Search Results (1,902)

Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy associated with occupational or environmental exposure to asbestos. Effective management of MPM remains challenging due to its prolonged latency period and the typically late onset of clinical symptoms. Accordingly, there is an increasing demand for the implementation of reliable, non-invasive, and data-driven diagnostic strategies within large-scale screening programs. In this context, the chemical profiling of volatile organic compounds (VOCs) in exhaled breath has recently gained recognition as a promising and non-invasive approach for the early detection of cancer, including MPM. Therefore, in this cross-sectional observational study, an overall number of 125 individuals, including 64 MPM patients and 61 healthy controls (HC), were enrolled. End-tidal breath fraction (EXP) was collected directly onto two-bed adsorbent cartridges by an automated sampling system and analyzed by thermal desorption–gas chromatography–mass spectrometry (TD-GC/MS). A machine learning approach based on a random forest (RF) algorithm and trained using a 10-fold cross-validation framework was applied to experimental data, yielding remarkable results (AUC = 86%). Fifteen VOCs reflecting key metabolic alterations characteristic of MPM pathophysiology were found to be able to discriminate between MPM and HC. Moreover, twenty breath samples from asymptomatic former asbestos-exposed (AEx) and eight MPM patients during follow-up (FUMPM) were exploratively analyzed, processed, and tested as blinded samples by the validated statistical method. Good agreement was found between model output and clinical information obtained by CT. These findings underscore the potential of breath VOC analysis as a non-invasive diagnostic approach for MPM and support its feasibility for longitudinal patient and at-risk subjects monitoring. Full article

Hypophosphatemia, defined as serum phosphate levels below 2.5 mg/dL, is a common yet underrecognized electrolyte disturbance in critically ill patients, with prevalence estimates reaching up to 80%. This review explores the intricate bidirectional relationship between hypophosphatemia and hyperventilation, emphasizing its profound implications for respiratory function and critical care management. Hypophosphatemia impairs oxygen delivery by depleting 2,3-diphosphoglycerate (2,3-DPG), disrupts central respiratory drive, and weakens respiratory muscles, leading to hyperventilation, ventilatory failure, and prolonged mechanical ventilation. Conversely, hyperventilation exacerbates hypophosphatemia through respiratory alkalosis, triggering intracellular phosphate shifts and metabolic cascades that rapidly deplete serum levels. This cycle creates significant challenges for ventilator weaning and increases morbidity and mortality. Underlying mechanisms include impaired ATP synthesis, altered chemoreceptor sensitivity, and systemic inflammatory responses. Hypophosphatemia-induced hyperventilation manifests as unexplained tachypnea and respiratory alkalosis, often misdiagnosed as anxiety or pain, while hyperventilation-induced hypophosphatemia contributes to diaphragmatic dysfunction and poor ventilatory performance. Common precipitating factors include refeeding syndrome, diabetic ketoacidosis, continuous renal replacement therapy, and malnutrition. Complications extend beyond respiratory dysfunction to include cardiac depression, immune dysfunction, prolonged ICU stays, and increased healthcare costs. Current diagnostic approaches rely on serum phosphate measurements, which poorly reflect total body stores due to significant intracellular shifts. Emerging biomarkers such as fibroblast growth factor 23 (FGF23) and advanced monitoring technologies, including continuous phosphate tracking, may enhance recognition. Treatment strategies emphasize targeted phosphate repletion based on severity, with intravenous supplementation and ventilatory support tailored to minimize complications. Preventive measures, including risk stratification, prophylactic supplementation, and ventilator management, are critical for high-risk populations. Despite advances, knowledge gaps persist in optimizing monitoring and repletion protocols, understanding genetic variations, and identifying ideal phosphate targets for improved respiratory outcomes. This review provides a comprehensive framework for recognizing and managing hypophosphatemia’s impact on respiratory dysfunction in critically ill patients. Adopting evidence-based interventions and leveraging emerging technologies can significantly improve clinical outcomes, reduce ICU complications, and enhance recovery in this vulnerable population. Full article

Background and Aim: Food hygiene is fundamental to public health, ensuring safe and nutritious food free from contaminants, and is vital for economic development and sustainability. The Hazard Analysis and Critical Control Points (HACCP) system is a crucial tool for managing risks in food production. Despite global recognition of food safety’s importance, significant disparities exist, especially in Southern Italy, where diverse food production, tourism, and economic factors pose challenges to enforcing hygiene standards. This study evaluates non-compliance with food hygiene regulations within a Local Health Authority (LHA) in Calabria, Southern Italy, to inform effective public health strategies. Materials and Methods Authorized by the Food Hygiene and Nutrition Service (FHNS) of the LHA, the study covers January 2022 to December 2024, analyzing 579 enterprises with 1469 production activities. Inspections followed EC Regulation No. 852/2004, verifying the correct application of procedures based on the Hazard Analysis and Critical Control Points (HACCP) principles, including the operator’s monitoring of Critical Control Points (CCPs), and adherence to Good Hygiene Practices (GHPs). Non-compliances were classified by severity, and corrective and punitive actions were applied. Data were analyzed annually and across the full period using descriptive statistics and chi-squared tests to assess trends. Results: Inspection coverage increased markedly from 29.8% of production activities in 2022 to 62.5% in 2023, sustaining 62.0% in early 2024, exceeding the growth of new activities. Inspections were mainly triggered by RASFF alerts (22.4%), routine controls (20.0%), and verification of previous prescriptions (14.3%). The most frequent corrective measures were long-term prescriptions (28.6%), violation reports (22.9%), and short-term prescriptions (20.0%). Enterprises averaged 4.61 production activities, highlighting operational complexity. Conclusions: This study provides a granular analysis of food hygiene non-compliance within a Local Health Authority (LHA) in Southern Italy, to inform effective public health strategies. While official control data may be publicly available in some contexts, our research offers a unique, in-depth view of inspection triggers, non-compliance patterns, and corrective measures, which is crucial for understanding specific regional challenges. The analysis reveals that the prevalence of long-term prescriptions and reliance on RASFF alerts indicate systemic challenges requiring sustained interventions. Full article

Right heart failure (RHF) remains an under-recognized yet devastating condition in critically ill and chronic patients, frequently complicating cardiac surgery, pulmonary embolism, advanced heart failure, sepsis and left ventricular assist device (LVAD) implantation. Despite growing awareness, clinical decision making is still hampered by the complex pathophysiology, limitations in diagnosis and a fragmented therapeutic landscape. In recent years, progress in hemodynamic phenotyping, advanced echocardiographic and biomarker-based assessment, and the development of mechanical circulatory support (MCS) systems, including percutaneous and surgical right ventricle assist devices (RVAD), veno-arterial extracorporeal membrane oxygenation (V-A ECMO), Impella RP (right percutaneous) or BiPella (Impella CP/5.0/5.5 + Impella RP) has expanded the armamentarium for managing RHF. This review synthetizes current evidences on the anatomical, physiological and molecular underpinnings of RHF, delineates the distinction and continuum between acute and chronic forms and provides a comparative analysis of diagnostic tools and MCS strategies. By integrating mechanistic insights with emerging clinical frameworks, the review aims to support earlier recognition, tailored management and innovative therapeutic approaches for this high-risk population. Full article

Potholes threaten public safety and automated vehicles (AVs) safe navigation by increasing accident risks and maintenance costs. Traditional pavement inspection methods, which rely on human assessment, are inefficient for rapid pothole detection and reporting due to potholes’ random and sudden occurring. Advancements in Artificial Intelligence (AI) now enable automated pothole detection using image-based object recognition, providing innovative solutions to enhance road safety and assist agencies in prioritizing maintenance. This paper proposes a novel approach that evaluates the integration of 3 state-of-the-art AI models (YOLOv8n, YOLOv11n, and YOLOv12n) with an ADAS-like camera, GNSS receiver, and Robot Operating System (ROS) to detect potholes in uncontrolled real-life scenarios, including different weather/lighting conditions and different route types, and generate ready-to-use data in a real-time manner. Tested on real-world road data, the algorithm achieved an average precision of 84% and 84% in recall, demonstrating its effectiveness, stable, and high performance for real-life applications. The results highlight its potential to improve road safety, allow vehicles to detect potholes through ADAS, support infrastructure maintenance, and optimize resource allocation. Full article

Exercise-induced fatigue can degrade athletic performance and increase injury risk, yet traditional fatigue assessments often rely on subjective measures. This study proposes an objective fatigue recognition approach using high-fidelity motion capture data and deep learning. This study induced both cognitive and physical fatigue in 50 male participants through a dual task (mental challenge followed by intense exercise) and collected three-dimensional lower-limb joint kinematics and kinetics during vertical jumps. A bidirectional Gate Recurrent Unit (GRU) with an attention mechanism (BiGRU + Attention) was trained to classify pre- vs. post-fatigue states. Five-fold cross-validation was employed for within-sample evaluation, and attention weight analysis provided insight into key fatigue-related movement phases. The BiGRU + Attention model achieved superior performance with 92% classification accuracy and an Area Under Curve (AUC) of 96%, significantly outperforming the single-layer GRU baseline (85% accuracy, AUC 92%). It also exhibited higher recall and fewer missed detections of fatigue. The attention mechanism highlighted critical moments (end of countermovement and landing) associated with fatigue-induced biomechanical changes, enhancing model interpretability. This study collects spatial data and biomechanical data during movement, and uses a bidirectional Gate Recurrent Unit (GRU) model with an attention mechanism to distinguish between non-fatigue states and fatigue states involving both physical and psychological aspects, which holds certain pioneering significance in the field of fatigue state identification. This study lays the foundation for real-time fatigue monitoring systems in sports and rehabilitation, enabling timely interventions to prevent performance decline and injury. Full article

Wildfire events pose significant threats to global ecosystems, with Greece experiencing substantial economic losses exceeding EUR 1.7 billion in 2023 alone, generating immediate financial burdens while contributing to atmospheric carbon dioxide emissions and accelerating climate change effects. This study presents a group of classification models for Greece wildfires utilizing historical datasets spanning 2017 to 2021, incorporating satellite-derived remote sensing data, topographical characteristics, and meteorological observations through a multimodal methodology that integrates satellite imagery processing with traditional numerical data analysis techniques. The framework encompasses multiple deep learning architectures, specifically implementing four standalone models comprising two convolutional neural networks optimized for spatial image processing and long short-term memory networks designed for temporal pattern recognition, extending classification approaches by incorporating visual satellite data alongside established numerical datasets to enable the system to leverage both spatial visual patterns and temporal numerical trends. The implementation employs an ensemble methodology that combines individual model classifications through systematic voting mechanisms, harnessing the complementary strengths of each architectural approach to deliver enhanced predictive capabilities and demonstrate the substantial benefits achieved through multimodal data integration for comprehensive wildfire risk assessment applications. Full article

Background/Objectives: Streptococcus pyogenes (Group A Streptococcus, GAS) is a major human pathogen that causes a wide spectrum of diseases. While mild infections like pharyngitis and impetigo are common, severe and invasive infections, though less frequent, pose significant health risks, particularly in children. In recent years, the re-emergence of hypervirulent GAS strains has heightened global concern. Nowadays, the absence of universally accepted guidelines compels clinicians to rely on a combination of clinical judgment, microbiological data and available evidence to manage these infections effectively. Our aim was to assess the current management of invasive GAS (iGAS) infections in Italy and the variability in therapeutic and preventive approaches. Methods: A web-based current clinical practice survey about invasive and severe GAS infections was designed according to the Checklist for Reporting of Survey Studies (CROSS) methodology and circulated among the members of the Italian Society of Pediatric Infectious Diseases (SITIP). Results: The survey reveals that while many practices are commonly shared among clinicians, particularly regarding first-line therapies (penicillin or ceftriaxone depending on the infection site), significant uncertainties remain, particularly about the use of combined antibiotic regimens and supportive treatments. The use of combined antibiotic regimens was considered appropriate as first-line therapy for STSS, NF and brain abscesses. Clindamycin was the preferred agent for combination with beta-lactam for most infections, except for brain abscesses, where linezolid was favored. However, there was disagreement regarding the optimal timing for de-escalation to beta-lactam monotherapy. Responses varied widely concerning the indications and dosages for IVIG, as well as the use of corticosteroids. Conclusions: Addressing the burden of invasive GAS (iGAS) infections in children requires enhanced surveillance, early recognition, prompt treatment and preventive strategies. Further work to increase surveillance, e.g., developing national registries, and to standardize the management of the disease, e.g., developing country-specific guidelines, is essential to build solid evidence on the most effective approaches. Full article

As global economic integration deepens, import and export trade plays an increasingly vital role in China’s economy. To enhance regulatory efficiency and achieve scientific, transparent credit supervision, this study proposes a trade credit risk evaluation model based on interpretable machine learning, incorporating loss preferences. Key risk features are identified through a comprehensive interpretability framework combining SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), forming an optimal feature subset. Using Light Gradient Boosting Machine (LightGBM) as the base model, a weight adjustment strategy is introduced to reduce costly misclassification of high-risk enterprises, effectively improving their recognition rate. However, this adjustment leads to a decline in overall accuracy. To address this trade-off, a Bagging ensemble framework is applied, which restores and slightly improves accuracy while maintaining low misclassification costs. Experimental results demonstrate that the interpretability framework improves transparency and business applicability, the weight adjustment strategy enhances high-risk enterprise detection, and Bagging balances the overall classification performance. The proposed method ensures reliable identification of high-risk enterprises while preserving overall model robustness, thereby providing strong practical value for enterprise credit risk assessment and decision-making. Full article

With the increasing diversity of remote sensing modalities, multimodal image fusion improves target recognition accuracy but also introduces new security risks. Adversaries can inject small, imperceptible perturbations into a single modality to mislead model predictions, which undermines system reliability. Most existing defences are designed for single-modal inputs and face two key challenges in multimodal settings: 1. vulnerability to perturbation propagation due to static fusion strategies, and 2. the lack of collaborative mechanisms that limit overall robustness according to the weakest modality. To address these issues, we propose CAGMC-Defence, a cross-attention-guided multimodal collaborative defence framework for multimodal remote sensing. It contains two main modules. The Multimodal Feature Enhancement and Fusion (MFEF) module adopts a pseudo-Siamese network and cross-attention to decouple features, capture intermodal dependencies, and suppress perturbation propagation through weighted regulation and consistency alignment. The Multimodal Adversarial Training (MAT) module jointly generates optical and SAR adversarial examples and optimizes network parameters under consistency loss, enhancing robustness and generalization. Experiments on the WHU-OPT-SAR dataset show that CAGMC-Defence maintains stable performance under various typical adversarial attacks, such as FGSM, PGD, and MIM, retaining 85.74% overall accuracy even under the strongest white-box MIM attack ($ϵ=0.05$), significantly outperforming existing multimodal defence baselines. Full article

Despite growing recognition of the role of the gut microbiome in host health and in modulating pathogen activity, the dynamic and reciprocal relationship between enteric viruses and the gut microbial ecosystem remains insufficiently defined and requires further exploration. This comprehensive review examines the bidirectional interplay between the gut microbiome and enteric viral infections by addressing (i) viruses associated with gastrointestinal alterations, (ii) how enteric viral infections alter the composition and function of the gut microbiome, (iii) how the gut microbiome modulates viral infectivity and host susceptibility, and (iv) current microbial-based approaches for preventing or treating enteric viral infections. Gastrointestinal viral infections induce gut microbiome dysbiosis, marked by reductions in beneficial bacteria and increases in potentially pathogenic populations. Specific gut microorganisms can modulate host susceptibility, with certain bacterial genera increasing or decreasing infection risk and disease severity. Pattern recognition receptors in the intestinal epithelium detect microbial signals and trigger antimicrobial peptides, mucus, and interferon responses to control viral replication while maintaining tolerance to commensal bacteria. The gut microbiome can indirectly facilitate viral infections by creating a tolerogenic environment, suppressing antiviral antibody responses, and modulating interferon signaling, or directly enhance viral replication by stabilizing virions, promoting host cell attachment, and facilitating coinfection and viral recombination. In turn, commensal gut bacteria can inhibit viral entry, enhance host antiviral responses, and strengthen mucosal barrier function, contributing to protection against gastrointestinal viral infections. Probiotics and fecal microbiota transplantation constitute potential microbial-based therapeutics that support antiviral defenses, preserve epithelial integrity, and restore microbial balance. In conclusion, the role of the gut microbiome in modulating enteric viral infections represents a promising area of future investigation. Therefore, integrating microbiome insights with virology and immunology could enable predictive and personalized strategies for prevention and treatment. Full article

Background: Non-islet cell tumor hypoglycemia is increasingly reported with gastrointestinal stromal tumors (GIST), but population-level estimates of its clinical impact are limited. We evaluated associations between hypoglycemia and inpatient outcomes among GIST hospitalizations. Methods: We conducted a retrospective cross-sectional study of the National Inpatient Sample (NIS) 2018–2020. Adult GIST discharges were identified by ICD-10-CM codes and stratified by hypoglycemia. Primary outcomes were in-hospital mortality and resource utilization—length of stay (LOS) and total hospital charge. Secondary outcomes included malnutrition, sepsis, ascites, peritonitis, bowel perforation, intestinal obstruction, gastrointestinal bleeding, and iron deficiency anemia. Analyses used survey-weighted logistic regression for binary outcomes and generalized linear models for continuous outcomes. A propensity score-matched sensitivity analysis balanced sepsis and malnutrition. Results: Among 61,725 GIST hospitalizations, 0.72% had hypoglycemia. Mortality was 12.6% with hypoglycemia vs. 3.1% without; adjusted odds of death were higher (aOR 4.16, 95% CI 2.06–8.37; p < 0.001). Hypoglycemia was also associated with malnutrition (aOR 5.63, 3.37–9.40), sepsis (aOR 4.00, 2.24–7.14), ascites (aOR 3.43, 1.63–7.19), and peritonitis (aOR 2.91, 1.17–7.22). LOS was 4.61 days longer on average (not significant; p = 0.185), and total hospital charge was $5218 higher (β = 19,116.8; p = 0.95). In the matched cohort, the mortality association attenuated but persisted (aOR 1.38, 1.27–1.49; p < 0.001); peritonitis remained significant (aOR 1.10, 1.04–1.17), intestinal obstruction (aOR 4.91, 3.44–7.05) and iron deficiency anemia (aOR 3.54, 1.62–7.74) became significant, while ascites and gastrointestinal bleeding were not significant. Conclusions: Hypoglycemia in GIST, although uncommon, marks a higher-risk inpatient trajectory with increased mortality and several complications; these signals largely persist after balancing severity proxies. Resource-use differences were directionally higher but not statistically significant. Recognition of hypoglycemia may aid risk stratification and inpatient management in GIST. Full article

Acute kidney injury (AKI) is a frequent and severe complication in trauma patients, affecting up to 28% of intensive care unit (ICU) admissions and contributing significantly to morbidity, mortality, and long-term renal impairment. Trauma-related AKI (TRAKI) arises from diverse mechanisms, including hemorrhagic shock, ischemia–reperfusion injury, systemic inflammation, rhabdomyolysis, nephrotoxicity, and complex organ crosstalk involving the brain, lungs, and abdomen. Pathophysiologically, TRAKI involves early disruption of the glomerular filtration barrier, tubular epithelial injury, and renal microvascular dysfunction. Inflammatory cascades, oxidative stress, immune thrombosis, and maladaptive repair mechanisms mediate these injuries. Trauma-related rhabdomyolysis and exposure to contrast agents or nephrotoxic drugs further exacerbate renal stress, particularly in patients with pre-existing comorbidities. Traditional markers such as serum creatinine (sCr) are late indicators of kidney damage and lack specificity. Emerging structural and stress response biomarkers—such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), interleukin-18 (IL-18), C-C motif chemokine ligand 14 (CCL14), Dickkopf-3 (DKK3), and the U.S. Food and Drug Administration (FDA)-approved tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein 7 (TIMP-2 × IGFBP-7)—allow earlier detection of subclinical AKI and better predict progression and the need for renal replacement therapy. Together, functional indices like urinary sodium and fractional potassium excretion reflect early microcirculatory stress and add clinical value. In parallel, risk stratification tools, including the Renal Angina Index (RAI), the McMahon score, and the Haines model, enable the early identification of high-risk patients and help tailor nephroprotective strategies. Together, these biomarkers and risk models shift from passive AKI recognition to proactive, personalized management. A new paradigm that integrates biomarker-guided diagnostics and dynamic clinical scoring into trauma care promises to reduce AKI burden and improve renal outcomes in this critically ill population. Full article

Maritime ship transportation is not only the core infrastructure of the global logistics system but also is closely related to national security and sustainable development. However, the human factor remains the primary source of risk leading to maritime accidents during ship navigation. In recent years, multi-source data has been recognized as an important means to improve the efficiency of ship operations and navigation safety. In this paper, the major research methods and technical pathways of maritime multi-source data in recent years have been systematically reviewed, and a comprehensive technical framework from data acquisition and preprocessing to practical application has been constructed. Focusing on the data layer, application layer, and system layer, this paper comprehensively analyzes the key technologies of maritime navigation based on multi-source data. At the same time, this paper also highlights the advantages and cutting-edge methods of multi-source data in typical application scenarios—such as track extraction, target recognition, behavior detection, path planning, and collision avoidance—and analyzes their performance and adaptation strategies in different usage contexts. Through the combination of theory and engineering practice, this paper looks forward to the future development of ship intelligence and water transportation systems, providing a theoretical basis and technical support for the construction of intelligent shipping systems. Full article

Digital Entrepreneurial Capability (DEC) is the integrated and learnable capacity that equips individuals, or founding teams, to sense, evaluate, and exploit entrepreneurial opportunities within digitally intermediated, platform-centric markets. The construct synthesises four interlocking elements. First, it requires technical dexterity: mastery of data engineering, AI-driven analytics, low-code development, cloud orchestration, and cybersecurity safeguards. Second, it draws on accumulated human capital—formal education, sector experience, and tacit managerial know-how that ground vision in operational reality. Third, DEC hinges on an opportunity-seeking mindset characterised by cognitive alertness, creative problem framing, a high need for achievement, and autonomous motivation. Finally, it depends on calculated risk tolerance, encompassing the ability to price and mitigate economic, technical, algorithmic, and competitive uncertainties endemic to platform economies. When these pillars operate synergistically, entrepreneurs translate digital affordances into scalable, resilient business models; when one pillar is weak, capability bottlenecks arise and ventures falter. Because each pillar can be intentionally developed through education, deliberate practice, and ecosystem support, DEC serves as a practical roadmap for stakeholders. It now informs scholarship across entrepreneurship, information systems, innovation management, and public-policy disciplines, and guides interventions ranging from curriculum design and accelerator programming to due-diligence heuristics and national digital literacy initiatives. Full article