Search Results (747)

The anisotropic mechanical properties of selective laser melting (SLM)-processed Ti-6Al-4V (TC4) alloy hinder its deployment in polar marine equipment. This study systematically probes the relationships between laser scanning strategies (unidirectional vs. 67°-rotated scanning between layers), notch orientation (governing loading direction), and cryogenic impact energy of SLM-TC4. Charpy impact tests from −60 °C to 20 °C were performed on V-notched specimens fabricated with distinct scanning strategies and notch orientations (top/side surfaces). The analysis of impact energy data and macro/micro-fractography demonstrates that impact energy declines markedly with decreasing temperature, showing a 25–35% reduction at −60 °C versus 20 °C while exhibiting enhanced data consistency under cryogenic conditions. Notably, specimens fabricated with 67°-rotated scanning between layers achieve higher impact toughness than unidirectionally scanned equivalents. Moreover, for identical scanning strategies, side-notched specimens consistently outperform top-notched specimens, evidencing superior interfacial bonding strength between deposited layers relative to bonding within individual layers. Within individual layers, toughness normal to the laser scan path exceeds that parallel to the path. However, controlling ductile-to-brittle transition behavior and precluding brittle failure are imperative for SLM-TC4 components in polar cryogenic service. This work delivers essential quantitative benchmarks and experimental validation for optimizing SLM processing in critical polar vessel components. Full article

Vascular smooth muscle cells (VSMCs) are a major cell type in the arterial wall responsible for regulating vascular homeostasis. Under physiological conditions, VSMCs reside in the medial layer of the arteries, express elevated levels of contractile proteins, regulate vascular tone, and provide mechanical strength and elasticity to the blood vessel. In response to obesity, hyperglycemia, and insulin resistance, critical pathogenic hallmarks of Type 2 diabetes (T2D), VSMCs undergo a phenotypic transformation, adopting new phenotypes with increased proliferative (synthetic), inflammatory (macrophage-like), or bone-like (osteogenic) properties. While crucial for normal repair and vascular adaptation, VSMC phenotypic plasticity is a key driver for the development and progression of macrovascular complications associated with T2D. Despite advances in lineage tracing and multi-omics profiling that have uncovered key molecular regulators of VSMC phenotypic switching in vasculopathy, our understanding of the cellular and molecular mechanisms underlying VSMC transformation into diseased phenotypes in T2D remains incomplete. This review will provide a holistic summary of research from the past 15 years, with a focus on the signaling pathways and transcriptional regulators that govern VSMC phenotypic transition in response to obesity, hyperglycemia, and insulin resistance. We examine the integrated molecular mechanisms that orchestrate VSMC fate reprogramming in T2D and highlight the dynamic interplay among diverse signaling and transcriptional networks. Emphasis is placed on how these interconnected pathways collectively influence VSMC behavior and contribute to the pathogenesis of T2D-associated atherosclerosis. Full article

Aberrant internal carotid arteries (ICA) are congenital vascular anomalies that occur from involution of the cervical portion of the ICA, which leads to enlargement of the normally small collateral inferior tympanic and caroticotympanic arteries. The inferior tympanic artery is a branch of the external carotid artery, usually the ascending pharyngeal artery, which extends through the inferior tympanic canaliculus (ITC), a small foramen located along the cochlea promontory. Aberrant ICAs can also be associated with a persistent stapedial artery (PSA), which is an abnormal vessel that arises from the petrous ICA and passes through the obturator foramen of the stapes. An aberrant ICA is a very important anomaly to recognize on imaging. Accurately describing its presence is important to help prevent iatrogenic injury during intervention. It is also important to distinguish an aberrant ICA from a lateralized ICA. The improvement of spatial resolution with photon counting detector (PCD)-CT has been proven to provide higher performance in detection of sub-centimeter vascular lesions compared to conventional energy-integrated detector (EID)-CT. PCD-CT also provides superior visualization of small skull-based foramina such as the inferior tympanic canaliculus, which can aid in more accurately characterizing an aberrant ICA (variant course without ITC involvement). Full article

This work presents an image processing algorithm for the segmentation of the personalized mapping of retinal nerve fiber layer (RNFL) bundle trajectories in the human retina. To segment RNFL bundles, preprocessing steps were used for noise reduction and illumination correction. Blood vessels were removed. The image was fed to a maximum–minimum modulation algorithm to isolate retinal nerve fiber (RNF) segments. A modified Garway-Heath map categorizes RNF orientation, assuming designated sets of orientation angles for aligning RNFs direction. Bezier curves fit RNFs from the center of the optic disk (OD) to their corresponding end. Fundus images from five different databases (n = 300) were tested, with 277 healthy normal subjects and 33 classified as diabetic without any sign of diabetic retinopathy. The algorithm successfully traced fiber trajectories per fundus across all regions identified by the Garway-Heath map. The resulting trace images were compared to the Jansonius map, reaching an average efficiency of 97.44% and working well with those of low resolution. The average mean difference in orientation angles of the included images was 11.01 ± 1.25 and the average RMSE was 13.82 ± 1.55. A 24-2 visual field (VF) grid pattern was overlaid onto the fundus to relate the VF test points to the intersection of RNFL bundles and their entry angles into the OD. The mean standard deviation (95% limit) obtained 13.5° (median 14.01°), ranging from less than 1° to 28.4° for 50 out of 52 VF locations. The influence of optic parameters was explored using multiple linear regression. Average angle trajectories in the papillomacular region were significantly influenced (p < 0.00001) by the latitudinal optic disk position and disk–fovea angle. Given the basic biometric ground truth data (only fovea and OD centers) that is publicly accessible, the algorithm can be customized to individual eyes and distinguish fibers with accuracy by considering unique anatomical features. Full article

Chimpanzees (Pan troglodytes) rescued from the illegal wildlife trade often suffer from chronic, traumatic injuries that require specialized and prolonged medical treatment in wildlife rehabilitation centers. We present the case report of a two-year-old male chimpanzee admitted at the Tchimpounga Chimpanzee Rehabilitation Center in the Republic of Congo with a chronic periorbital abscess, likely caused by a machete wound sustained during the poaching of his mother. Despite receiving extended antimicrobial therapy, his condition was never fully controlled and progressed to a chronic orbital infection, causing him discomfort and producing chronic purulent discharge. Enucleation was performed under general anesthesia using ketamine and medetomidine, with surgical approach adapted to the distinctive orbital anatomy of chimpanzees. During the procedure, ligation of the optic nerve and ophthalmic vessels was required due to the confined orbital apex and extensive vascularization, ensuring adequate haemostasias and procedural safety. The chimpanzee made an uneventful postoperative recovery, resuming normal feeding and social behavior within 48 h, with complete wound healing occurring within two weeks. This case report highlights the importance of prompt surgical intervention when conservative medical management fails to resolve refractory ocular infections in chimpanzees. It also emphasizes the importance of specific anesthetic protocols, refined surgical techniques and tailored postoperative care in wildlife rehabilitation centers. Documenting and sharing detailed case reports such as this contributes to the limited veterinary literature on great ape surgery and supports evidence-based clinical decision-making to improve the welfare and treatment outcomes of rescued chimpanzees. Full article

In maritime blurred image recognition, ship collision accidents frequently result from three primary blur types: (1) motion blur from vessel movement in complex sea conditions, (2) defocus blur due to water vapor refraction, and (3) scattering blur caused by sea fog interference. This paper proposes a dual-branch recognition method specifically designed for motion blur, which represents the most prevalent blur type in maritime scenarios. Conventional approaches exhibit constrained computational efficiency and limited adaptability across different modalities. To overcome these limitations, we propose a hybrid CNN–Transformer architecture: the CNN branch captures local blur characteristics, while the enhanced Transformer module models long-range dependencies via attention mechanisms. The CNN branch employs a lightweight ResNet variant, in which conventional residual blocks are substituted with Multi-Scale Gradient-Aware Residual Block (MSG-ARB). This architecture employs learnable gradient convolution for explicit local gradient feature extraction and utilizes gradient content gating to strengthen blur-sensitive region representation, significantly improving computational efficiency compared to conventional CNNs. The Transformer branch incorporates a Hierarchical Swin Transformer (HST) framework with Shifted Window-based Multi-head Self-Attention for global context modeling. The proposed method incorporates blur invariant Positional Encoding (PE) to enhance blur spectrum modeling capability, while employing DyT (Dynamic Tanh) module with learnable α parameters to replace traditional normalization layers. This architecture achieves a significant reduction in computational costs while preserving feature representation quality. Moreover, it efficiently computes long-range image dependencies using a compact 16 × 16 window configuration. The proposed feature fusion module synergistically integrates CNN-based local feature extraction with Transformer-enabled global representation learning, achieving comprehensive feature modeling across different scales. To evaluate the model’s performance and generalization ability, we conducted comprehensive experiments on four benchmark datasets: VAIS, GoPro, Mini-ImageNet, and Open Images V4. Experimental results show that our method achieves superior classification accuracy compared to state-of-the-art approaches, while simultaneously enhancing inference speed and reducing GPU memory consumption. Ablation studies confirm that the DyT module effectively suppresses outliers and improves computational efficiency, particularly when processing low-quality input data. Full article

Background/Objectives: First-trimester referral scans in high-risk pregnancies are performed by expert fetal medicine operators using an extended protocol that includes direct fetal heart assessment. This study evaluated inter-operator agreement in characterizing the four-chamber view (4CV) and three vessels and trachea view (3VTV) using color Doppler during such scans in both normal and abnormal cases. Methods: Two independent operators and a fetal cardiologist, all blinded to final diagnoses, retrospectively reviewed 2D images and video clips of the 4CV and 3VTV in 90 fetuses (45 with congenital heart disease [CHD] and 45 controls). The 4CV was classified into four patterns: (1) two atrioventricular (A-V) inflows of similar size, (2) one A-V inflow filling two ventricles, (3) one A-V inflow filling one ventricle, and (4) two A-V inflows with disproportion. The 3VTV was assessed for (1) normal V-sign, (2) abnormal vessel number, (3) abnormal vessel dimension, (4) abnormal spatial relationships, and (5) ductal dependence. Agreement was measured using Cohen’s Kappa. Results: Perfect agreement (K = 1) was seen in normal cases. In CHD cases, inter-operator and operator–cardiologist agreement was almost perfect for 4CV (K = 0.83–0.96) and substantial for 3VTV (K = 0.77–0.80). The lowest agreement occurred with ventricular disproportion in 4CV and abnormalities in vessel number and size in 3VTV. Conclusions: Expert operators show strong agreement in interpreting 4CV and 3VTV patterns in first-trimester scans using color Doppler. However, certain abnormalities—particularly ventricular disproportion and vessel anomalies—remain challenging to consistently interpret. Full article

Background: To personalize the care for persons with smoking-related lung disease, a thorough understanding of its etiology is essential. The role of pulmonary vessels remains poorly understood. Living at high altitude provides a natural model to investigate the effects of low oxygen levels on pulmonary vessels. This study aims to evaluate the relationship between living at high altitudes and small pulmonary vein and artery volumes. We hypothesize that small vein and artery volumes were independently associated with living at high altitude. Methods: We quantified small pulmonary vein and artery dimensions (ᴓ < 1 mm) on computed tomography (CT) down to 0.2 mm in diameter and normalized the dimensions by body surface area. In 8931 current and former smokers participating in the COPDGene study, we used multivariate regression models corrected for clinical and technical confounders. Results: 1262 residents (14.1%) were defined as high-altitude residents (~1600 m, Denver, CO, USA). Compared to lower-altitude residents, the high-altitude residents had a higher age (62.0 ± 9.1 vs. 59.6 ± 9.0 years), more pack-years smoked (46.8 vs. 44.1) and a lower FEV₁% predicted (64.6 ± 32.4% vs. 76.8 ± 25.2%). Both mean small artery volume (4.09 ± 0.89 mL/m² vs. 3.85 ± 0.90 mL/m²) and mean small vein volume (2.96 ± 0.53 mL/m² vs. 2.67 ± 0.53 mL/m²) were higher in high-altitude residents. Multivariate linear regression showed that, in those without COPD, high-altitude residents have a higher small vein volume (0.129 mL/m², p < 0.001) and higher small artery volume (0.170 mL/m², p = 0.001) compared to lower-altitude residents. There was no significant association in residents with COPD. Conclusions: In current and former smokers without COPD, higher small pulmonary vein and artery volumes were associated with living at high altitude, independent of lung disease or technical CT parameters. A potential cause includes vascular remodeling due to an elevated need for blood oxygen transport, which becomes concealed when COPD develops. Full article

Background/Objectives: Alterations in liver vascularization play a remarkable role in liver disease development, including hepatocellular carcinoma (HCC), but remain understudied. This study evaluated the hepatic microvascular imaging method and provided high-resolution quantitative anatomical data on the characteristics and architecture of liver vasculature in wild-type (WT) mice and HCC mouse models. Methods: C57BL/6 mice were injected with Akt/Ras or Sleeping Beauty transposon to induce HCC. Liver tissues from normal and Akt/Ras mice underwent hematoxylin and eosin, Masson’s trichrome, Ki67, and lymphatic endothelial receptor-1 staining. Using cutting-edge high-definition fluorescence micro-optical sectioning tomography, high-precision microvascular visualization of the liver was performed in WT and Akt/Ras HCC mice. Results: The sectioned volumes of normal and HCC liver tissues were 204.8 mm³ and 212.8 mm³, respectively. The microvascular systems associated with the tissues of the Akt/Ras HCC mouse were twisted, disordered, and compressed by tumor nodules. In the four tumor nodules, the path of the hepatic artery was more around the tumor edge, whereas the portal vein occupied the central position and constituted the main blood vessel entering the tumors. The porosity of HCC and paracancerous cirrhotic tissues was significantly less than that of normal tissues. The radii of the central vessels in the hepatic sinusoid of paratumoral cirrhotic tissues were significantly higher than those of normal tissues; however, the hepatic sinusoid density of paratumoral cirrhotic tissues was lower. Conclusions: This research provides a deeper understanding of the normal liver microvasculature and alterations in cases of cirrhosis and HCC, which complements scientific insights into liver morphology and physiology. This straightforward research approach involving the novel 3D liver microvasculature can be used in multiscale physiological and pathophysiological studies regarding liver diseases. Full article

Background: Seminoma is the most common subtype of testicular germ cell tumors in young men; however, the contribution of tumor-associated macrophages (TAMs) to disease progression remains insufficiently understood. This study aimed to quantitatively and phenotypically characterize CD68⁺ and CD163⁺ TAMs in non-metastatic seminomas (pT1N0M0 and pT2N0M0). Methods: This retrospective, multicenter, cohort, observational, analytical study was conducted from 1 January 2015 to 1 January 2025 at two branches of the National Medical Research Radiological Center of the Ministry of Health of the Russian Federation: the A. Tsyb Medical Radiological Research Center and the P. Hertsen Moscow Oncology Research Institute. Archived paraffin-embedded tumor samples from 96 patients and 21 samples of normal testicular tissue were analyzed using immunohistochemistry and digital morphometric analysis with QuPath software to assess macrophage density and spatial distribution. Results: Compared to normal testicular tissue, seminomas demonstrated more than a 10-fold increase in CD68⁺ TAMs and over a 100-fold increase in CD163⁺ TAMs. CD68⁺ cells predominantly localized to peripheral tumor regions, while CD163⁺ cells formed diffuse clusters in central tumor zones and around peripheral vessels. No statistically significant differences in CD68⁺ cell density were found between pT1 and pT2 stages. However, pT2 tumors showed a trend toward higher CD163⁺ TAMs density, suggesting increased M2 polarization with advancing tumor stage. Conclusions: These findings highlight the spatial and phenotypic heterogeneity of TAMs in seminoma and indicate a shift toward an immunosuppressive tumor microenvironment during local progression. Future studies should assess macrophage polarization and progression-free survival to evaluate their potential as prognostic biomarkers and therapeutic targets in seminoma. Full article

Although the upper nodal roots are vital for resource uptake in late-stage maize, their anatomical and physiological responses to varying nitrogen and light regimes remain unclear. In a field experiment, maize was grown under three nitrogen levels (0, 192, and 240 kg·ha⁻¹) and two light regimes (normal light and 50% shading). At flowering (R1), we selected the number, diameter, anatomy of nodal roots, root-bleeding sap composition, and grain yield. Sample sizes ranged from three to twelve replicates per treatment, depending on the trait measured. Data were analyzed using ANOVA and Duncan’s test (p < 0.05). Under normal light, N₁₉₂ and N₂₄₀ significantly enhanced stele and vessel diameters in the sixth and seventh nodal root whorls, vessel number and cortical cell layers in the fifth and seventh whorls, root-bleeding intensity, exudation rates of sucrose, abscisic acid, key free amino acids (Asn, Asp, Glu), and grain yield, compared to N₀. Shading markedly suppressed the nodal root anatomical structure, reducing root-bleeding intensity by 18.2–26.6% and yield by 30.6–40.8%; especially under SS-N0, which also notably increased the exudation of stress-related amino acids (particularly Asp and Glu). Correlation analysis revealed positive relationships of root-bleeding intensity with vessel area and grain yield, indicating that impaired root anatomy restricts resource transport under shading. Sufficient nitrogen partially alleviated these adverse effects. This study demonstrates that light and nitrogen synergistically regulate the upper nodal root anatomy, thereby modulating root-bleeding sap and ultimately influencing grain yield. These results provide a theoretical basis for high-yield maize cultivation and precision nitrogen management under low-light stress. Full article

Multi-layer multi-pass welding plays an important role in manufacturing industries such as nuclear power plants, pressure vessel manufacturing, and ship building. However, distortion or welding defects are still challenges; therefore, welding monitoring and quality control are essential tasks for the dynamic adjustment of execution during welding. The aim was to propose a machine vision system for monitoring and surface quality evaluation during multi-pass welding using a line scanner and infrared camera sensors. The cross-section modelling based on the line scanner data enabled the measurement of distortion and dynamic control of the welding plan. Lack of fusion, porosity, and burn-through defects were intentionally generated by controlling welding parameters to construct a defect inspection dataset. To reduce the influence of material surface colour, the proposed normal map approach combined with a deep learning approach was applied for inspecting the surface defects on each layer, achieving a mean average precision of 0.88. In addition to monitoring the temperature of the weld pool, a burn-through defect detection algorithm was introduced to track welding status. The whole system was integrated into a graphical user interface to visualize the welding progress. This work provides a solid foundation for monitoring and potential for the further development of the automatic adaptive welding system in multi-layer multi-pass welding. Full article

Background/Objectives: The rapid development of artificial intelligence is transforming the face of medicine. Due to the large number of imaging studies (pre-, intra-, and postoperative) combined with histopathological and molecular findings, its impact may be particularly significant in neurosurgery. We aimed to perform a scoping review of recent applications of deep learning in MRI-based diagnostics of brain tumors relevant to neurosurgical practice. Methods: We conducted a systematic search of scientific articles available in the PubMed database. The search was performed on 22 April 2024, using the following query: ((MRI) AND (brain tumor)) AND (deep learning). We included original studies that applied deep-learning methods to brain tumor diagnostics using MRI, with potential relevance to neuroradiology or neurosurgery. A total of 893 records were retrieved, and after title/abstract screening and full-text assessment by two independent reviewers, 229 studies met the inclusion criteria. The study was not registered and received no external funding. Results: Most included articles were published after 1 January 2022. The studies primarily focused on developing models to differentiate between specific CNS tumors. With improved radiological analysis, deep-learning technologies can support surgical planning through enhanced visualization of cerebral vessels, white matter tracts, and functional brain areas. Over half of the papers (52%) focused on gliomas, particularly their detection, grading, and molecular characterization. Conclusions: Recent advancements in artificial intelligence methods have enabled differentiation between normal and abnormal CNS imaging, identification of various pathological entities, and, in some cases, precise tumor classification and molecular profiling. These tools show promise in supporting both diagnosis and treatment planning in neurosurgery. Full article

Reliable operation of marine equipment is crucial for ensuring vessel performance and safeguarding the safety of personnel and the marine environment. However, the complexity of evaluation criteria and the subjectivity inherent in expert judgments pose significant challenges for effective reliability assessment. To address these challenges, this study proposes an extended VIKOR method within a group decision-making (GDM) framework based on picture fuzzy numbers. The method first collects expert evaluations through questionnaires and voting to construct individual decision matrices, and then it applies a newly developed entropy-based approach to determine attribute weights, resulting in a group-weighted decision matrix. Subsequently, an extended VIKOR model is introduced, where the group utility measure is derived from one positive reference matrix and two negative reference matrices, while the group regret measure is based on two negative reference matrices. To improve assessment precision, this study also introduces a novel normalized projection measure to evaluate the closeness between decision matrices. Finally, two ranking strategies are developed, for static and dynamic environments, respectively. The proposed method is validated through a case study on marine equipment reliability assessment, confirming its effectiveness and feasibility. This study provides valuable insights for both theoretical research and practical applications in maritime engineering. Full article

Objective: Retinal capillary dropout, characterized by acellular capillaries or “ghost vessels,” is an early pathological sign of diabetic retinopathy (DR) that remains undetectable through standard clinical imaging techniques until visible morphological changes, such as microaneurysms or hemorrhages, occur. This study aims to develop a non-destructive artificial intelligence (AI)-based method using fluorescein angiography (FA) images to detect early-stage, silent retinal capillary dropout. Methods: We utilized 94 FA images and corresponding destructive retinal capillary density measurements obtained through retinal trypsin digestion from 51 Nile rats. Early capillary dropout was defined as having an acellular capillary density of ≥18 counts per mm². A DenseNet based deep learning model was trained to classify images into early capillary dropout or normal. A Bayesian framework incorporating diabetes duration was used to enhance model predictions. RNA sequencing was conducted on retinal vasculature to identify molecular markers associated with capillary early dropout. Results: The AI-based FA imaging model demonstrated an accuracy of 80.85%, sensitivity of 84.21%, specificity of 75.68%, and an AUC of 0.86. Integration of diabetes duration into a Bayesian predictive framework further improved the model’s performance (AUC = 0.90). Transcriptomic analysis identified 43 genes significantly upregulated in retinal tissues preceding capillary dropout. Notably, inflammatory markers such as Bcl2a1, Birc5, and Il20rb were among these genes, indicating that inflammation might play a critical role in early DR pathogenesis. Conclusions: This study demonstrates that AI-enhanced FA imaging can predict silent retinal capillary dropout before conventional clinical signs of DR emerge. Combining AI predictions with diabetes duration data significantly improves diagnostic performance. The identified gene markers further highlight inflammation as a potential driver in early DR, offering novel insights and potential therapeutic targets for preventing DR progression. Full article