Search Results (2,672)

This study investigates the application of computational hemodynamic modeling, involving both FSI and CFD models, using SimVascular to simulate blood flow in the right pulmonary artery for patient-specific cardiovascular assessment. The artery’s three-dimensional geometry was reconstructed from a computed tomography (CT) image, and pressure measurements from a CardioMEMS™ device were used as clinical ground truth for validation. To represent the arterial hemodynamics, we initially formulated a fluid–structure interaction (FSI) approach to capture wall mechanics. However, given the high computational cost of fully patient-specific FSI simulations for routine clinical decision-making, we evaluated the validity of key simplifications by assuming rigid vessel walls coupled with a three-element Windkessel (3WK) model and applying a half-sine inflow waveform derived from the patient’s cardiac output. These simplifications yielded results with minimal error: the rigid-wall assumption introduced a 1.1% deviation, while the idealized waveform resulted in a 0.56 mmHg offset. Crucially, while wall rigidity was acceptable, we found that arterial compliance in the boundary conditions is non-negotiable; reducing the model to a pure resistance approach resulted in non-physiological pressures (130 mmHg). A subsequent parametric analysis examined how varying resistance (R) and compliance (C) distinctively alter the pressure waveform morphology. The results underscore the potential of combining remote monitoring data with validated computational simulations to deepen the understanding of cardiovascular dynamics and enhance diagnostic and therapeutic approaches for cardiovascular diseases. Full article

Sutureless aortic bioprostheses have become an established alternative for surgical aortic valve replacement, particularly in elderly and high-risk patients. The Perceval (Livanova) valve, the most widely studied sutureless device, offers favorable hemodynamic performance and reduced operative times but introduces specific challenges when prosthetic valve endocarditis (PVE) occurs. Although the incidence of Perceval PVE is low and comparable to that of conventional bioprostheses, this complication is associated with substantial morbidity and mortality. Diagnosis is often complex due to acoustic shadowing on echocardiography, making multimodality imaging with transesophageal echocardiography, cardiac computed tomography, and [18F]-FDG PET/CT essential. Microbiological profiles resemble those of other biological prostheses, but perivalvular extension and early mechanical instability are frequent. Management follows general PVE principles but often requires early surgical intervention because of the valve’s reliance on radial fixation. This review summarizes current evidence on epidemiology, microbiology, diagnostic strategies, treatment, and prognosis of endocarditis involving the Perceval valve, and identifies areas for future research. Full article

Fault diagnosis in modern power systems increasingly depends on unstructured operation and maintenance (O&M) logs, yet real-world logs are often small in scale and highly imbalanced across fault types, which degrades the generalizability of standard neural models. This paper proposes a graph-augmented diagnostic framework that integrates imbalance-aware knowledge extraction with interpretable reasoning. The framework consists of three stages: (1) domain adaptation of a BERT–BiLSTM–CRF NER model and a BERT–MLP RE model using an imbalance-aware training recipe that combines Low-Rank Adaptation (LoRA), a mixed focal–range loss, and undersampling; (2) construction of a power-system knowledge graph that organizes extracted entities and relations (e.g., fault devices, abnormal phenomena, causes, and handling measures); and (3) a graph-augmented assistant agent that reuses the NER model as a graph-aware retriever within a retrieval-augmented generation (RAG) architecture to support contextualized and interpretable diagnostic reasoning. Experiments on 3921 real-world fault-processing logs show consistent gains: NER reaches 92.0% accuracy and 71.3% Macro-F1 (vs. 80.3% and 63.2%), and RE achieves 88.0% accuracy and 70.1% F1 (vs. 82.1% and 60.4%), while reducing average training time per epoch by about 18%. These results demonstrate an efficient and practical path toward robust log-based fault diagnosis under scarce and imbalanced data. Full article

The Internet of Medical Things (IoMT) has become a core component of modern healthcare infrastructures, enabling continuous patient monitoring, remote diagnostics, and data-driven clinical decision-making. Despite these advances, authentication in IoMT environments remains a critical security challenge, intensified by strict resource constraints of medical devices and the emerging threat posed by quantum computing to classical cryptographic techniques. This systematic review investigates authentication mechanisms in IoMT from both post-quantum and system-level perspectives. A structured literature review was conducted using a PRISMA-informed methodology across major scientific databases, including IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and MDPI. From an initial set of 95 records, 63 studies were selected for qualitative synthesis following screening and eligibility assessment. To organise existing research, this study introduces a multi-dimensional classification framework that categorises authentication solutions according to cryptographic paradigm (classical, hybrid, and post-quantum), deployment architecture, system objectives, and clinical operational constraints. The comparative synthesis demonstrates important trade-offs between security strength, latency, computational overhead, and energy consumption that are frequently underexplored in the existing literature. Furthermore, the analysis identifies key research gaps related to scalability in heterogeneous medical environments, trust establishment across administrative and clinical domains, usability under strict timing constraints, and resilience against quantum-capable adversaries. Based on these findings, future research directions are outlined toward adaptive, lightweight, and context-aware post-quantum authentication frameworks designed for real-world IoMT deployments. Limitations of this review include restriction to English-language publications and selected databases. This study received no external funding, and the review protocol was not formally registered. Full article

Acute gastrointestinal bleeding (GIB) is one of the most common and dangerous condition in patients admitted in Emergency Departments. The incidence and the mortality of acute GIB remain significant, although some positive trends were observed in recent years. Initial evaluation of GIB needs an accurate assessment of the medical history and the clinical presentation. Physicians should pay attention about the presence of hemorrhagic shock that usually requires urgent diagnosis and treatment. Only a prompt diagnostic approach can identify the source of bleeding and improve the outcomes in acute GIB patients. Risk stratification and time of endoscopy are fundamental issues in the management of upper and lower GIB. Small bowel capsule enteroscopy (SBCE) and device-assisted enteroscopy (DAE) are the basic approaches to suspected small bowel bleeding. Machine Learning Prognostic Models have been proposed, such as alternative prognostic tools in GIB, but they are currently recommended only to identify low-risk outpatients. Full article

Francisella tularensis, the causative agent of tularemia, is a highly infectious Category A biothreat agent characterized by an exceptionally low infectious dose and diverse transmission routes. Due to the pathogen’s fastidious growth requirements and the high risk of laboratory-acquired infections, traditional cultivation methods are often protracted and hazardous. Consequently, the development of rapid and sensitive diagnostic tools is paramount. This manuscript provides a comprehensive overview of the current landscape of immunoassays, with a specific focus on the evolution from standard laboratory techniques to advanced biosensors. We detail the critical phases of antigen preparation, including high-pressure homogenization and sonication, and the generation of high-affinity polyclonal and monoclonal antibodies. Furthermore, we evaluate the implementation of novel biosensor-like devices, such as electrochemiluminescence and Surface-Enhanced Raman Scattering platforms, designed for point-of-care and field-ready scenarios. By synthesizing recent advancements in nanomaterial-enhanced recognition and microfluidic integration, this review emphasizes the pivotal role of these technologies in achieving early detection and mitigating the impact of both natural outbreaks and potential deliberate misuse of F. tularensis. Full article

Point-of-care ultrasound (POCUS) is portable and radiation-free, but its clinical reliability is constrained by operator-dependent image acquisition and the limited scalability of expert quality assurance (QA) review. As handheld devices proliferate faster than mentorship capacity, trainees increasingly rely on heterogeneous free open access medical education (FOAMed) resources that rarely provide real-time psychomotor feedback. We conducted a structured narrative review (MEDLINE, Embase, Scopus, and Web of Science; last searched on 23 February 2026), with searches performed by H.H. and independently checked by J.J.P. (both POCUS-trained clinicians). After screening, 31 studies were included. We synthesized evidence on artificial intelligence (AI) systems that support bedside image acquisition and automate QA. The primary synthesis centered on key prospective or comparative clinical evaluations of AI-guided acquisition across echocardiography, focused assessment with sonography in trauma, abdominal aortic aneurysm screening, and lung ultrasound, complemented by peer-reviewed studies of FOAMed appraisal tools and online resource quality. These evaluations suggest that real-time probe guidance, view recognition, anatomy labeling, and automated capture may enable novices, after brief training, to acquire diagnostically adequate images for narrowly defined tasks. Early reports of automated QA scoring and program-level triage for expert review suggest potential to reduce expert workload and shorten feedback cycles, but external validation, generalizability across devices and patient habitus, and patient-centered outcomes remain limited. Acquisition-focused AI may therefore serve as an upstream “digital mentor” to improve novice image acquisition. We propose a practical pathway that integrates curated FOAMed resources and simulation with AI-guided bedside acquisition and continuous QA governance for safe deployment. Full article

Bolted joints are extensively used in mechanical and civil engineering structures because of their low cost, standardized design, and ease of installation and maintenance. The preload in a bolted connection is critical for ensuring joint stability and service reliability; however, preload degradation commonly occurs under complex operating conditions, particularly in environments involving sustained or cyclic vibration. To tackle this problem, this study proposes a portable, force-controllable percussion system for bolt looseness detection. The system integrates a solenoid-driven automatic percussion device, acoustic signal acquisition, onboard data-processing, and real-time visualization of diagnostic results. By adjusting the driving current of the solenoid, the percussion force can be accurately controlled, ensuring stable and repeatable excitation. Benefiting from its compact structure and low cost, the proposed system is suitable for real-time, on-site inspection of bolt looseness. Furthermore, a novel audio-processing approach based on a Siamese Capsule Network is developed to identify bolt looseness conditions. Compared with existing percussion-based techniques, the proposed method exhibits improved classification performance, especially in recognizing bolt states that are unseen during training. Exploratory experimental results validate the effectiveness of the proposed system and demonstrate its strong potential for practical engineering applications. Full article

Background: Athletes may experience paroxysmal arrhythmias that occur during exercise and are difficult to document using standard diagnostic modalities. Such arrhythmias are often unpredictable, transient, and cannot be reproduced during routine exercise testing or ambulatory electrocardiographic monitoring, leading to prolonged diagnostic pathways and uncertainty regarding management. Methods: This case series presents ten athletes in whom clinically relevant paroxysmal arrhythmias were initially detected using commercially available wearable digital devices, primarily chest-strap heart rate monitors and smartwatches. Results: In most cases, arrhythmias could not be documented using conventional diagnostic methods despite repeated investigations. Most presented athletes were referred for invasive electrophysiological study, which confirmed supraventricular arrhythmias and enabled curative catheter ablation based solely on data obtained from wearable devices. The use of digital devices substantially shortened the time to diagnosis and treatment, reduced diagnostic burden, and allowed definitive therapy in symptomatic athletes. Conclusions: Wearable technology, particularly chest-strap heart rate monitors, may play an important role in the diagnostic evaluation of exercise-induced paroxysmal arrhythmias when standard methods fail. Full article

Background/Objectives: The effective prevention and control of healthcare-associated infections (HAIs) require the active engagement of clinical staff, which depends on strong relationships between the Infection Prevention and Control Team (IPCT) and frontline healthcare personnel. The role of the Infection Control Physician (ICP) as a clinical leader is essential for supporting evidence-based practice and fostering collaboration. This study aimed to demonstrate the impact of leadership-oriented interventions—particularly the introduction of ICP consultations in hospital wards—on HAI surveillance quality. Methods: A retrospective observational quasi-experimental study was conducted in a single hospital in southern Poland between 2017 and 2024, excluding 2020–2021 due to the COVID-19 pandemic. HAI surveillance followed the ECDC HAI-Net methodology. The study included all hospitalized patients in wards where invasive medical devices or invasive procedures were used. The intervention consisted of expanding the IPCT, increasing managerial support, extending infection control nurses’ competencies, and implementing routine ICP medical consultations. Changes in HAI incidence rates between the pre-intervention (pre-IP) and post-intervention (post-IP) periods were analyzed for catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), and central line-associated bloodstream infections (CLABSI), expressed per 1000 device-days. Results: The overall device utilization increased from 0.44 to 0.54 per 1000 patient-days in the post-IP period. The utilization of microbiological diagnostic tests more than doubled, with marked increases in blood cultures (6.4% vs. 15.5%) and urine cultures (7.7% vs. 11.0%). No IPCT consultations occurred in the pre-IP period, while 874 consultations were recorded in the post-IP period. Th incidence rates for CAUTI and VAP increased (1.4 to 3.1 and 11.7 to 24.6 per 1000 device-days, respectively). The CLABSI incidence showed no significant overall change. Conclusions: Structural and functional changes in the IPCT, combined with the introduction of ICP consultations, substantially enhanced the quality and completeness of HAI surveillance in the analyzed hospital. The findings highlight the importance of leadership-driven engagement in improving infection prevention and control systems. Full article

Dual-axis rotational inertial navigation systems (DRINSs) have been widely used in marine navigation due to their high accuracy. However, the long-term operation of a DRINS over weeks poses a significant challenge to its reliability. In order to address the fault diagnosis challenges faced by DRINSs on long-endurance vessels in global navigation satellite system (GNSS)-denied environments, this paper proposes a fault diagnosis algorithm for redundant DRINSs based on geometric constraint observation. The mechanization of dual DRINSs is implemented using a globally referenced framework. A residual-normalized strong tracking filter based on geometric constraint observation is employed to estimate the fault states of the dual DRINSs. A highly robust fault diagnosis method is proposed to detect and diagnose faults in the inertial devices of dual DRINSs. The experimental results show that the proposed algorithm exhibits excellent performance with a diagnostic accuracy of 98.67% and low diagnostic delay. Full article

The rapid growth of Internet of Medical Things (IoMT) devices has revolutionized diagnostics and patient care within smart healthcare networks. However, this progress has also expanded the attack surface due to the heterogeneity and interconnectivity of medical devices. To overcome the limitations of traditional batch-trained security models, this study proposes an adaptive online intrusion detection framework designed for real-time operation in dynamic healthcare environments. The system combines Leveraging Bagging with Hoeffding Tree classifiers for incremental learning while integrating the Page–Hinkley test to detect and adapt to concept drift in evolving attack patterns. A modular and scalable network architecture supports centralized monitoring and ensures seamless interoperability across various IoMT protocols. Implemented within a low-latency, high-throughput stream-processing pipeline, the framework meets the stringent clinical requirements for responsiveness and reliability. To simulate streaming conditions, we evaluated the model using the CICIoMT2024 dataset, presenting one instance at a time in random order to reflect dynamic, real-time traffic in IoMT networks. Experimental results demonstrate exceptional performance, achieving accuracies of 0.9963 for binary classification, 0.9949 for six-class detection, and 0.9860 for nineteen-class categorization. These results underscore the framework’s practical efficacy in protecting modern healthcare infrastructures from evolving cyber threats. Full article

The continuous expansion of the Internet of Things (IoT) has intensified the need to evaluate and guarantee the quality of entropy sources used in random number generation, an essential element in securing communications used in IoT ecosystems. This work presents an automated and web-based framework designed to execute and analyze the results of statistical tests defined in the NIST SP 800-22 standard, enabling systematic assessment of entropy sources and random numbers generators in IoT devices and environments. The proposed system integrates a Python-based backend built upon an optimized implementation of the original NIST suite, along with an intuitive web interface that facilitates configuration, monitoring, and parallel execution of tests through Representational State Transfer (REST) endpoints. Session management based on Redis ensures reliable and concurrent operation of multiple users or devices while maintaining isolation and data integrity. To demonstrate its applicability, an emulated IoT ecosystem was implemented in which multiple virtual devices periodically and asynchronously request real-time validation of their local random numbers generators. The obtained results confirm the system’s capability to detect deficiencies in pseudo random generators and validate true random number sources, highlighting its potential as a diagnostic and verification tool for distributed IoT security systems. The tool developed in this work is fully accessible to the public, allowing researchers, engineers, and practitioners to evaluate random number generators without requiring specialized hardware or proprietary software. Full article

Venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, is a significant and preventable cause of morbidity and mortality worldwide. Despite the existence of clinical prediction models, biomarker-based risk assessments, and imaging techniques, gaps remain in accurately identifying and managing high-risk patients. In recent years, artificial intelligence has emerged as a transformative tool in healthcare, offering promising applications for enhancing VTE prevention strategies. This narrative review synthesizes current evidence on the use of artificial intelligence (AI) technologies including machine learning (ML), deep learning (DL), and natural language processing (NLP). We explore how supervised ML algorithms, such as random forests, support vector machines, and gradient boosting, improve predictive performance compared to traditional models by capturing complex, nonlinear relationships within electronic health record data. We also examine the role of DL models, particularly convolutional neural networks, in interpreting imaging data, achieving diagnostic accuracies comparable to expert radiologists. Additionally, the review highlights NLP applications in extracting risk-relevant information from unstructured clinical notes and the emerging integration of wearable device data and time-series analysis for dynamic risk assessment. We argue that the successful integration of AI into routine VTE prevention workflows requires rigorous prospective validation, cross-institutional collaboration, and thoughtful implementation into clinical decision support systems. Full article

Cardiovascular disease has become the leading cause of death worldwide, underscoring the urgent need for widespread cardiac monitoring, while the Electrocardiogram (ECG) remains the diagnostic gold standard, the complexity of its acquisition limits its long-term feasibility. In contrast, Photoplethysmography (PPG), ubiquitous in wearable devices, is increasingly adopted due to its accessibility. However, synthesizing ECG from PPG poses an intrinsically ill-posed inverse problem. Existing purely data-driven paradigms often neglect underlying biophysical mechanisms, resulting in a lack of physical constraints and interpretability, which renders them prone to generating non-physiological hallucinations. To address this, we propose PhysDiff-LBM, a novel physics-aware framework that incorporates Lattice Boltzmann hemodynamic constraints into a conditional diffusion model. Employing a dual-stream architecture, our framework captures high-frequency morphological details via a cross-attention-guided diffusion model with region-wise adaptability. Synergistically, we physically regularize the ECG synthesis by leveraging the mesoscopic streaming and collision operators of LBM. By forcing the synthesized waveform gradients to evolve consistently with hemodynamic momentum, this mechanism constrains the model to strictly adhere to the fluid dynamic conservation laws governing pulse wave propagation. Experimental results demonstrate that our method achieves superior signal fidelity and exhibits significant advantages in downstream clinical applications. Full article