Search Results (1,365)

Extensive radar investigations, observed spectral signatures, geomorphological, and paleoclimate modeling support the presence of mid- to low-latitude ground ice on Mars. The presence of near-surface ice and glacial features has been proposed in Ismenius Lacus, but the ice composition and age remain unconstrained. Our high-resolution stereoscopic analysis reveals distinctive landforms, including sharp-edged polyhedra, chevron patterns, and en-echelon open fractures, indicative of plastic glacial deformation. Current climatic conditions may support year-round ice stability, while sharp-edged polyhedra, open fractures, and the absence of superposed craters suggest active glaciation. The Ariguani delta system lacks fluvial signatures but aligns with glacial erosional and depositional processes. Unlike terrestrial glaciers, ice accumulation here is likely driven by escarpment-fed melt from seasonal permafrost thawing under lithostatic pressure, generating neo-glacial flows that sustain the glacial tongue. This mechanism can also explain regional features, including U-shaped valley subsidence, gravitational slides, flow of low-viscosity material lobes, and ring-mold craters. Thus, we propose sharp-edged polyhedra as diagnostic markers for identifying ongoing ice dynamics on Mars, enabling future automated detection of active glacial environments. Full article

The integration of nanotechnology into drug repurposing strategies is redefining the development landscape for diagnostic, therapeutic, and theranostic agents. In radiopharmacy, nanoplatforms are increasingly being explored to enhance or extend the use of existing radiopharmaceuticals, complementing earlier applications in other biomedical fields. Many of these nanoplatforms evolve into multifunctional systems by incorporating additional imaging modalities (e.g., MRI, fluorescence) or non-radioactive therapies (e.g., photodynamic therapy, chemotherapy). These hybrid constructs often emerge from the reformulation, repositioning, or revival of previously approved or abandoned compounds, generating entities with novel pharmacological, pharmacokinetic, and biodistribution profiles. However, their translational potential faces significant regulatory hurdles. Existing frameworks—typically designed for single-modality drugs or devices—struggle to accommodate the combined complexity of nanoengineering, radioactive components, and integrated functionalities. This review examines how these systems challenge current norms in classification, safety assessment, preclinical modeling, and regulatory coordination. It also addresses emerging concerns around digital adjuncts such as AI-assisted dosimetry and software-based therapy planning. Finally, the article outlines international initiatives aimed at closing regulatory gaps and provides future directions for building harmonized, risk-adapted frameworks that support innovation while ensuring safety and efficacy. Full article

Introduction: Sperm morphology is a fundamental parameter in the evaluation of male infertility, offering critical insights into reproductive health. However, traditional manual assessments under microscopy are limited by operator dependency and subjective interpretation caused by biological variation. To overcome these limitations, there is a need for accurate and fully automated classification systems. Objectives: This study aims to develop a two-stage, fully automated sperm morphology classification framework that can accurately identify a wide spectrum of abnormalities. The framework is designed to reduce subjectivity, minimize misclassification between visually similar categories, and provide more reliable diagnostic support in reproductive healthcare. Methods: A novel two-stage deep learning-based framework is proposed utilizing images from three staining-specific versions of a comprehensive 18-class dataset. In the first stage, sperm images are categorized into two principal groups: (1) head and neck region abnormalities, and (2) normal morphology together with tail-related abnormalities. In the second stage, a customized ensemble model—integrating four distinct deep learning architectures, including DeepMind’s NFNet-F4 and vision transformer (ViT) variants—is employed for detailed abnormality classification. Unlike conventional majority voting, a structured multi-stage voting strategy is introduced to enhance decision reliability. Results: The proposed framework consistently outperforms single-model baselines, achieving accuracies of 69.43%, 71.34%, and 68.41% across the three staining protocols. These results correspond to a statistically significant 4.38% improvement over prior approaches in the literature. Moreover, the two-stage system substantially reduces misclassification among visually similar categories, demonstrating enhanced ability to detect subtle morphological variations. Conclusions: The proposed two-stage, ensemble-based framework provides a robust and accurate solution for automated sperm morphology classification. By combining hierarchical classification with structured decision fusion, the method advances beyond traditional and single-model approaches, offering a reliable and scalable tool for clinical decision-making in male fertility assessment. Full article

This study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency. It uses the Remaining Useful Life (RUL) prediction technology to establish a sensing platform for ship propellers to capture vibration signals during ship operations. The Diagnosis and RUL Prediction Model is designed to assess bearing aging status and the RUL of the propeller. The synchronized signal reshaping technology is employed in the Diagnosis and RUL Prediction Model to process the original vibration signals as input to the model. The vibration signals obtained are used to analyze the temporal and spectral energy of propeller bearings. Exponential functions are used to generate the health index as model outputs. Model inputs and outputs are simultaneously input into a Long Short-Term Memory (LSTM) model for training, culminating as the Diagnosis and RUL Prediction Model. Compared to Recurrent Neural Network and Support Vector Regression models used in previous studies, the Diagnosis and RUL Prediction Model developed in this study achieves a Mean Squared Error (MSE) of 0.018 and a Mean Absolute Error (MAE) of 0.039, demonstrating outstanding performance in prediction results and computational efficiency. This study integrates the Diagnosis and RUL Prediction Model, bearing aging experimental data, and real-world vibration measurements to develop the diagnosis and RUL prediction system for ship propellers. Experiments with ship propellers show that when the bearing of the propeller enters the worn stage, this diagnostic system for ship propellers can accurately determine the current status of the bearing and its remaining useful life. This study offers a practical solution to insufficient ship maintenance capacity and contributes to improving the operational efficiency of ships. Full article

FastDiag is a widely used algorithm for model-based diagnosis, computing minimal subsets of constraints whose removal restores consistency in knowledge-based systems. As applications grow in complexity, researchers have proposed parallel extensions such as FastDiagP and FastDiagP++ to accelerate diagnosis through speculative and multiprocessing strategies. This paper presents a reproducible and extensible framework for evaluating FastDiag and its parallel variants across a benchmark suite of feature models and ontology-like constraints. We analyze each variant in terms of recursion structure, runtime performance, and diagnostic correctness. Tracking mechanisms and structured logs enable the fine-grained comparison of recursive behavior and branching strategies. Technical validation confirms that parallel execution preserves minimality and structural soundness, while benchmark results show runtime improvements of up to 4× with FastDiagP++. The accompanying dataset, available as open source, supports educational use, algorithmic benchmarking, and integration into interactive configuration environments. The framework is primarily intended for reproducible benchmarking and teaching with open-source implementations that facilitate analysis and extension. Full article

Background: The peach allergen Pru p 7, a member of the Gibberellin-Regulated Protein (GRP) family, has emerged as a key marker of severe fruit-induced allergies. It is hypothesized to mediate cross-reactivity between fruit allergens and cypress pollen. Given the increasing prevalence of food allergies and the complex patterns of cross-sensitization, the role of Pru p 7 in clinical allergy diagnostics warrants further investigation. Objective: This study aims to characterize the sensitization profile to Pru p 7 in a cohort of patients with suspected fruit allergy and to assess its relationship with cypress pollen allergy, particularly to Cup s 7, a homologous GRP from Cupressus sempervirens. Methods: A retrospective analysis was conducted on 20 patients evaluated at the Allergy Unit of the Fondazione IRCCS Policlinico San Matteo. Specific IgE (sIgE) levels to peach extract, Pru p 7, and Cup a 1 (cypress extract) were assessed using the ImmunoCAP^® system (Thermo Fisher Scientific Inc., Waltham, MA, USA). Statistical associations between sensitizations were evaluated using chi-square tests and Spearman’s correlation. Results: Sensitization to peach extract, Pru p 7, and cypress pollen was detected in 38%, 30%, and 45% of patients, respectively. Significant associations were observed between peach and cypress (χ² = 8.80, p = 0.003), peach and Pru p 7 (χ² = 8.23, p = 0.004), and cypress and Pru p 7 (χ² = 6.55, p = 0.01). Notably, all patients sensitized to Pru p 7 also tested positive for both peach and cypress allergens, supporting the hypothesis of pollen–food cross-reactivity. Conclusions: Pru p 7 is a clinically relevant allergen that may account for severe allergic responses in patients not sensitized to classical peach allergens. Its cross-reactivity with Cupressaceae-derived GRPs, such as Cup s 7, highlights the importance of molecular diagnostics in evaluating food allergies, particularly in regions with significant exposure to cypress pollen. Full article

Background/Objectives: this study describes the development of a novel three-dimensional electrogoniometer for the quantitative assessment of knee mobility and stability during gait. The primary objective is to determine whether real-time measurements obtained during dynamic activity provide more clinically relevant information than traditional static assessments. Methods: the device employs angular position encoders to capture knee joint kinematics—specifically flexion, extension, rotation, and tibial translation—during locomotion. Data are transmitted in real time to an Android-based application, enabling immediate graphical visualization. A descriptive observational study was conducted involving healthy participants and individuals with anterior cruciate ligament (ACL) injuries to evaluate the device’s performance. Results: results showed that the electrogoniometer captured knee flexion-extension with a range of up to 90°, compared to 45° typically recorded using conventional systems. The device also demonstrated enhanced sensitivity in detecting variations in tibial translation during gait cycles. Conclusions: this electrogoniometer provides a practical tool for clinical assessment of knee function, enabling real-time monitoring of joint behavior during gait. By capturing functional mobility and stability more accurately than static methods, it may enhance diagnostic precision and support more effective rehabilitation planning in orthopedic settings. Full article

Artificial intelligence (AI) has emerged as a transformative technology in radiology, offering enhanced diagnostic accuracy, improved workflow efficiency and potential risk mitigation. However, its effectiveness in reducing clinical and occupational risks in radiology departments remains underexplored. This systematic review aimed to evaluate the current literature on AI applications in computed tomography (CT) radiology and their contributions to risk reduction. Following the PRISMA 2020 guidelines, a systematic search was conducted in PubMed, Scopus and Web of Science for studies published between 2021 and 2025 (the databases were last accessed on 15 April 2025). Thirty-four studies were included based on their relevance to AI in radiology and reported outcomes. Extracted data included study type, geographic region, AI application and type, role in clinical workflow, use cases, sensitivity and specificity. The majority of studies addressed triage (61.8%) and computer-aided detection (32.4%). AI was most frequently applied in chest imaging (47.1%) and brain haemorrhage detection (29.4%). The mean reported sensitivity was 89.0% and specificity was 93.3%. AI tools demonstrated advantages in image interpretation, automated patient positioning, prioritisation and measurement standardisation. Reported benefits included reduced cognitive workload, improved triage efficiency, decreased manual annotation and shorter exposure times. AI systems in CT radiology show strong potential to enhance diagnostic consistency and reduce occupational risks. The evidence supports the integration of AI-based tools to assist diagnosis, lower human workload and improve overall safety in radiology departments. Full article

Water, land, energy, and food are fundamental resources for human survival and ecological stability, yet they face intensifying pressure from surging demands and spatial mismatches. Integrated governance of their interconnected nexus is pivotal to achieving sustainable development. In this study, we analyze the water-land-energy-food (WLEF) nexus synergies in China’s North China Plain, a vital grain base for China’s food security. We develop a city-level WLEF evaluation framework and employ a coupling coordination model to assess spatiotemporal patterns of the WLEF system from 2010 to 2022. Additionally, we diagnose critical internal and external influencing factors of the WLEF coupling system, using obstacle degree modeling and geographical detectors. The results indicate that during this period, the most critical internal factor was per capita water resource availability. The impact of the external factor—urbanization level—was characterized by fluctuation and a general upward trend, and by 2022, it had become the dominant influencing factor. Results indicated that the overall development of the WLEF system exhibited a fluctuating trend of initial increasing then decreasing during the study period, peaking at 0.426 in 2016. The coupling coordination level of the WLEF system averaged around 0.5, with the highest value (0.526) in 2016, indicating a marginally coordinated state. Regionally, a higher degree of coordination was presented in the southern regions of the North China Plain compared with the northern areas. Anhui province achieved the optimal coordination, while Beijing consistently ranked lowest. The primary difference lies in the abundant water resources in Anhui, in contrast to the water scarcity in Beijing. Internal diagnostic analysis identified per capita water availability as the primary constraint on system coordination. External factors, including urbanization rate, primary industry’s added value, regional population, and rural residents’ disposable income, exhibited growing influence on the system over time. This study provides a theoretical framework for WLEF system coordination and offers decision-making support for optimizing resource allocation and promoting sustainable development in comparable regions. Full article

Sustainable intensification requires metrics that are able to capture both economic performance and the often-hidden environmental inputs that support agriculture. Emergy analysis (EmA) meets this need by converting all inputs—free environmental flows and purchased goods/services—into a common unit (solar emjoules, sej). We conducted a PRISMA-documented bibliometric review of EmA in agroecosystems (Web of Science + Scopus, 2000–2022) using Bibliometrix and synthesized farm-scale indicators (ELR, EYR, ESI, %R). Our results show output has grown but is concentrated in a few countries (China, Italy and Brazil) and journals, with farm-level assessments dominating over regional and national assessments. Across cases, mixed crop–livestock systems tend to show lower environmental loading (ELR) and higher sustainability (ESI) than crop-only or livestock-only systems. %R is generally modest, indicating continued reliance on non-renewables, with fertilizers (crops) and purchased feed (livestock) identified as recurrent drivers. Thematic mapping reveals well-developed niche clusters but no single motor theme, consistent with the presence of incongruous baselines, transformities and boundaries that limit comparability. We recommend adoption of the 12.1 × 10²⁴ sej yr⁻¹ baseline, transparent transformity reporting and multi-scale designs that link farm diagnostics to basin and national trajectories. Co-reporting with complementary sustainability assessment methods (such as LCA and carbon footprint), along with appropriate UEV resources, would increase its reputation among policymakers while preserving EmA’s systems perspective, converting dispersed case evidence into cumulative knowledge for circular, resilient agroecosystems. Full article

Background: Timely and accurate identification of wound infections is essential for effective management, yet remains clinically challenging. This study evaluated the utility of a near-infrared autofluorescence imaging system (Fluobeam^®, Fluoptics, Grenoble, France) and a thermal imaging system (FLIR^®, Teledyne LLC, Thousand Oaks, CA, USA) for detecting bacterial and fungal infections in chronic wounds. Fluobeam^® enables real-time visualization of microbial autofluorescence without exogenous contrast agents, whereas FLIR^® detects localized thermal changes associated with infection-related inflammation. Methods: This retrospective clinical study included 33 patients with suspected wound infections. All patients underwent autofluorescence imaging using Fluobeam^® and concurrent thermal imaging with FLIR^®. Imaging findings were compared with microbiological culture results, clinical signs of infection, and semi-quantitative microbial burdens. Results: Fluobeam^® achieved a sensitivity of 78.3% and specificity of 80.0% in detecting culture-positive infections. Fluorescence signal intensity correlated strongly with microbial burden (r = 0.76, p < 0.01) and clinical indicators, such as exudate, swelling, and malodor. Pathogens with high metabolic fluorescence, including Pseudomonas aeruginosa and Candida spp., were consistently identified. Representative cases demonstrate the utility of fluorescence imaging in guiding targeted debridement and enhancing intraoperative decision-making. Conclusions: Near-infrared autofluorescence imaging with Fluobeam^® and thermal imaging with FLIR^® offer complementary, noninvasive diagnostic insights into microbial burden and host inflammatory response. The combined use of these modalities may improve infection detection, support clinical decision-making, and enhance wound care outcomes. Full article

With the continuous advancement of intelligent, integrated, and sophisticated modern marine equipment, bearing fault diagnosis faces increasingly severe technical challenges. Compared with traditional industrial environments, marine propulsion systems are characterized by multi-bearing coupled vibrations and complex operating conditions. To address these characteristics, this paper proposes a fault diagnosis method that combines a least squares support vector machine (LSSVM) with multi-domain feature extraction based on an improved hippopotamus optimization algorithm (LCM-HO). This method directly extracts time, spectral, and time-frequency domain features from the raw signal, effectively avoiding complex preprocessing and enhancing its potential for field engineering applications. Experimental verification using the Paderborn bearing dataset and a self-built marine bearing test bench demonstrates that the LCM-HO-LSSVM method achieves diagnostic accuracy rates of 99.11% and 98.00%, respectively, demonstrating significant performance improvements. This research provides a reliable, efficient, and robust technical solution for bearing fault diagnosis in complex marine environments. Full article

Emerging fungal plant pathogens are significant biotic stresses to crops that threaten global food security, biodiversity, and agricultural sustainability. Historically, these pathogens cause devastating crop losses and continue to evolve rapidly due to climate change, international trade, and intensified farming practices. Recent advancements in diagnostic technologies, including remote sensing, sensor-based detection, and molecular techniques, are transforming disease monitoring and detection. These tools, when combined with data mining and big data analysis, facilitate real-time surveillance and early intervention strategies. There is a need for extension and digital advisory services to empower farmers with actionable insights for effective disease management. This manuscript presents an inclusive review of the socioeconomic and historical impacts of fungal plant diseases, the mechanisms driving the emergence of these pathogens, and the pressing need for global surveillance and reporting systems. By analyzing recent advancements and the challenges in the surveillance and diagnosis of fungal pathogens, this review advocates for an integrated, multidisciplinary approach to address the growing threats posed by these emerging fungal diseases. Fostering innovation, enhancing accessibility, and promoting collaboration at both national and international levels are crucial for the agricultural community to protect crops from these emerging biotic stresses, ensuring food security and supporting sustainable farming practices. Full article

Recent advances in artificial intelligence (AI) have transformed ophthalmic diagnostics, particularly for retinal diseases. In this prospective, non-randomized study, we evaluated the performance of an AI-based software system against conventional clinical assessment—both quantitative and qualitative—of optical coherence tomography (OCT) images for diagnosing diabetic macular edema (DME). A total of 700 OCT exams were analyzed across 26 features, including demographic data (age, sex), eye laterality, visual acuity, and 21 quantitative OCT parameters (Macula Map A X-Y). We tested two classification scenarios: binary (DME presence vs. absence) and multiclass (six distinct DME phenotypes). To streamline feature selection, we applied paraconsistent feature engineering (PFE), isolating the most diagnostically relevant variables. We then compared the diagnostic accuracies of logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), and decision tree models. In the binary classification using all features, SVM and KNN achieved 92% accuracy, while logistic regression reached 91%. When restricted to the four PFE-selected features, accuracy modestly declined to 84% for both logistic regression and SVM. These findings underscore the potential of AI—and particularly PFE—as an efficient, accurate aid for DME screening and diagnosis. Full article

Objectives: Connective tissue diseases (CTDs) include a diverse group of systemic autoimmune conditions, among which interstitial lung disease (ILD) is acknowledged as a major determinant of prognosis. High-resolution computed tomography (HRCT) is the gold standard for ILD assessment. The distribution of HRCT patterns across CTDs remain incompletely defined. The objective of this systematic review is to synthesize available evidence regarding the prevalence of specific radiological patterns within CTD-ILDs and to assess whether specific patterns occur at different frequencies among individual CTDs. Methods: The inclusion criteria encompassed original human studies published in English between 2015 and 2024, involving adult participants (≥18 years) with CTD-ILDs assessed primarily by HRCT and designed as retrospective, prospective, or cross-sectional trials with extractable data. We systematically searched PubMed, Scopus, and Web of Science (January 2025). Risk of bias was evaluated using the Newcastle–Ottawa Scale (NOS) for cohort and case–control studies, and the JBI Critical Appraisal Checklist for cross-sectional studies. Data were extracted and categorized by HRCT pattern for each CTD, and then summarized descriptively and statistically. Results: We analyzed 23 studies published between 2015 and 2024, which included 2020 patients with CTD-ILDs. The analysis revealed non-specific interstitial pneumonia (NSIP) as the most prevalent pattern overall (36.5%), followed by definite usual interstitial pneumonia (UIP) (24.8%), organizing pneumonia (OP) (9.8%) and lymphoid interstitial pneumonia (LIP) (1.25%). HRCT distribution varied by CTD: NSIP predominated in systemic sclerosis, idiopathic inflammatory myopathies, and mixed connective tissue disease; UIP was most frequent in rheumatoid arthritis; LIP was more common in Sjögren’s syndrome. While global differences were statistically significant, pairwise comparisons often lacked significance, likely due to sample size constraints. Discussion: Limitations include varying risk of bias across study designs, heterogeneity in HRCT reporting, small sample sizes, and inconsistent follow-up, which may reduce precision and generalizability. In addition to the quantitative synthesis, this review offers a detailed description of each radiologic pattern mentioned above, illustrated by representative examples to support the recognition in clinical settings. Furthermore, it includes a brief overview of the major CTDs associated with ILD, summarizing their epidemiological data, risk factors for ILD and clinical presentation and diagnostic recommendations. Conclusions: NSIP emerged as the most common HRCT pattern across CTD-ILDs, with UIP predominating in RA. Although inter-disease differences were observed, statistical significance was limited, likely reflecting sample size constraints. These findings emphasize the diagnostic and prognostic relevance of HRCT pattern recognition and highlight the need for larger, standardized studies. Full article