolution advantages of acoustic imaging, enabling the acquisition of three-dimensional images with deep tissue penetration (up to several centimeters) for in vivo disease detection and diagnosis. Among various photoacoustic nanoagents, gold nanomaterials (GNMs) have been widely explored for the PAI-based imaging analysis and photothermal therapy of diseases, owing to their strong near-infrared (NIR) absorption, which can generate distinct photoacoustic signals in deep tissues. This review focuses on recent advances and achievements in the development of functionalized gold nanoprobes, including Janus gold nanoprobes, gold nanocomposite probes (such as functionally coated GNMs and GNMs-loaded nanocarriers), and gold nanoaggregate probes (e.g., pre-assembly of GNMs and in situ aggregation of GNMs). The multifunctionalization of GNMs can enhance their PAI performance by shifting absorption to the NIR-I and NIR-II regions, while simultaneously imparting additional functionalities such as targeted delivery to disease sites and specific responsiveness to disease biomarkers. These features can render functionalized GNMs-based nanoprobes highly suitable for PAI-based analysis and the precise detection of various pathological conditions, including bacterial infections, tumors, kidney injury, and disorders affecting the ocular, gastrointestinal, cardiovascular, visceral, and lymphatic systems. Finally, this review provides a concise summary of biosafety evaluation and outlines the current challenges and future perspectives in optimizing the GNMs-based PAI methods, highlighting their potential to enhance the rapid and precise diagnosis of diseases in the future. Full article

Surface soil moisture (SSM) is essential for crop growth, irrigation management, and drought monitoring. However, conventional field-based measurements offer limited spatial and temporal coverage, making it difficult to capture environmental variability at scale. This study introduces a multimodal soil moisture estimation framework that combines synthetic aperture radar (SAR), optical imagery, vegetation indices, digital elevation models (DEM), meteorological data, and spatio-temporal metadata. To strengthen model performance and adaptability, an intermediate fine-tuning strategy is applied to two datasets comprising 10,571 images and 3772 samples. This approach improves generalization and transferability across regions. The framework is evaluated across diverse agro-ecological zones, including farmlands, alpine grasslands, and environmentally fragile areas, and benchmarked against single-modality methods. Results with RMSE 4.5834% and $R^2$ 0.8956 show consistently high accuracy and stability, enabling the production of reliable field-scale soil moisture maps. By addressing the spatial and temporal challenges of soil monitoring, this framework provides essential information for precision irrigation. It supports site-specific water management, promotes efficient water use, and enhances drought resilience at both farm and regional scales. Full article

Urban scene classification in high-resolution remote sensing images is critical for applications such as power facility site selection and grid security monitoring. However, the complexity and variability of ground objects present significant challenges to accurate classification. While convolutional neural networks (CNNs) excel at extracting local features, they often struggle to model long-range dependencies. Transformers can capture global context but incur high computational costs. To address these limitations, this paper proposes a Global–Local Information Fusion Network (GLIFNet), which integrates VMamba for efficient global modeling with CNN for local detail extraction, enabling more effective fusion of fine-grained and semantic information. Furthermore, a Haar Wavelet Transform Attention Mechanism (HWTAM) is designed to explicitly exploit frequency-domain characteristics, facilitating refined fusion of multi-scale features. The experiment compared nine commonly used or most advanced methods. The results show that GLIFNet achieves mean F1 scores (mF1) of 90.08% and 87.44% on the ISPRS Potsdam and ISPRS Vaihingen datasets, respectively. This represents improvements of 1.26% and 1.91%, respectively, compared to the compared model. The overall accuracy (OA) reaches 90.43% and 92.87%, with respective gains of 2.28% and 1.58%. Experimental results on the LandCover.ai dataset demonstrate that GLIFNet achieved an mF1 score of 88.39% and an accuracy of 92.23%, exhibiting relative improvements of 0.3% and 0.28% compared with the control model. In summary, GLIFNet demonstrates advanced performance in urban scene classification from high-resolution remote sensing images and can provide accurate basic data for power construction. Full article

Partial discharge pattern recognition is a crucial task for assessing the insulation condition of Gas-Insulated Switchgear (GIS). However, the on-site environment presents challenges such as strong electromagnetic interference, leading to acquired signals with a low signal-to-noise ratio (SNR). Furthermore, traditional pattern recognition methods based on statistical parameters suffer from redundant and inefficient features that compromise classification accuracy, while existing artificial-intelligence-based classification methods lack the ability to quantify the uncertainty in defect classification. To address these issues, this paper proposes a novel GIS partial discharge pattern recognition method based on time–frequency energy grayscale maps and an improved variational Bayesian autoencoder. Firstly, a denoising-based approximate message passing algorithm is employed to sample and denoise the discharge signals, which enhances the SNR while simultaneously reducing the number of sampling points. Subsequently, a two-dimensional time–instantaneous frequency energy grayscale map of the discharge signal is constructed based on the Hilbert–Huang Transform and energy grayscale mapping, effectively extracting key time–frequency features. Finally, an improved variational Bayesian autoencoder is utilized for the unsupervised learning of the image features, establishing a GIS defect classification method with an associated confidence level by integrating probabilistic features. Validation based on measured data demonstrates the effectiveness of the proposed method. Full article

Objective: To evaluate the effect of low-concentration contrast media (LCCMs) on 100 kVp conventional chest CT by comparing the proportions of acceptable-quality scans obtained using various CM concentrations with those obtained using the standard protocol. Materials and methods: This prospective, multicenter, randomized controlled trial enrolled adult patients with BMI < 30 who underwent enhanced chest CT between December 2023 and September 2024. Participants were randomly assigned to four groups: one control group (120 kVp, 320 mgI/mL) and three test groups using 100 kVp and standard CM (320 mgI/mL) or LCCMs (270 or 240 mgI/mL). Non-inferiority was tested based on the proportion of acceptable-quality scans independently assessed by two readers. Adverse reactions, including injection site pain and localized and systemic heat sensations, were recorded. Results: A total of 371 participants (mean age: 66.0, IQR: 58–73 years) were randomized. 100 kVp chest CT with LCCM groups showed non-inferior image quality to the standard protocol (270 mgI/mL: reader 1—98.33% confidence interval [CI]: −4.95, 9.37 and reader 2–98.33% CI: −7.11, 7.21; 240 mgI/mL: reader 1–98.33% CI: −6.44, 8.71 and reader 2–98.33% CI: −11.47, 5.11; all p < 0.001). Reader 2 reported a lower proportion of acceptable scans in the 320 mgI/mL low-voltage group (p = 0.11), whereas Reader 1 did not find significant inferiority (p = 0.003). Injection site pain, localized heat, and systemic heat were less frequent in LCCM groups, while systemic heat was only statistically significant (p = 0.003). Conclusions: Low-tube-voltage (100 kVp) chest CECT using LCCMs yields non-inferior image quality compared with the standard protocol while using conventional concentration contrast media under 100 kVp resulted in decreased proportion of diagnostically acceptable scans. Full article

Background and Clinical Significance: Metastatic breast cancer is a major global health burden, with common metastatic sites including the bones, lungs, liver, and brain. Cardiac metastasis is rare and often clinically silent, leading to underdiagnosis. Recognizing cardiac involvement, even when asymptomatic, is important for understanding the full extent of disease and ensuring optimal patient care. Case Presentation: We report the case of a woman with advanced breast carcinoma who showed no clinical or imaging evidence of cardiac involvement throughout the course of her illness. Following her death from progressive metastatic disease, an autopsy revealed metastatic carcinoma infiltrating the myocardium and epicardium without gross cardiac abnormalities. Histological and immunohistochemical analysis confirmed the tumor’s origin from breast carcinoma. Conclusions: This case illustrates the potential for clinically occult cardiac metastasis in breast cancer and underscores the importance of pathological examination in detecting hidden metastatic sites. The absence of cardiac symptoms or imaging abnormalities highlights the diagnostic challenge of this rare manifestation and the need for greater awareness in managing advanced malignancies. Full article

Frequent cloud cover severely limits the use of optical remote sensing for continuous ecological monitoring. Synthetic aperture radar (SAR) offers an all-weather alternative, but translating SAR data to optical equivalents is challenging, particularly in cloudy regions where paired training data are scarce. To address this, we developed an enhanced CycleGAN (denoted by SA-CycleGAN) to derive a high-fidelity, temporally continuous normalized difference vegetation index (NDVI) from SAR imagery. The SA-CycleGAN introduces a novel spatiotemporal attention generator that dynamically computes global and local feature relationships to capture long-range spatial dependencies across diverse landscapes. Furthermore, a structural similarity (SSIM) loss function is integrated into the SA-CycleGAN to preserve the structural and textural integrity of the synthesized images. The performance of the SA-CycleGAN and three unsupervised models (DualGAN, GP-UNIT, and DCLGAN) was evaluated by deriving NDVI time series from Sentinel-1 SAR images across four sites with different vegetation types. Ablation experiments were conducted to verify the contributions of the key components in the SA-CycleGAN model. The results demonstrate that the SA-CycleGAN significantly outperformed the comparison models across all four sites. Quantitatively, the proposed method achieved the lowest Root Mean Square Error (RMSE) of 0.0502 and the highest Coefficient of Determination (R²) of 0.88 at the Zhangbei and Xishuangbanna sites, respectively. The ablation experiments confirmed that the attention mechanism and SSIM loss function were crucial for capturing long-range features and maintaining spatial structure. The SA-CycleGAN proves to be a robust and effective solution for overcoming data gaps in optical time series. Full article

Cereal leaf beetle (CLB, Oulema melanopus L.) is an important pest that damages cereals. Insecticide use against CLB could be reduced with targeted treatments. Our aims were to develop a methodology to map CLB damage on cereal fields using remote sensing. We investigated the suitability of four vegetation indices (VIs: the Visible Atmospherically Resistance Index (VARI), the Green Chromatic Coordinate (GCC), the Green Leaf Index (GLI), and the Normalized Green–Red Difference Index (NGRDI)) derived from RGB images (drone (UAV) imagery). Study sites were located in different regions of Hungary in 2024. Images were taken at different phenological stages of cereals. Suitability of VIs was analyzed with ANOVA and MANOVA. Machine learning models were developed to classify damaged field sections with random forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms. Results show that VARI, GCC, GLI, and NGRDI contain complementary features for early detection of CLB damage. Difference in sample points’ VI from field median is advantageous for the LGBM algorithm (F1_damaged = 0.64–0.72), while the best RF models were obtained with more features (F1_damaged = 0.66). Random test data splits had optimistic results (overall accuracy: RF = 0.63–0.80, LightGBM = 0.63–0.79) compared to spatially controlled test splits (overall accuracy: RF = 0.53–0.70, LightGBM = 0.53–0.62). Full article

Background: Pancreatic hamartoma (PH) is an exceptionally rare, benign, mass-forming lesion accounting for less than 1% of all pancreatic tumors. Its rarity and non-neoplastic nature contribute to significant diagnostic challenges, often leading to misclassification as malignant disease. This study presents a case of PH and a systematic review of all reported cases, with emphasis on histopathological and imaging characteristics. Methods: A comprehensive electronic search of PubMed, Scopus, and Web of Science was conducted up to 1 April 2025, to identify eligible case reports and series. Results: We describe a 37-year-old woman with a cystic lesion of the pancreatic tail, ultimately confirmed histologically as a cystic pancreatic hamartoma following distal pancreatectomy with splenectomy, with an uneventful postoperative course. Of 687 screened studies, 51 met the inclusion criteria, comprising 77 cases (68 adults, 9 pediatric). PHs occurred most frequently in males (52.9%), with a mean age of 59.5 ± 12.9 years, and were often asymptomatic (57.4%). The pancreatic head was the most common site (52.9%). On MRI, PHs typically exhibited low T1-weighted and high T2-weighted signal intensity, with no FDG uptake (82%) and moderate or no restriction on DWI, distinguishing them from neuroendocrine tumors (NETs). Histologically, most lesions were solid (64.7%) or solid–cystic (35.3%), with low spindle cell cellularity and absent Langerhans islets. Conclusions: Low T1WI signal and moderate DWI signal are the key features distinguishing PHs from NETs. Incorporating these findings with EUS-FNA and immunohistochemistry can support a provisional diagnosis and help avoid unnecessary radical surgery. Full article

Clear cell renal cell carcinoma is the most common histological type of renal cancer, which is a common cancer type usually associated with a long clinical course. During this course, various metastatic sites can be observed. In this review, we have focused on metastases to the thyroid gland. We conducted research in three medical databases, including PubMed, Scopus, and Web of Science, using the same search algorithm. Our inclusion criteria focused on case reports and case series studies since 2011, covering therapeutic strategies for the primary and/or metastatic disease, as well as subsequent follow-up data. Studies with insufficient or uncertain data, or written in a language other than English, were excluded. An analysis of 510 articles from PubMed, 1729 from Scopus, and 649 from Web of Science, after application of inclusion and exclusion criteria, resulted in 77 reports, analyzing 189 patients. A description of the clinical, pathological, ancillary, and follow-up data, in the light of recent therapeutic schemes, was attempted. Our results suggest that metastases’ imaging features comprised more commonly a solitary nodule with a median size of 3.5 cm and worrisome features in ultrasonography, such as heterogeneity, hypoechogenicity, partially solid configuration, and variable internal vascularization. Histological and immunohistochemical examination of the lesion is necessary because these findings are not specific. Common non-thyroid metastases are seen in the urogenital system, lungs, and pancreas. We calculated the restricted mean survival from primary diagnosis at 274.6 months (95% CI: 264.3–285.0 months) and the restricted mean survival from thyroid metastases treatment at 93.9 months (95% CI: 65.3–122.4 months). Results regarding how patient characteristics affect these survival numbers were statistically nonsignificant (p > 0.05). Full article

This study evaluates the use of UAV-based photogrammetry to document shallow submerged cultural heritage, focusing on the Tirpitz wreck salvage site near Håkøya, Norway. Using a DJI Phantom 4 Multispectral drone, we acquired RGB and multispectral imagery over structures located at depths of up to 5–10 m. Structure-from-motion (SfM) processing enabled the three-dimensional reconstruction of submerged features, including a 52 × 10 m wharf and adjacent debris piles, with an accuracy of the order of 10 cm. Our data represents the first and only accurate mapping of the site yet carried out, with an absolute position uncertainty estimated to be no greater than 3 m. Volumes of imaged debris could be estimated, using a background subtraction method to allow for variable bathymetry, at around 350 m³. Bathymetric data for the sea floor could be derived effectively from an SfM point cloud, though less effectively applying the Stumpf model to the multispectral data as a result of significant spectral variation in the sea floor reflectance. Our results show that UAV-based through-surface SfM is a viable, low-cost method for reconstructing submerged heritage with high spatial accuracy. These findings support the integration of UAV-based remote sensing into heritage and environmental monitoring frameworks for shallow aquatic environments. Full article

Occupational exposure to respirable coal mine dust remains a significant health risk, especially for underground workers. Rapid dust monitoring methods are sought to support timely identification of hazards and corrective actions. Recent research has investigated how optical light microscopy (OLM) with automated image processing might meet this need. In laboratory studies, this approach has been demonstrated to classify particles into three primary classes—coal, silicates and carbonates. If the same is achievable in the field, results could support both hazard monitoring and dust source apportionment. The objective of the current study is to evaluate the performance of OLM with image processing to classify real coal mine dust particles, employing scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) as a reference method. The results highlight two possible challenges for field implementation. First, particle agglomeration can effectively yield mixed particles that are difficult to classify, so integration of a dispersion method into the dust collection or sample preparation should be considered. Second, optical differences can exist between dust particles used for classification model development (i.e., typically generated in the lab from high-purity materials) versus real mine dust, so our results demonstrate the necessity of site-specific model calibration. Full article

Background and Objectives: Access-site complications (ASCs) remain clinically relevant after peripheral endovascular procedures, particularly with large femoral sheaths and complex anatomy. While randomized coronary trials show non-inferiority of vascular closure devices (VCDs) versus manual compression (MC), real-world data in peripheral interventions performed under systematic ultrasound-guided access are limited. Materials and Methods: This retrospective single-center cohort included consecutive peripheral arterial revascularizations (2010–2023) performed via common femoral access under real-time ultrasound guidance. Hemostasis was achieved using MC or VCDs, categorized as collagen plug-based, suture-mediated, or clip-based systems. The primary endpoint was 30-day ASCs, defined as hematoma requiring management, pseudoaneurysm, bleeding requiring transfusion, access-site thrombosis/occlusion, arteriovenous fistula, or infection. The secondary endpoint was VCD failure, defined as unsuccessful hemostasis requiring adjunctive measures. Multivariable logistic regression adjusted for prespecified anatomical and procedural covariates, including sheath size > 6 Fr and puncture-site calcification. Results: Among 231 procedures, VCDs were used in 139 (60.2%) and MC in 92 (39.8%). ASC occurred in 28 cases (12.1%), with higher rates in the MC group compared with VCDs (18.5% vs. 9–14% across device types; p = 0.044). In adjusted analyses, MC (vs any VCD) (odds ratio [OR] 2.41, 95% confidence interval [CI] 1.06–5.47; p = 0.035), sheath size > 6 Fr, and puncture-site calcification were independently associated with ASCs. VCD failure occurred in 5 cases (3.6%) and was not observed with collagen plug-based devices. Conclusions: In this ultrasound-guided real-world peripheral cohort, VCD use was associated with lower 30-day ASC rates and low device failure rates compared with MC. Given the retrospective and non-randomized design, these findings should be considered hypothesis-generating and support individualized, imaging-guided strategies for femoral closure in peripheral interventions. Full article

Background and clinical significance: Primary cardiac diffuse large B-cell lymphoma (DLBCL) arising in bioprosthetic valves is exceedingly rare. Most patients present with localized disease often masquerading as suspected thrombi or vegetations. Imaging studies are inconclusive and due to the rarity of the disease, treatment and follow-up data are very limited. Case presentation: We present one such case developing 9 years after aortic valve replacement in an otherwise immunocompetent patient, who presented with minor symptoms despite significant disease burden. This tumor contained Epstein–Barr virus (EBV), was confined to the site of origin, and has behaved non-aggressively after excision with a follow-up of 59 months. Conclusions: This unique disease is classified as Fibrin-associated large B-cell lymphoma (FA-LBCL) in view of its distinct clinical-pathological features. This report also addresses the unique features of this type of lymphoma. Full article