Search Results (92)

Intravascular lymphoma (IVL) is a rare, aggressive subtype of non-Hodgkin lymphoma (NHL) characterized by the selective proliferation of neoplastic lymphoid cells within small and medium-sized blood vessels, most frequently of B-cell origin (IVLBCL). Its protean clinical presentation, lack of pathognomonic findings, and absence of tumor masses or lymphadenopathies often lead to diagnostic delays and poor outcomes. IVLBCL can manifest in classic, hemophagocytic syndrome-associated (HPS), or cutaneous variants, with extremely variable organ involvement including the central nervous system (CNS), skin, lungs, and endocrine system. Diagnosis requires histopathologic identification of neoplastic intravascular lymphoid cells via targeted or random tissue biopsies. Tumor cells are highly atypical and display a non-GCB B-cell phenotype, often expressing CD20, MUM1, BCL2, and MYC; molecularly, they frequently harbor mutations in MYD88 and CD79B, defining a molecular profile shared with ABC-type DLBCL of immune-privileged sites. Therapeutic approaches are based on rituximab-containing chemotherapy regimens (R-CHOP), often supplemented with CNS-directed therapy due to the disease’s marked neurotropism. Emerging strategies include autologous stem cell transplantation (ASCT) and novel immunotherapeutic approaches, potentially exploiting the frequent expression of PD-L1 by tumor cells. A distinct but related entity, intravascular NK/T-cell lymphoma (IVNKTCL), is an exceedingly rare EBV-associated lymphoma, showing unique own histologic, immunophenotypic, and molecular features and an even poorer outcome. This review provides a comprehensive overview of the current understandings about clinicopathological, molecular, and therapeutic landscape of IVL, emphasizing the need for increased clinical awareness, standardized diagnostic protocols, and individualized treatment strategies for this aggressive yet intriguing malignancy. Full article

Breast cancer invasion and subsequent metastasis to distant tissues occur when cancer cells lose cell–cell contact, develop a migrating phenotype, and invade the basement membrane (BM) and the extracellular matrix (ECM) to penetrate blood and lymphatic vessels. The identification of the mechanisms which induce the development from a ductal carcinoma in situ (DCIS) to a minimally invasive breast carcinoma (MIBC) is an emerging area of research in understanding tumor invasion and metastatic potential. To investigate the progression from DCIS to MIBC, we analyzed peritumoral collagen architecture using correlative scanning electron microscopy (SEM) on histological sections from human biopsies. In DCIS, the peritumoral collagen organizes into concentric lamellae (‘circular fibers’) parallel to the ducts. Within each lamella, type I collagen fibrils align in parallel, while neighboring lamellae show orthogonal fiber orientation. The concentric lamellar arrangement of collagen may physically constrain cancer cell migration, explaining the lack of visible tumor cell invasion into the peritumoral ECM in DCIS. A lamellar dissociation or the development of small inter fiber gaps allowed isolated breast cancer cell invasion and exosomes infiltration in the DCIS microenvironment. The radially arranged fibers observed in the peri-tumoral microenvironment of MIBC biopsies develop from a bending of the circular fibers of DCIS and drive a collective cancer cell invasion associated with an intense immune cell infiltrate. Type I collagen fibrils represent the peri-tumoral nano-environment which can play a mechanical role in regulating the development from DCIS to MIBC. Collectively, it is plausible to suggest that the ECM effectors implicated in breast cancer progression released by the interplay between cancer, stromal, and/or immune cells, and degrading inter fiber/fibril hydrophilic ECM components of the peritumoral ECM, may serve as key players in promoting the dissociation of the concentric collagen lamellae. Full article

The demand for maritime–coastal tourism has been intensifying, but its offerings are sometimes limited to a few activities. Some of these activities do not require specific skills or certifications, while others do. This study aimed to investigate what type of activities are carried out by tourism and recreational vessels in the coastal area of the central Algarve (Portugal). To this end, data from the automatic identification system (AIS) of recreational vessels was used to monitor and categorise these activities in a non-intrusive manner. A model (TORMA) was defined to facilitate the analysis of AIS data and relate them to five independent variables (distance from the coast, boat speed, bathymetry, seabed type, and number of pings). The results of the analysis of more than 11 thousand hourly AIS records for passenger, sailing, and charter vessels showed that the 14 most regular ones had strong seasonal patterns, greater intensity in summer, and spatial patterns with more records near some coastal cliffs. This study provides valuable information on the management of motorised nautical activities near the coast and at sea, contributing to more informed and effective tourism regulation and planning. Full article

Succenturiate placenta is a rare anatomical variant characterized by one or more accessory lobes connected to the main placental mass by fetal vessels. While frequently asymptomatic, this condition can lead to serious maternal–fetal complications if not diagnosed prenatally. Early detection through advanced ultrasonographic techniques plays a critical role in guiding obstetric management and reducing adverse outcomes. Objective: To describe and analyze the prenatal diagnosis, sonographic characteristics, clinical management, and maternal–fetal outcomes of succenturiate placenta cases diagnosed over a ten-year period at a tertiary care center. Methods: We conducted a retrospective observational study of nine pregnancies diagnosed with succenturiate placenta between 2014 and 2024. Data collected included maternal demographics, ultrasound findings, type of cord insertion, presence of associated anomalies such as velamentous cord insertion or vasa previa, vaginal or cesarean delivery, complications, and neonatal outcomes. Ultrasound evaluation was scored based on a four-point checklist assessing key diagnostic steps. Results: Five of the nine cases (55.6%) presented isolated succenturiate placenta, while four (44.4%) were associated with velamentous cord insertion. No cases of vasa previa were identified. Obstetric outcomes included three vaginal deliveries (33.3%), two instrumental (22.2%), and four cesarean sections (44.4%), one of which was emergent due to fetal distress. Complications occurred in 44.4% of cases, with intrapartum bradycardia being the most common. One neonatal death was reported due to placental abruption. The quality of the ultrasound diagnosis was high in most cases, though transvaginal scanning was inconsistently applied. Conclusions: Prenatal identification of succenturiate placenta via detailed ultrasound, including color Doppler and targeted assessment of cord insertion, is essential to minimize risks associated with this condition. Standardized diagnostic protocols can improve detection rates and enable timely clinical decisions, ultimately improving maternal and neonatal outcomes. Full article

This paper presents an artificial intelligence-based model for the classification of maritime vessel images obtained by cameras operating in the visible part of the electromagnetic spectrum. It incorporates both the deep learning techniques for initial image representation and traditional image processing and machine learning methods for subsequent image classification. The presented model is therefore a hybrid approach that uses the Inception v3 deep learning model for the purpose of image vectorization and a combination of SVM, kNN, logistic regression, Naïve Bayes, neural network, and decision tree algorithms for final image classification. The model is trained and tested on a custom dataset consisting of a total of 2915 images of maritime vessels. These images were split into three subsections: training (2444 images), validation (271 images), and testing (200 images). The images themselves encompassed 11 distinctive classes: cargo, container, cruise, fishing, military, passenger, pleasure, sailing, special, tanker, and non-class (objects that can be encountered at sea but do not represent maritime vessels). The presented model accurately classified 86.5% of the images used for training purposes and therefore demonstrated how a relatively straightforward model can still achieve high accuracy and potentially be useful in real-world operational environments aimed at sea surveillance and automatic situational awareness at sea. Full article

Gliomas, the most common type of primary brain tumor, are a significant cause of morbidity and mortality worldwide. Glioblastoma, a highly malignant subtype, is particularly common, aggressive, and resistant to treatment. The tumor microenvironment (TME) of gliomas, especially glioblastomas, is characterized by a distinct presence of tumor-associated macrophages (TAMs), which densely infiltrate glioblastomas, a hallmark of these tumors. This macrophage population comprises both tissue-resident microglia as well as macrophages derived from the walls of blood vessels and the blood stream. Ionized calcium-binding adapter molecule 1 (Iba1) and CD163 are established cellular markers that enable the identification and functional characterization of these cells within the TME. This review provides an in-depth examination of the roles of Iba1 and CD163 in malignant gliomas, with a focus on TAM activation, migration, and immunomodulatory functions. Additionally, we will discuss how recent advances in AI-enhanced cell identification and visualization techniques have begun to transform the analysis of TAMs, promising unprecedented precision in their characterization and providing new insights into their roles within the TME. Iba1 and CD163 appear to have both unique and shared roles in glioma pathobiology, and both have the potential to be targeted through different molecular and cellular mechanisms. We discuss the therapeutic potential of Iba1 and CD163 based on available preclinical (experimental) and clinical (human tissue-based) evidence. Full article

In complex marine environments, intelligent vessels require a high level of dynamic perception to process multiple types of information for mitigating collision risks. To ensure the safety of maritime traffic and enhance the efficiency of navigation information, vessel trajectory prediction is crucial for Automatic Identification Systems (AIS). This study introduces a Graph Convolutional Mamba Network (GC-MT) utilizing AIS data for predicting vessel trajectories. To capture motion interaction characteristics, we employed a Graph Convolutional Network (GCN) to construct a spatiotemporal graph that reflects the interaction relationships among various vessels within the maritime information flow. Furthermore, high-level spatiotemporal features were extracted using a Mamba Neural Network (MNN) to incorporate time-related dynamics. Validation against real-world historical AIS data demonstrates that the proposed model achieved improvements of approximately 35% and 28% in the Average Displacement Error (ADE) and Final Displacement Error (FDE), respectively, compared to the leading baseline model. The predictive capability of the proposed method demonstrates its effectiveness in improving maritime navigation safety in a shipping environment with multiple information sources. Full article

With the rapid growth in maritime traffic, navigational safety has become a pressing concern. Some vessels deliberately manipulate their type information to evade regulatory oversight, either to circumvent legal sanctions or engage in illicit activities. Such practices not only undermine the accuracy of maritime supervision but also pose significant risks to maritime traffic management and safety. Therefore, accurately identifying vessel types is essential for effective maritime traffic regulation, combating maritime crimes, and ensuring safe maritime transportation. However, the existing methods fail to fully exploit the long-term sequential dependencies and intricate mobility patterns embedded in vessel trajectory data, leading to suboptimal identification accuracy and reliability. To address these limitations, we propose MESTR, a Multi-Task Enhanced Ship-Type Recognition model based on Automatic Identification System (AIS) data. MESTR leverages a Transformer-based deep learning framework with a motion-pattern-aware trajectory segment masking strategy. By jointly optimizing two learning tasks—trajectory segment masking prediction and ship-type prediction—MESTR effectively captures deep spatiotemporal features of various vessel types. This approach enables the accurate classification of six common vessel categories: tug, sailing, fishing, passenger, tanker, and cargo. Experimental evaluations on real-world maritime datasets demonstrate the effectiveness of MESTR, achieving an average accuracy improvement of 12.04% over the existing methods. Full article

Environment sensing plays an important role for the safe autonomous navigation of intelligent ships. However, the inherent limitations of sensors, such as the low frequency of the automatic identification system (AIS), blind zone of the marine radar, and lack of depth information in visible images, make it difficult to achieve accurate sensing with a single modality of sensor data. To overcome this limitation, we propose a new multi-source data fusion framework and technologies that integrate AIS, radar, and visible data. This framework leverages the complementary strengths of these different types of sensors to enhance sensing performance, especially in real complex scenarios where single-modality data are significantly affected by blind zone and adverse weather conditions. We first design a multi-stage detection and tracking method (named MSTrack). By feeding the historical fusion results back to earlier tracking stages, the proposed method identifies and refines potential missing detections from the layered detection and tracking processes of radar and visible images. Then, a cascade association matching method is proposed to realize the association between multi-source trajectories. It first performs pairwise association in a high-accuracy aligned coordinate system, followed by association in a low-accuracy coordinate system and integrated matching between multi-source data. Through these association operations, the method can effectively reduce the association errors caused by measurement noise and projection system errors. Furthermore, we develop the first multi-source fusion dataset for intelligent vessel (WHUT-MSFVessel), and validate our methods. The experimental results show that our multi-source data fusion methods significantly improve the sensing accuracy and identity consistency of tracking, achieving average $MOTA$ scores of 0.872 and 0.938 on the radar and visible images, respectively, and $ID{F}_1$ scores of 0.811 and 0.929. Additionally, the fusion accuracy reaches up to 0.9, which can provide vessels with a comprehensive perception of the navigation environment for safer navigation. Full article

The aim of this article is to examine existing technologies for the use of electrical energy and to develop proposals for their improvement on maritime vessels. As a criterion for evaluating the effectiveness of alternative energy sources on ships, factors such as greenhouse gas emissions levels, production and transportation characteristics, onboard storage conditions, and technoeconomic indicators have been proposed. The analysis of fuel types reveals that hydrogen has zero greenhouse gas emissions. However, transportation and storage issues, along with the high investment required for implementation, pose barriers to the widespread use of hydrogen as fuel for maritime vessels. This article demonstrates that solar energy can serve as an alternative to gases and liquid fuels in maritime transport. The technologies and challenges in utilizing solar energy for shipping are analyzed, trends in solar energy for maritime transport are discussed, and future research directions for the use of solar energy in the maritime sector are proposed. The most significant findings include the identification of future research directions in the application of solar energy in the maritime sector, including the adaptation of concentrated solar power (CSP) systems for maritime applications; the development of materials and designs for solar panels specifically tailored to marine conditions; the development of methods for assessing the long-term economic benefits of using solar energy on vessels; and the creation of regulatory frameworks and international standards for the use of solar energy on ships. Furthermore, for hybrid photovoltaic and diesel power systems, promising research directions could include efforts to implement direct torque control systems instead of field-orientated control systems, as well as working on compensating higher harmonics in the phase current spectra of asynchronous motors. Full article

Identifying illegal fishing activities from Automatic Identification System (AIS) data is difficult since AIS messages are broadcast cooperatively, the ship’s master controls the timing, and the content of the transmission and the activities of interest usually occur far away from the shore. This paper presents our work to predict ship types using AIS data from satellites: in such data, there is a pronounced imbalance between the data for different types of ships, the refresh rate is relatively low, and there is a misreporting of information. To mitigate these issues, our prediction algorithm only uses the sequence of ports the ships visited, as inferred from the positions reported in AIS messages. Experiments involving multiple machine learning algorithms showed that such port visits are informative features when inferring ship type. In particular, this was shown to be the case for the fishing vessels, which is the focus of this paper. We then applied a KD-tree to efficiently identify pairs of ships that are close to one another. As this activity is usually dangerous, multiple occurrences of such encounters that are linked to one ship sensibly motivate extra attention. As a result of applying the analysis approach to a month of AIS data related to a large area in Southeast Asia, we identified 17 cases of potentially illegal behaviours. Full article

Hydrogen, as a zero-emission fuel, produces only water when used in fuel cells, making it a vital contributor to reducing greenhouse gas emissions across industries like transportation, energy, and manufacturing. Efficient hydrogen storage requires lightweight, high-strength vessels capable of withstanding high pressures to ensure the safe and reliable delivery of clean energy for various applications. Type V composite pressure vessels (CPVs) have emerged as a preferred solution due to their superior properties, thus this study aims to predict the performance of a Type V CPV by developing its numerical model and calculating numerical burst pressure (NBP). For the validation of the numerical model, a Hydraulic Burst Pressure test is conducted to determine the experimental burst pressure (EBP). The comparative study between NBP and EBP shows that the numerical model provides an accurate prediction of the vessel’s performance under pressure, including the identification of failure locations. These findings highlight the potential of the numerical model to streamline the development process, reduce costs, and accelerate the production of CPVs that are manufactured by prepreg hand layup process (PHLP), using carbon fiber/epoxy resin prepreg material. Full article

In the field of research into vessel type recognition utilizing trajectory data, researchers have primarily concentrated on developing models based on trajectory sequences to extract the relevant information. However, this approach often overlooks the crucial significance of the spatial dependency relationships among trajectory points, posing challenges for comprehensively capturing the intricate features of vessel travel patterns. To address this limitation, our study introduces a novel multi-graph fusion representation method that integrates both trajectory sequences and dependency relationships to optimize the task of vessel type recognition. The proposed method initially extracts the spatiotemporal features and behavioral semantic features from vessel trajectories. By utilizing these behavioral semantic features, the key nodes within the trajectory that exhibit dependencies are identified. Subsequently, graph structures are constructed to represent the intricate dependencies between these nodes and the sequences of trajectory points. These graph structures are then processed through graph convolutional networks (GCNs), which integrate various sources of information within the graphs to obtain behavioral representations of vessel trajectories. Finally, these representations are applied to the task of vessel type recognition for experimental validation. The experimental results indicate that this method significantly enhances vessel type recognition performance when compared to other baseline methods. Additionally, ablation experiments have been conducted to validate the effectiveness of each component of the method. This innovative approach not only delves deeply into the behavioral representations of vessel trajectories but also contributes to advancements in intelligent water traffic control. Full article

Neuroendocrine tumours (NETs) are relatively rare neoplasms but represent one of the most frequent types of primary small-bowel tumours. Their incidence is rising, and this is most likely because of their more frequent early-stage detection, physician awareness, and increasing availability and use of imaging and small-bowel endoscopic techniques, such as video capsule endoscopy and device-assisted enteroscopy, which enable the detection, localisation, and histological sampling of previously inaccessible and underdiagnosed small-bowel lesions. This review summarises the role of small-bowel endoscopy in the diagnosis and management of small-bowel NETs to assist clinicians in their practice. Small-bowel endoscopy may play a complementary role in the diagnosis of these tumours alongside other diagnostic tests, such as biomarkers, conventional radiology, and functional imaging. In addition, small-bowel enteroscopy may play a role in the preoperative setting for the identification and marking of these tumours for surgical resection and the management of rare complications, such as small-bowel variceal bleeding, in cases of portal hypertension due to the encasement of mesenteric vessels in fibrotic small-bowel NETs. Full article

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system. Full article