Search Results (242)

Introduction: Primary hyperparathyroidism (pHPT) is an endocrine disorder characterized by excessive parathyroid hormone production, typically due to adenomas, hyperplasia, or carcinoma. Preoperative imaging plays a critical role in guiding surgical planning, particularly in selecting patients for minimally invasive procedures. While first-line imaging techniques, such as ultrasound and 99mTc-sestamibi scintigraphy, are standard, advanced second-line imaging modalities like 18F-fluorocholine PET/CT (FCH-PET) and four-dimensional computed tomography (4D-CT) have emerged as valuable tools when initial diagnostics are inconclusive. Methods: This article provides an updated review and recommendations of the role of these advanced imaging techniques in localizing parathyroid adenomas. Results: FCH-PET has shown exceptional sensitivity (94% per patient, 96% per lesion) and is particularly useful in detecting small or ectopic adenomas. Despite its higher sensitivity, it can yield false positives, particularly in the presence of thyroid disease. On the other hand, 4D-CT offers detailed anatomical imaging, aiding in the identification of parathyroids in challenging cases, including recurrent disease and ectopic glands. Studies suggest that FCH-PET and 4D-CT exhibit similar diagnostic performance and could be complementary in preoperative planning of most difficult situations. Conclusions: This article also emphasizes a multimodal approach, where initial imaging is followed by advanced techniques only in cases of uncertainty. Although 18F-fluorocholine PET/CT is favored as a second-line option, 4D-CT remains invaluable for its high spatial resolution and ability to guide surgery in complex cases. Despite limitations in evidence, these imaging modalities significantly enhance the accuracy of parathyroid localization, contributing to more targeted and minimally invasive surgery. Full article

The structural health monitoring of historic buildings represents one of the most significant challenges in contemporary structural analysis, particularly for large-scale structures with accumulated damage. Obtaining reliable diagnostics is crucial yet complex due to the inherent uncertainties in both geometric definition and material properties of historic constructions, especially when structural stability may be compromised. This study presents a comprehensive structural assessment of the Bullring of the Real Maestranza de Caballería de Ronda (Spain), an emblematic 18th-century structure, through an innovative multi-technique approach aimed at evaluating its structural stability. The methodology integrates various non-destructive techniques: 3D laser scanning for precise geometric documentation, operational modal analysis (OMA) for global dynamic characterisation, experimental modal analysis (EMA) for local assessment of critical structural elements, and sonic tests (ST) to determine the elastic moduli of the principal materials that define the historic construction. The research particularly focuses on the inner ring of sandstone columns, identified as the most vulnerable structural component through initial dynamic testing. A detailed finite-element (FE) model was developed based on high-precision laser-scanning data and calibrated using experimental dynamic properties. The model’s reliability was validated through the correlation between numerical predictions and experimental observations, enabling a thorough stability analysis of the structure. Results reveal concerning stability issues in specific columns of the inner ring, identifying elements at significant risk of collapse. This finding demonstrates the effectiveness of the proposed methodology in detecting critical structural vulnerabilities in historic buildings, providing crucial information for preservation strategies. Full article

Background: Oncocytoma and angiomyolipoma (AML) are benign renal tumors that may mimic malignant lesions on imaging. With the increasing use of partial nephrectomy (PN) for renal masses, accurate preoperative characterization of these lesions is essential. This study highlights the role of partial nephrectomy as a valuable diagnostic tool in situations where imaging is inconclusive or raises concern for malignancy without definitive confirmation. In the absence of a reliable preoperative diagnosis, partial nephrectomy provides direct histologic verification with minimal perioperative morbidity. Moreover, it offers curative potential when malignancy is present. By achieving both diagnostic certainty and renal preservation, this approach is well-suited for clinical scenarios in which imaging ambiguity might otherwise result in overtreatment through radical surgery or undertreatment Material and methods: in this retrospective study, we reviewed our 5-year experience (2019–2024), 188 partial nephrectomies—including bilateral procedures and operations on solitary kidneys—using robotic and open approaches. All of these 30 tumors were solid renal masses with indeterminate imaging features or suspicious characteristics suggestive of malignancy, further underscoring the limitations of current preoperative diagnostic modalities. Results: Histopathological evaluation confirmed benign renal tumors in 30 cases, with oncocytoma diagnosed in 18 cases (16 robotic, 2 open) and AML in 12 cases (9 robotic, 3 open). Conclusions: Even when imaging raises suspicion of malignancy or remains inconclusive, many small renal masses are ultimately confirmed as benign upon histopathological examination. This study underscores the diagnostic uncertainty associated with small renal tumors and highlights the value of partial nephrectomy as a decisive diagnostic intervention. In situations where non-invasive modalities fail to provide definitive answers, partial nephrectomy offers tissue confirmation with minimal morbidity. Furthermore, when malignancy is present, this approach ensures appropriate oncologic management while preserving renal function. Our findings support the integration of this strategy into routine clinical practice, particularly when diagnostic clarity is essential for guiding safe and effective treatment. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

The Internet of Things (IoT) is rapidly advancing toward increased autonomy; however, the inherent dynamism, environmental uncertainty, device heterogeneity, and diverse data modalities pose serious challenges to its reliability and security. This paper proposes a novel framework for embedding security awareness into IoT systems—where security awareness refers to the system’s ability to detect uncertain changes and understand their impact on its security posture. While machine learning and deep learning (ML/DL) models integrated with explainable AI (XAI) methods offer capabilities for threat detection, they often lack contextual interpretation linked to system security. To bridge this gap, our framework maps XAI-generated explanations to a system’s structured security profile, enabling the identification of components affected by detected anomalies or threats. Additionally, we introduce a procedural method to compute an Importance Factor (IF) for each component, reflecting its operational criticality. This framework generates actionable insights by highlighting contextual changes, impacted components, and their respective IFs. We validate the framework using a smart irrigation IoT testbed, demonstrating its capability to enhance security awareness by tracking evolving conditions and providing real-time insights into potential Distributed Denial of Service (DDoS) attacks. Full article

The structural modal parameter identification method can be classified into time-domain and frequency-domain methods. Practically, two types of methods are characterized by different advantages, and the estimated modal parameters are always subjected to statistical uncertainties due to measurement noise. In this work, an uncertainty-based fusion method for structural mode identification is proposed to merge the advantages of different methods. The extensively applied time-domain AutoRegressive (AR) and frequency-domain Left-Matrix Fraction (LMF) models are expressed in a unified parametric model. With this unified model, a generalized framework is developed to identify the modal parameters of structures and compute variances associated with modal parameter estimates. The final modal parameter estimates are computed as the inverse-variance weighted sum of the results identified from different methods. A numerical and an experimental example demonstrate that the proposed method can obtain reliable modal parameter estimates, substantially mitigating the occurrence of extremely large estimation errors. Furthermore, the fusion method demonstrates enhanced identification capabilities, effectively reducing the likelihood of missing structural modes. Full article

The unique vegetation in riparian zones is fundamental for various ecological and socio-economic functions in these transitional areas. Sustainable management requires detailed spatial information about the occurring flora. Here, we present a Deep Learning (DL)-based approach for processing multimodal high-resolution remote sensing data (aerial RGB and near-infrared (NIR) images and elevation maps) to generate a classification map of the tidal Elbe and a section of the Rhine River (Germany). The ground truth was based on existing mappings of vegetation and biotope types. The results showed that (I) despite a large class imbalance, for the tidal Elbe, a high mean Intersection over Union (IoU) of about 78% was reached. (II) At the Rhine River, a lower mean IoU was reached due to the limited amount of training data and labelling errors. Applying transfer learning methods and labelling error correction increased the mean IoU to about 60%. (III) Early fusion of the modalities was beneficial. (IV) The performance benefits from using elevation maps and the NIR channel in addition to RGB images. (V) Model uncertainty was successfully calibrated by using temperature scaling. The generalization ability of the trained model can be improved by adding more data from future aerial surveys. Full article

Background/Objectives: Auditory capacity plays a fundamental role in human emotional development from prenatal stages and persists as the last sensory modality to fade during terminal phases. In palliative sedation, uncertainty about preserved hearing—despite potential unconsciousness—underscores the need to evaluate current care recommendations for this critical sensory dimension. This review examines European guidelines to (i) assess auditory care integration in palliative sedation protocols and (ii) propose humanization strategies for sensory-preserving end-of-life care. Methods: Narrative review of evidence from the European Palliative Sedation Repository and the European Association for Palliative Care (EAPC). Results: Three key findings emerged: (i) lack of explicit protocols for auditory care despite acknowledging environmental sound management (e.g., music, family communication); (ii) limited consensus exists regarding hearing preservation during unconsciousness. Conclusions: Although auditory perception during palliative sedation remains scientifically uncertain, the precautionary principle warrants integrating auditory care into palliative sedation through (i) family education on potential hearing preservation; (ii) therapeutic sound protocols; and (iii) staff training in sensory-inclusive practices. This approach addresses current gaps in the guidelines while enhancing patient dignity and family support during end-of-life care. Further research should clarify auditory perception thresholds during sedation. Full article

The long-term preservation of heritage structures relies on effective Structural Health Monitoring (SHM) systems, where sensor placement is key to ensuring early damage detection and guiding conservation efforts. Optimal Sensor Placement (OSP) methods offer a systematic framework to identify efficient sensor configurations, yet their application in historical buildings remains limited. Typically, OSP is driven by numerical models; however, in the context of heritage structures, these models are often affected by substantial uncertainties due to irregular geometries, heterogeneous materials, and unknown boundary conditions. In this scenario, data-driven approaches become particularly attractive as they eliminate the need for potentially unreliable models by relying directly on experimentally identified dynamic properties. This study investigates how the choice of input data influences OSP outcomes, using the Church of Santa Ana in Seville, Spain, as a representative case. Three data sources are considered: an uncalibrated numerical model, a calibrated model, and a data-driven set of modal parameters. Several OSP methods are implemented and systematically compared. The results underscore the decisive impact of the input data on the optimisation process. Although calibrated models may improve certain modal parameters, they do not necessarily translate into better sensor configurations. This highlights the potential of data-driven strategies to enhance the robustness and applicability of SHM systems in the complex and uncertain context of heritage buildings. Full article

Background/Objective: The objective of this study is to compare the relative effectiveness of combination therapy (exercise plus manual therapy) versus single-modality physiotherapy interventions for improving pain and function in patients with rotator cuff-related shoulder pain (RCRSP), using a network meta-analysis (NMA) approach. Methods: We systematically searched five electronic databases from inception to October 2023 for randomized controlled trials (RCTs) evaluating non-invasive physiotherapy interventions in adults with RCRSP. Primary outcomes included pain intensity and shoulder function, assessed at 12 weeks. A frequentist NMA was conducted to estimate standardized mean differences (SMDs) with 95% confidence intervals (CIs). Risk of bias was assessed using the Cochrane RoB 2.0 tool. Results: Eleven RCTs (n = 548) were included. Combination therapy demonstrated the greatest improvement in function (SMD = −1.02; 95% CI: −2.59 to 0.56) and pain (SMD = −1.05; 95% CI: −2.41 to 0.30), although the wide confidence intervals crossing the null suggests statistical uncertainty. Exercise therapy alone showed moderate functional improvement (SMD = −0.41; 95% CI: −1.64 to 0.82), and Kinesio taping (KT) provided moderate pain relief (SMD = −0.53; 95% CI: −1.81 to 0.75). While these effects approached known minimal clinically important difference (MCID) thresholds (e.g., DASH: 10–15; VAS: 1.4–2.0), they did not reach statistical significance. Conclusions: Based on 11 RCTs, combination therapy (exercise plus manual therapy) appears to be the most effective non-invasive approach for improving pain and function in patients with RCRSP. However, the wide confidence intervals highlight uncertainty. Further large-scale and long-term trials are warranted to confirm its clinical utility and sustainability. Full article

Microbial biofilms present a formidable challenge in ophthalmology. Their intrinsic resistance to antibiotics and evasion of host immune defenses significantly complicate treatments for ocular infections such as conjunctivitis, keratitis, blepharitis, and endophthalmitis. These infections are often caused by pathogens, including Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, particularly in patients using contact lenses or intraocular implants—devices that serve as surfaces for biofilm formation. The global rise in antimicrobial resistance has intensified the search for alternative treatment modalities. In this regard, plant-derived antimicrobials have emerged as promising candidates demonstrating broad-spectrum antimicrobial and antibiofilm activity through different mechanisms from those of conventional antibiotics. These mechanisms include inhibiting quorum sensing, disrupting established biofilm matrices, and interfering with microbial adhesion and communication. However, the clinical translation of phytochemicals faces significant barriers, including variability in chemical composition due to environmental and genetic factors, difficulties in standardization and reproducibility, poor water solubility and ocular bioavailability, and a lack of robust clinical trials evaluating their efficacy and safety in ophthalmic settings. Furthermore, regulatory uncertainties and the absence of unified guidelines for approving plant-derived formulations further hinder their integration into evidence-based ophthalmic practice. This review synthesizes the current knowledge on the pathogenesis and treatment of biofilm-associated ocular infections, critically evaluating plant-based antimicrobials as emerging therapeutic agents. Notably, resveratrol, curcumin, abietic acid, and selected essential oils demonstrated notable antibiofilm activity against S. aureus, P. aeruginosa, and C. albicans. These findings support the potential of phytochemicals as adjunctive or alternative agents in managing biofilm-associated ocular infections. By highlighting both their therapeutic promise and translational limitations, this review contributes to the ongoing discourse on sustainable, innovative approaches to managing antibiotic-resistant ocular infections. Full article

Aortic stenosis (AS), the most common valvular heart disease, is traditionally graded based on several echocardiographic quantitative parameters, such as aortic valve area (AVA), mean pressure gradient (MPG), and peak jet velocity (Vmax). This systematic review evaluates the clinical significance and prognostic implications of discordant high-gradient AS (DHG-AS), a distinct hemodynamic phenotype characterized by elevated MPG despite a preserved AVA (>1.0 cm²). Although often overlooked, DHG-AS presents unique diagnostic and therapeutic challenges, as high gradients remain a strong predictor of adverse outcomes despite moderately reduced AVA. Sixty-three studies were included following rigorous selection and quality assessment of the key studies. Prognostic outcomes across five key studies were discrepant: some showed better survival in DHG-AS compared to concordant high-gradient AS (CHG-AS), while others reported similar or worse outcomes. For instance, a retrospective observational study including 3209 patients with AS found higher mortality in CHG-AS (unadjusted HR: 1.4; 95% CI: 1.1 to 1.7), whereas another retrospective multicenter study including 2724 patients with AS observed worse outcomes in DHG-AS (adjusted HR: 1.59; 95% CI: 1.04 to 2.56). These discrepancies may stem from delays in intervention or heterogeneity in study populations. Despite the diagnostic ambiguity, the presence of high gradients warrants careful evaluation, aggressive risk stratification, and timely management. Current guidelines recommend a multimodal approach combining echocardiography, computed tomography (CT) calcium scoring, transesophageal echocardiography (TEE) planimetry, and, when needed, catheterization. Anatomic AVA assessment by TEE, CT, and cardiac magnetic resonance imaging (CMR) can improve diagnostic accuracy by directly visualizing valve morphology and planimetry-based AVA, helping to clarify the true severity in discordant cases. However, these modalities are limited by factors such as image quality (especially with TEE), radiation exposure and contrast use (in CT), and availability or contraindications (in CMR). Management remains largely based on CHG-AS protocols, with intervention primarily guided by transvalvular gradient and symptom burden. The variability among the different guidelines in defining severity and therapeutic thresholds highlights the need for tailored approaches in DHG-AS. DHG-AS is clinically relevant and associated with substantial prognostic uncertainty. Timely recognition and individualized treatment could improve outcomes in this complex subgroup. Full article

Background: BCI and AI-driven image segmentation are revolutionizing precision neurosurgery by enhancing surgical accuracy, reducing human error, and improving patient outcomes. Methods: This systematic review explores the integration of AI techniques—particularly DL and CNNs—with neuroimaging modalities such as MRI, CT, EEG, and ECoG for automated brain mapping and tissue classification. Eligible clinical and computational studies, primarily published between 2015 and 2025, were identified via PubMed, Scopus, and IEEE Xplore. The review follows PRISMA guidelines and is registered with the OSF (registration number: J59CY). Results: AI-based segmentation methods have demonstrated Dice similarity coefficients exceeding 0.91 in glioma boundary delineation and tumor segmentation tasks. Concurrently, BCI systems leveraging EEG and SSVEP paradigms have achieved information transfer rates surpassing 22.5 bits/min, enabling high-speed neural decoding with sub-second latency. We critically evaluate real-time neural signal processing pipelines and AI-guided surgical robotics, emphasizing clinical performance and architectural constraints. Integrated systems improve targeting precision and postoperative recovery across select neurosurgical applications. Conclusions: This review consolidates recent advancements in BCI and AI-driven medical imaging, identifies barriers to clinical adoption—including signal reliability, latency bottlenecks, and ethical uncertainties—and outlines research pathways essential for realizing closed-loop, intelligent neurosurgical platforms. Full article

To accurately monitor the operational state of Internet Data Centers (IDCs) and fulfill integrated management objectives, this paper introduces a bidirectional cross-attention LSTM–Informer with uncertainty-aware multi-task learning framework (BiCA-LI) for time series analysis. The architecture employs dual-branch temporal encoders—long short-term memory (LSTM) and Informer—to extract local transient dynamics and global long-term dependencies, respectively. A bidirectional cross-attention module is subsequently designed to synergistically fuse multi-scale temporal representations. Finally, task-specific regression and classification heads generate predictive outputs and anomaly detection results, while an uncertainty-aware dynamic loss weighting strategy adaptively balances task-specific gradients during training. Experimental results validate BiCA-LI’s superior performance across dual objectives. In regression tasks, it achieves an MAE of 0.086, MSE of 0.014, and RMSE of 0.117. For classification, the model attains 99.5% accuracy, 100% precision, and an AUC score of 0.950, demonstrating substantial improvements over standalone LSTM and Informer baselines. The dual-encoder design, coupled with cross-modal attention fusion and gradient-aware loss optimization, enables robust joint modeling of heterogeneous temporal patterns. This methodology establishes a scalable paradigm for intelligent IDC operations, enabling real-time anomaly mitigation and resource orchestration in energy-intensive infrastructures. Full article

Since the advent of recombinant DNA technologies and leading up to the clinical approval of T cell engager blinatumomab, the modular design of therapeutic antibodies has enabled the fusion of antibody fragments with proteins of various functionalities. This has resulted in an expansive array of possible mechanisms of action and has given birth to fragment-based antibodies (fbAbs) with immune cell engager modalities. In searchable databases, the preclinical development of these antibodies has shown promise; however, clinical outcomes and restructuring efforts involving these agents have produced mixed results and uncertainties. Amid budgetary cuts in both academia and industry, critical planning and evaluation of drug R&D would be more essential than ever before. While many reviews have provided outstanding summaries of preclinical phase fbAbs and cataloged relevant clinical trials, to date, very few of the articles in searchable databases have comprehensively reviewed the details of clinical outcomes along with the underlying reasons or potential explanations for the success and failures of these fbAb drug products. To fill the gap, in this review, we seek to provide the readers with clinically driven insights, accompanied by translational and mechanistic studies, on the current landscape of fragment-based immune cell engager antibodies in treating cancer, infectious, and autoimmune diseases. Full article