Search Results (1,036)

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part of a higher education campus. Five sampling points were monitored during three campaigns between January and March 2026. At each point, pH, electrical conductivity, temperature, oxidation–reduction potential, and total dissolved solids were recorded at 10 s intervals over approximately 10 min monitoring windows using a multiparameter probe integrated into an IoT-enabled data acquisition workflow. Microbiological analyses were performed on groundwater samples as complementary information. Treated municipal water showed lower mineralization, narrower parameter ranges, and higher oxidation–reduction potential, reflecting source-water characteristics, treatment, and operational control. Groundwater showed higher mineralization, lower oxidation–reduction potential, and greater variability among sampling points and campaigns, consistent with stronger local hydrogeochemical and operational influences. The repeated short-interval readings provided more detailed physicochemical profiles than isolated spot measurements, although the short monitoring windows do not represent continuous long-term high-frequency monitoring. Overall, the results support standardized IoT-enabled sensor-based monitoring as a complementary tool for short-term water-quality assessment and indicate the need for longer seasonal datasets and laboratory confirmation. Full article

A circulating tumor DNA (ctDNA) assay is an emerging non-invasive diagnostic approach providing real-time insights into the heterogeneous tumor molecular landscape of advanced prostate cancer, overcoming the limitations of traditional tissue biopsies and PSA. Detection methods include droplet digital PCR, next-generation sequencing, and new epigenomic and fragmentomic strategies (investigational) designed to improve sensitivity in cases of low ctDNA shedding. While ctDNA’s role in localized prostate cancer is limited, it offers significant prognostic value in metastatic cases, where high ctDNA levels correlate with shorter survival. Additionally, longitudinal ctDNA monitoring can predict treatment response and identify emerging resistance mechanisms, including androgen receptor alterations associated with androgen receptor pathway inhibitor therapy and BRCA reversion mutations linked to PARP inhibitors. Importantly, liquid biopsy enables genomic characterization to inform treatment decision-making, particularly in clinical scenarios where tissue biopsy is challenging, such as bone-only disease. However, the widespread clinical implementation of ctDNA analysis is hindered by several analytical challenges, including low sensitivity in localized disease and low disease burden, and the risk of false positives due to clonal hematopoiesis. Furthermore, greater efforts are required to standardize pre-analytical workflows and post-analytical data interpretation and reporting across institutions. This review aims to summarize the evolving role of cfDNA technologies in localized and advanced prostate cancer, highlighting their prognostic and predictive value and their role in uncovering mechanisms of treatment resistance. Full article

Background: Pre-existing diabetes mellitus is prevalent among critically ill adults and can influence initial glycemic targets, therapeutic decisions, and early risk stratification in the intensive care unit (ICU). However, diabetes status may be distributed across heterogeneous electronic health record (EHR) sources and may be incomplete at the time of ICU admission, particularly for inter-facility transfers. Methods: Using the public WiDS Datathon 2021 tabular release derived from the Global Open-Source Severity of Illness Score (GOSSIS) initiative, we conducted a retrospective machine-learning benchmarking study for admission-time identification of documented diabetes status in ICU patients. Candidate predictors included demographics, admission characteristics, anthropometrics, day-1 physiologic and laboratory summaries, APACHE-related variables, comorbidity indicators, and site descriptors. We compared CatBoost, random forest, tuned XGBoost, tuned LightGBM, histogram-based gradient boosting, and a soft-voting ensemble combining XGBoost, LightGBM, and histogram-based gradient boosting. Because class imbalance was a central concern, the final workflow emphasized model-intrinsic class weighting and threshold-aware evaluation rather than synthetic oversampling. Results: In the primary leakage-mitigated random validation split, the voting ensemble achieved the highest overall balance, with AUROC 0.8539, precision 0.5671, recall 0.6690, and F1-score 0.6138. Tuned LightGBM was the most sensitivity-oriented individual model, achieving recall 0.7677 and AUROC 0.8537, although with lower precision and a less favorable Brier score. Ablation analyses clarified the source of this performance: removing leakage-prone and APACHE-related variables caused only modest decreases in discrimination, whereas the strict reduced model that also excluded glucose-like predictors produced a marked decline, with LightGBM AUROC falling to 0.7432 and the voting ensemble AUROC falling to 0.7448. These findings, together with SHAP analyses identifying day-1 glucose maximum, day-1 glucose minimum, BMI, age, hemoglobin, and related clinical variables as major contributors, indicate that glucose-related admission variables remained the dominant predictive signal. In grouped hospital validation, tuned LightGBM maintained recall of 0.7684 while AUROC decreased modestly to 0.8443, indicating preserved case detection under stricter site separation but reduced precision. Precision–recall analysis further showed that average precision decreased from 0.622 under random validation to 0.551 under grouped validation; at a high-sensitivity grouped-site operating point, a probability threshold of 0.4537 achieved recall of 0.8001 with precision of 0.4314. Calibration curves and Brier scores showed that predicted probabilities were imperfectly calibrated. Conclusions: Although the dominance of glucose-related predictors is clinically plausible for identifying documented diabetes status, early glycemic measurements in critically ill patients may also partly capture acute stress physiology, treatment-related effects, monitoring intensity, or other forms of acute dysglycemia rather than chronic diabetes status alone. Therefore, these findings support gradient-boosted and ensemble models as reproducible tools for ICU admission-time phenotyping of documented diabetes status, but the proposed system should be interpreted primarily as a screening-oriented phenotyping aid for chart review, cohort enrichment, or workflow support, not as a stand-alone diagnostic tool. Further external validation, recalibration, threshold selection matched to intended use, and clinical review are needed before deployment. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide despite advances in screening, navigational bronchoscopy, and systemic therapies. Diagnostic and therapeutic limitations persist, including uncertainty regarding intraprocedural tissue adequacy during biopsy sampling and constraints of existing ablative modalities for tumors located near critical thoracic structures. This review examines two emerging technologies: Full-Field Optical Coherence Tomography-based Dynamic Cell Imaging (DCI) and monopolar biphasic Pulsed Electric Field (PEF) ablation as complementary emerging technologies that may address these gaps. The Van Gogh™ Microscopy System (CellTivity Scientific, Inc.) utilizes DCI to enable real-time visualization of cellular metabolic activity without tissue destruction, providing functional information regarding tissue viability and microstructural morphology. The Aliya^® PEF ablation system (Galvanize Therapeutics, Inc.) delivers biphasic high-voltage electrical pulses that induce non-thermal tumor cell death while preserving extracellular matrix architecture, potentially allowing treatment near sensitive thoracic structures such as airways, vasculature, and pleura. Early preclinical studies and initial clinical experience suggest that DCI can facilitate rapid intraprocedural assessment of biopsy adequacy, while PEF ablation may provide reproducible focal tumor destruction with a favorable safety profile near critical structures. Although the current evidence base remains limited to early-phase studies and feasibility trials, the convergence of real-time biologic tissue assessment with structurally preserving ablation technologies introduces the possibility of integrating diagnostic confirmation and local therapy within a single procedural workflow. This review summarizes the mechanistic rationale, emerging evidence, and potential clinical applications of these technologies and proposes a conceptual “See and Strike” framework within these two emerging technologies. The methodological limitations, workflow considerations, and future research directions required to validate this approach are also discussed. Prospective multicenter trials and long-term oncologic outcomes will be necessary before widespread clinical adoption. Full article

Computational clearing (CC) enhances widefield (WF) fluorescence microscopy by suppressing out-of-focus haze and autofluorescence, yielding semi-confocal quality images suitable for segmentation and image-based phenotyping. Here, we propose an “image tracing” workflow for inflammatory mouse intestinal organoids (mIOs) using paired CC and WF images to generate a differential signal (CC − WF). mIOs were derived from intestinal crypts of Lgr5-EGFP stem cell reporter mice and expanded under epidermal growth factor, Noggin, and R-spondin (ENR) conditions. Inflammation was induced by dextran sulfate sodium (DSS) treatment. CC processing enhanced phalloidin-stained apical F-actin and improved EGFP signals by reducing background noise, enabling robust segmentation and quantitative extraction of image morphometrics including area, circularity, and perimeter. CC-WF vectors derived from three-dimensional area–perimeter–circularity plots sensitively captured DSS-induced epithelial disruption analogous to a leaky-epithelium phenotype. Transcriptomic analysis by RNA-seq of DSS-treated mIOs revealed upregulation of inflammatory pathways including TNF-α signaling via NF-κB and IL-6/JAK/STAT3, aligning with microscopy findings. In a proof-of-concept demonstration using phalloidin-stained fluorescence images, ROC analysis of the CC-WF workflow achieved an AUC = 0.95 with 87.5% sensitivity and 92.9% specificity in distinguishing intact from injured mIOs. Full article

Cervical cancer remains a major global health challenge, particularly in low- and middle-income countries, where access to screening, vaccination, and timely treatment may be limited. Cervical cytology has played an important historical role in prevention, but it is labor-intensive, time-consuming, and subject to observer variability and limited sensitivity. In many contemporary screening programs, HPV testing is now used as the primary screening test, while cytology is used mainly for the triage of HPV-positive women. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown considerable potential in medical image analysis and computer-aided diagnosis. This review summarizes current applications of AI in cervical cytology and related diagnostic workflows, including automated and assisted slide screening, liquid-based cytology, the triage of equivocal or HPV-positive cases, and colposcopy support. Across these settings, AI-assisted systems may improve efficiency, standardization, and diagnostic consistency, and may reduce workload in resource-constrained environments. However, the evidence is heterogeneous, and important challenges remain, including the need for large and diverse datasets, prospective validation, regulatory approval, digital infrastructure, workflow integration, and the resolution of ethical and legal issues. AI should therefore be regarded as a promising adjunct to human expertise rather than a replacement in cervical cytology and related clinical diagnostic pathways. Full article

Radiotherapy simulation is essential for accurately targeting tumors while preserving healthy tissue, ensuring treatment precision and safety. This study aimed to develop an integrated radiotherapy simulation system capable of automated segmentation, dose estimation, and collision detection within a virtual planning environment to enhance efficiency and reduce costs in radiotherapy treatment planning. The Point Transformer model was applied to organ point cloud data derived from CT medical imaging for automated segmentation. Farthest point sampling (FPS) was employed to downsample the data before training. To enhance the accuracy and anatomical fidelity of the AI-generated segmentation results, reconstruction and refinement algorithms, including k-d tree, outlier removal, marching cubes, and surface smoothing, were implemented. Beam penetration simulation with the ray casting algorithm was employed for correction-based dose estimation. A collision detection module was incorporated to identify potential machine–machine or machine–patient interactions. The entire workflow was executed within a Unity 3D-based virtual simulation environment. As a result, the Point Transformer model demonstrated high segmentation accuracy, achieving Dice scores of 93.86 $\pm$ 1.50% for single-organ and 91.86 $\pm$ 3.25% for multi-organ cases, surpassing the performance of PointNet++. Applying ray casting for the refined surface meshes generated through post-processing enabled accurate dose estimation with discrepancies of 3.5% (brain), 5.9% (liver), and 13.8% (lung) compared to a Pinnacle TPS. The proposed method provides a low-cost and adaptable solution that enables easy modification and further development, making it particularly suitable for widespread applications in radiotherapy research, education, and clinical workflow optimization. Full article

This study establishes an integrated qualification workflow combining mechanical testing, microstructural characterization, and statistically defined eddy current testing (ECT) on the same material heats to provide a coherent and traceable material qualification methodology. Forged 316 and rolled 304L were fully annealed and subsequently subjected to a 700 °C/1 h low-temperature stress-relief (recovery) treatment. Room-temperature tensile testing and Charpy impact testing at room and cryogenic temperatures were performed alongside optical and electron microscopy to quantify grain size, δ-ferrite content, and representative fracture morphology under the investigated conditions. ECT responses were evaluated using a statistically defined threshold (T = μ + 3σ) as a decision criterion for indication screening under assumed noise conditions and calibrated near-surface inspection sensitivity. The tested specimens showed stable measured mechanical responses, the examined fracture surfaces were consistent with predominantly ductile fracture behavior, and no reportable ECT indications were observed above the adopted threshold. The proposed framework provides a reproducible and scalable strategy for reducing uncertainty in material qualification and strengthening integration between destructive and non-destructive evaluation in stainless steel applications. Full article

Background/Objectives: The use of artificial intelligence (AI) in imaging departments is increasing in Europe. This study assesses the clinical value of an AI fracture algorithm by assessing ease of use, clinicians’ trust, and perceived barriers and benefits of this decision support tool in daily practice across two emergency departments (EDs) in Denmark. Methods: An online survey was distributed over four weeks (June–July 2025) to healthcare professionals interpreting radiographs in the ED at Lillebaelt Hospital. The survey included open-ended, closed-ended, and free-text questions addressing AI use. Additionally, an observational study was conducted, including workflow observations and time tracking of patient progression through the ED. Historical injury conference records from February 2023 to 2025 were reviewed to assess changes in patient management before and after AI implementation. Results: A total of 56 responses were obtained (24 male, 32 female). Most respondents reported a positive attitude toward the algorithm. Ease of use was rated satisfactory by 51 out of 56 participants, and 48 were satisfied with AI as a clinical decision support tool. Overall trust was high, with more than two thirds (n = 38) “agreeing” or “strongly agreeing” that the algorithm reliably detects fractures. However, an asymmetry in clinical trust was observed, whereby clinicians expressed greater confidence in their own assessments when the algorithm indicated the presence of a fracture than when it did not. Value stream analyses showed a delay of 6–23 min between radiograph acquisition and availability of the AI report. No differences were observed in the number of patients with treatment changes before, during, or after full implementation of the algorithm. Conclusions: In our limited study population, the AI fracture detection tool was overall well received by clinicians, although some observations indicate that implementation and workflow integration still require improvement. Larger studies are needed to validate the reported barriers and benefits of the AI fracture detection tool. Full article

Plastic design enables efficient structural systems by exploiting controlled inelastic deformation and force redistribution. While mature in steel structures due to stable ductility and well-defined yielding, its extension to reinforced concrete (RC) remains challenging because cracking, stiffness degradation, confinement dependency, and progressive damage govern deformation capacity and collapse mechanisms. This paper presents a state-of-the-art review of optimal plastic design methodologies for RC structures by tracing the evolution from classical plasticity theory to modern damage-informed, reliability-oriented, and sustainability-driven formulations. A systematic and structured literature review of more than 90 peer-reviewed journal articles (1990–2025) was conducted using Scopus, Web of Science, and ScienceDirect. The selected studies are classified by structural system type, plastic analysis approach, constitutive modeling strategy, and strengthening technique, including CFRP and hybrid fiber systems, optimization framework, and uncertainty treatment. The review highlights how nonlinear elasto-plastic and damage–plasticity models improve the prediction of plastic hinge development, redistribution, and failure-mode transitions, and how metaheuristic optimization, topology optimization, surrogate modeling, and machine learning are increasingly used to manage discrete design variables and computational cost. Reliability-based methods (e.g., FORM/SORM and simulation) are shown to be essential for quantifying deformation-capacity uncertainty and ensuring consistent collapse-prevention performance. A comparative assessment of nine plastic design methodologies is also provided, identifying their core assumptions, limitations, and domains of applicability within a structured evaluative framework. Remaining challenges include robust deformation-capacity prediction, reproducible calibration of damage models, and integration of life-cycle sustainability criteria within reliability-constrained plastic optimization. Future research directions are proposed toward multi-objective reliability-based design, durability-informed plastic modeling, and hybrid physics-informed AI-assisted workflows. Full article

Objectives: Early identification of treatment response in brain metastases remains clinically challenging. This study explores tensor-valued diffusion MRI (dMRI), specifically q-space trajectory imaging (QTI), as a novel source of early imaging biomarkers during stereotactic radiotherapy (SRT). Methods: Twenty-six patients with brain metastases were enrolled; thirteen met quality and completeness criteria for QTI analysis (10 responders, three non-responders). MRI was acquired at four time points: before SRT, before final SRT fraction, and at 3 and 6 months post-SRT. QTI-derived metrics included mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (µFA), and isotropic (MKI) and anisotropic (MKA) diffusional variance. Parameter values within the tumour volume were compared pre- and during SRT and correlated with treatment response from standard MRI follow-up. Overall survival was assessed using Kaplan–Meier analysis. Results: Median survival was 12 months. QTI analysis was feasible with visible changes in the tumour tissue parameter maps over time. Statistically significant differences (p < 0.05) were found between responders and non-responders in FA before treatment. MKI in responders was significantly lower (p < 0.05) during SRT than before. Conclusions: This study presents a first exploration of QTI-derived parameters in a cohort of patients with brain metastases. We demonstrate feasibility and a scalable workflow, supporting further investigation in larger cohorts and in patients with larger or more stable lesions. Full article

Background/Objectives: Image-guided percutaneous thermal ablation is an established local treatment for selected patients with liver metastases, provided that accurate tumor targeting and adequate ablation margins can be achieved. However, lesion detection, target delineation, and intraprocedural margin verification remain challenging in post-chemotherapy or previously treated lesions that may become morphologically inconspicuous or radiologically occult. Catheter-assisted C-arm (cone-beam) CT hepatic arteriography (CBCT-HA) improves intraprocedural visualization of tumor vascularity and supports streamlined workflows within the angiography suite, yet it may underestimate tumor extent in lesions with limited or absent angiographic conspicuity. This pictorial essay illustrates the feasibility and added value of integrating preprocedural PET/CT with intraprocedural CBCT-HA for liver tumor ablation. Methods: Representative clinical cases of percutaneous liver tumor ablation guided by PET–CBCT-HA fusion are presented. Preprocedural PET/CT datasets were rigidly registered and fused with intraprocedural CBCT-HA to support tumor detection, target delineation, ablation planning, and real-time intraprocedural margin assessment. The complementary roles of metabolic and angiographic imaging were evaluated qualitatively across different clinical scenarios. Results: PET–CBCT-HA fusion improved detection and delineation of viable tumor components that were occult or insufficiently defined on CBCT-HA alone, particularly in post-chemotherapy or previously treated lesions. Conversely, CBCT-HA identified angiographically evident lesions not apparent on PET/CT. The combined approach enabled confident target definition, biologically informed ablation planning, and immediate post-ablation verification of metabolic and angiographic coverage, supporting margin-oriented intraprocedural decision-making. Conclusions: By integrating complementary metabolic and vascular information into a single-session workflow, PET–CBCT-HA fusion represents a multimodal guidance strategy that enhances lesion visualization and intraprocedural margin assessment. This approach may improve local tumor control in complex post-treatment and oligometastatic liver disease. Full article

Purpose: The Cardiovascular and Interventional Radiological Society of Europe (CIRSE) conducted an international survey on the real-life application of transarterial radioembolization (TARE). This sub-analysis of the complete survey evaluates intercontinental disparities in TARE practices. Materials and Methods: A survey of 32 multiple-choice questions was distributed to CIRSE members between November and December 2022. The questions addressed steps of the TARE workflow, including treatment work-up, planning and dosimetry, intervention, follow-up and innovations. Responses were curated to remove duplicates and incomplete entries and categorised into continental groups. Analysis focused on variations between Europe and North America and impacting factors in the respective regions were identified. Data is presented using descriptive statistics. Results: Responses were obtained from 30 countries and 133 hospitals, including 87 European and 21 North American centres. Hepatocellular carcinoma was the most common indication, constituting 61% of treatments in North America and 51% in Europe. North America predominantly used ⁹⁰Y glass microspheres, whereas Europe used ⁹⁰Y resin. Procedural differences included the adoption of intra-procedural CT imaging, utilized by all North American sites, versus 89% of European sites. Outpatient treatments were favoured in North America (85%), while in Europe, most patients remained hospitalized for one night (51%). Both regions increasingly emphasized dosimetry-guided treatments, with personalized dosimetry planning in 71% and 84% of North American and European sites, respectively. Conclusions: This North America–Europe comparison highlights regional differences in radioembolization practice between the leading continents in procedure volume, based on results of the CIRSE TARE survey. Specific intercontinental differences identified in this survey included hospitalization, product utilization, and procedural techniques. Full article

Background/Objectives: Artificial intelligence (AI) has gained growing interest in the field of orthognathic surgery due to its potential to improve diagnostic accuracy, surgical planning, and treatment outcomes. This scoping review maps literature from 2017 to May 2025 to identify AI applications in orthognathic surgery, assess their clinical relevance, and discuss the associated ethical, legal, and technical limitations. Methods: This scoping review further examines the stages of the orthognathic surgical workflow at which AI applications have been prospectively validated, the artificial intelligence methodologies applied to virtual surgical planning and outcome prediction, and the main methodological, ethical, and legal factors that may constrain broader clinical adoption. Results: A total of 62 studies were included, covering AI use in cephalometric analysis, virtual surgical planning (VSP), outcome prediction, and intraoperative support. While AI demonstrates remarkable potential in orthognathic planning, current approaches are often limited by heterogeneous methodologies and retrospective validation. Conclusions: Future studies should prioritize prospective, multicentre designs integrating AI-assisted decision-making directly into the clinical workflow, with emphasis on model interpretability, patient-specific accuracy, and ethical transparency. These questions extend beyond mapping applications by emphasizing clinical validation, methodological rigor, and ethical accountability—dimensions insufficiently explored in prior reviews. Full article