Search Results (448)

Background/Objectives: Diagnosis of periprosthetic joint infection (PJI) after total knee arthroplasty remains challenging when standard criteria are inconclusive. This study evaluated the diagnostic accuracy of synovial calprotectin and its prespecified exploratory adjunctive value to preoperative 2018 International Consensus Meeting (ICM) classification. Methods: This prospective single-center diagnostic accuracy study included 35 consecutive patients with primary or revision total knee arthroplasty who underwent synovial calprotectin testing followed by surgery for suspected PJI. Calprotectin was measured using a lateral flow assay with a prespecified threshold of ≥50 mg/L. Diagnostic performance was assessed against a multidisciplinary composite postoperative reference standard that did not incorporate calprotectin. Results: Twenty-one patients were classified as infected and 14 as non-infected. Calprotectin yielded 18 true-positive, 11 true-negative, 3 false-positive, and 3 false-negative results, corresponding to 85.7% sensitivity, 78.6% specificity, 85.7% positive predictive value, 78.6% negative predictive value, and an area under the curve of 0.83. Preoperative ICM correctly classified 26 patients, misclassified one, and left eight inconclusive. Among the inconclusive cases, calprotectin correctly classified all six non-infected patients but neither of the two infected patients. The combined strategy classified all 35 patients, correctly classifying 32 and misclassifying three. Among 10 antibiotic-exposed patients—seven infected and three non-infected—no misclassifications occurred; this finding was descriptive. Conclusions: Synovial calprotectin showed good overall diagnostic performance in this surgically managed cohort. When applied to preoperative ICM-inconclusive cases, it increased classification yield but missed both infected cases and should not be used alone to exclude PJI. Larger blinded multicenter studies are required before routine implementation. Full article

Background: The most appropriate management of glucose-lowering therapies in patients exposed to iodinated contrast media (ICM) is still debated. The recent development of antidiabetic drugs that improve cardiovascular and renal outcomes leads to questions regarding their impact on the risk of contrast-associated acute kidney injury (CA-AKI). The present study aimed to assess the effect of different glucose-lowering therapies on the rate of CA-AKI among patients undergoing coronary angiography and/or angioplasty. Methods: Diabetic patients exposed to ICM for coronary procedures were retrospectively identified and divided according to the strategy for the management of diabetes mellitus. The use of a new antidiabetic drug (NAD) was defined for patients treated with SGLT2-I, DDP4-I or GLP-1 receptor agonists on admission. The primary endpoint was the occurrence of CA-AKI within 72 h after contrast medium exposure. Results: We included 462 patients with diabetes mellitus, 51.5% treated with insulin, 44.4% treated with metformin and 50.9% receiving NAD. Among them, 48 (10.4%) experienced CA-AKI. Patients experiencing acute renal injury were more often treated with calcium channel blockers (p = 0.04) and diuretics (p = 0.004), and less often P2Y12 inhibitors (p = 0.04), and presented lower levels of hemoglobin (p = 0.02). Patients receiving NADs displayed a significantly lower occurrence of CA-AKI (33.3% vs. 53.6%, p = 0.009), mainly for those treated with SGLT2-I. On the contrary, patients treated with sulfonylureas and meglitinides displayed a significant increase in the rate of CA-AKI (10.4% vs. 3.9%, p = 0.05). The results were confirmed via multivariable analysis, with NADs and diuretics emerging as the only independent predictors of CA-AKI (NAD: adjusted OR = 0.42 [0.21–0.81], p = 0.01; diuretics: adjusted OR = 2.22 [1.14–4.35], p = 0.02). The independent predictors of CA-AKI were the use of NADs (adjusted OR = 0.45 [0.24–0.86], p = 0.02) and diuretics (adjusted OR = 2.57 [1.33–4.97], p = 0.005). Conclusions: Among patients with diabetes mellitus undergoing coronary angiographic procedures, the use of diuretics, sulfonylureas and meglitinides is associated with an increased occurrence of CA-AKI, whereas the rate of events was significantly lower among users of new antidiabetic drugs and especially SGLT2-I. Full article

Accurate prediction of quality indicators during juice pasteurization is challenged by the intricate coupling between temperature-dependent reaction kinetics and the limited availability of labeled data under laboratory-controlled pasteurization conditions. Here, we present a physics-guided residual learning framework that fuses near-infrared spectroscopy with electronic nose signals for dynamic quality monitoring. A first-order volatilization–saturation kinetic equation is embedded as a physical prior (PIRL), anchoring the prediction to known degradation behavior while reserving the residual learner for data-driven compensation of unmodeled deviations. To further incorporate process control information, a Feature-wise Linear Modulation-inspired conditional modulation (FiLM–ICM) mechanism is deployed, enabling temperature and time to act not as passive covariates but as active modulators that adaptively rescale spectral and olfactory feature responses. Validated on a kiwifruit juice pasteurization dataset under leave-one-trajectory-out cross-validation, the PIRL–FiLM framework consistently outperforms conventional chemometric baselines, achieving R² improvements up to 0.030 and RPD gains exceeding 0.230. This hybrid strategy demonstrates that physical knowledge and data-driven learning are not competing but complementary, offering a robust and interpretable paradigm for process analytical technology under small-sample constraints. This framework offers a practical solution for quality monitoring in scenarios where large-scale labeled datasets are unavailable, such as in laboratory-scale process development and small-batch production. Full article

High-entropy alloys (HEAs) offer attractive combinations of mechanical performance, thermal stability, and compositional flexibility, making them promising candidates for advanced structural applications. Laser-based additive manufacturing, particularly laser powder bed fusion (LPBF) and laser-directed energy deposition (LDED), enables the fabrication of geometrically complex HEA components with non-equilibrium microstructures. However, the distinct thermal histories of LPBF and LDED, with typical cooling rates of approximately 10⁵–10⁷ K s⁻¹ and 10²–10⁴ K s⁻¹, respectively, strongly govern solidification behavior, elemental segregation, residual stress development, defect formation, and mechanical properties. Although previous reviews have discussed additively manufactured HEAs, an integrated framework linking composition design, printability, LPBF/LDED processing, microstructural evolution, post-processing, and industrial qualification remains limited. Therefore, this review establishes a unified composition–process–structure–property framework for laser additively manufactured HEAs. Fundamental HEA concepts, LPBF/LDED process characteristics, solidification behavior, phase formation, defect evolution, and mechanical performance from ambient to elevated temperatures are systematically discussed across representative FCC, refractory, and dual-phase HEA systems. This review emphasizes that printability should be considered during alloy design by correlating composition-dependent solidification characteristics, cracking susceptibility, phase stability, and defect formation with mechanical performance. Post-processing treatments are shown to modify residual stress, microsegregation, precipitation behavior, porosity, and deformation mechanisms, although their benefits must be balanced against thermal softening or brittle phase formation. Finally, CALPHAD, integrated computational materials engineering (ICME), machine learning (ML), and in situ monitoring are identified as promising tools for accelerating alloy and process optimization, while reproducible process windows, defect-control criteria, databases, and qualification protocols remain essential for industrial implementation. Full article

Urban pluvial flood analysis requires spatial inundation information within operationally useful computation times. This study develops a graphics processing unit (GPU)-accelerated Node–Edge Urban Flood Model (GNE-UFM) that couples SWMM-style runoff generation, structured-grid surface flow, a typed sewer graph, and conservative surface–sewer exchange. The model was evaluated in the Sillim drainage district using the August 2022 observed event and three one-hour rainfall scenarios of 40, 90, and 150 mm, with inputs and output thresholds matched to InfoWorks ICM. For the synthetic scenarios, GNE-UFM achieved CSI values of 0.878–0.904 and wet-union RMSE values of 0.050–0.078 m, while full coupled GPU runs closed the effective runoff mass balance with absolute residuals no larger than 0.0284%. Runtime was comparable to ICM for the lowest-intensity case and became more efficient for the 90 and 150 mm cases, with GNE-UFM maintaining nearly constant RTR as rainfall intensity and inundated area increased. Full article

Fourier transform infrared spectroscopy (FTIR) has become an increasingly valuable analytical tool in the dairy industry due to its rapid, non-destructive nature and ability to capture complex chemical information. This study evaluated the feasibility of using FTIR in combination with chemometric modeling to predict the freezing point (FP) of ice cream mix (ICM), an important quality attribute that influences product texture, freezing behavior, and manufacturing efficiency. Traditional methods for FP determination are often labor-intensive and can present challenges when analyzing high-solids dairy systems. In this study, FP values were obtained through a cryoscopic method. FTIR spectra were collected for all samples, and partial least squares (PLS) regression models were developed to relate spectral information to FP values. Model performance metrics indicated that FTIR spectra contained information associated with FP variation; however, overall predictive performance was limited. The relatively weak model accuracy was attributed to the complex nature of FP as a colligative property influenced by multiple compositional factors, including sugars, salts, proteins, and stabilizers, as well as the narrow compositional range and limited sample size of the dataset. Despite these limitations, the results demonstrated the potential of FTIR as a rapid screening tool for FP estimation and provided a foundation for future model development using larger and more compositionally diverse datasets and improved reference methodologies. Full article

Background: Atrial fibrillation (AF) is a common but frequently undiagnosed cause of ischemic stroke, particularly among patients with cryptogenic stroke and embolic stroke of undetermined source (ESUS). Implantable cardiac monitors (ICMs) enable prolonged continuous rhythm monitoring and may improve AF detection following ischemic stroke or transient ischemic attack (TIA). This systematic review and meta-analysis aimed to evaluate the diagnostic yield, clinical impact, and safety of prolonged ICM monitoring in patients with ischemic stroke or TIA. Methods: This systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 guidelines and registered with PROSPERO (CRD42024573913). PubMed, Google Scholar, and the Cochrane Library were systematically searched. Randomized controlled trials and observational studies evaluating the use of ICMs after ischemic stroke or TIA were included. Randomized evidence was synthesized narratively, whereas single-arm random-effects meta-analyses of observational studies were performed to estimate pooled proportions for AF detection, oral anticoagulation initiation, recurrent ischemic stroke or TIA, and device-related adverse events. Results: Twelve completed studies involving 4563 participants met the inclusion criteria, including two randomized controlled trials and ten observational studies. One additional ongoing randomized controlled trial (Find-AF 2) involving a planned enrollment of 5200 participants was identified and is described narratively. Across the observational studies, the pooled AF detection rate during prolonged ICM monitoring was 25.9% (95% CI, 18.9–33.5%), although substantial heterogeneity was observed (I² = 95%). Oral anticoagulation was initiated in 94.2% (95% CI, 79.4–100.0%) of patients diagnosed with AF. Device-related complications were uncommon, with a pooled incidence of 3.7% (95% CI, 2.0–6.0%; I² = 0%), while the pooled rate of recurrent ischemic stroke or TIA during follow-up was 6.2% (95% CI, 3.9–9.2%). Narrative synthesis of the randomized evidence demonstrated that ICMs significantly increased AF detection compared with conventional monitoring but did not demonstrate a significant reduction in recurrent stroke during the available follow-up period. Conclusions: Prolonged implantable cardiac monitoring identifies AF in approximately one-quarter of patients following ischemic stroke or TIA and frequently leads to the initiation of oral anticoagulation, with a favorable safety profile. Although ICMs substantially improve AF detection, current evidence remains insufficient to confirm that increased detection translates into a reduction in recurrent stroke. Large, adequately powered randomized controlled trials are needed to determine the long-term clinical benefits of ICM-guided management and to define the optimal monitoring strategy for patients following ischemic stroke. Full article

Background: Several strategies such as premedication or allergologic work-up have been used to facilitate safe iodinated contrast media (ICM) application. The aim of this study was to assess the negative predictive value of skin tests and complete allergologic work-up with iodinated contrast media in real-life settings. Methods: We retrospectively enrolled 210 patients with a history of immediate hypersensitivity reactions to ICMs. The stepwise protocol of selecting safe ICMs included skin prick tests (SPTs), intracutaneous tests (ICTs), and intravenous drug provocation tests (DPTs). The tests were performed with one or more of the following medications: iohexol, iodixanol, iomeprol, iopromide, and ioversol. At least 6 months after selecting safe ICMs, a telephone follow-up was scheduled. Based on this data, negative predictive values of skin tests and the whole work-up were calculated. Results: Overall, we obtained the following rates of positive tests: SPT—12% (55/441); ICT—10% (40/383); and DPT—5% (10/195). The highest rates of positive work-ups were found in patients tested within 1 year after the reaction and patients with a history of urticaria/angioedema after ICMs. The skin tests and DPTs determined a safe ICM in 184/210 (87%) patients. The negative predictive value of skin tests in reference to DPTs in hospital settings was 95%. Finally, a telephone follow-up demonstrated that 75/184 participants (41%) had been given the recommended ICM, including 68 cases (37%) with no subsequent hypersensitivity reaction and 7 (4%) with a reaction. The negative predictive value of allergologic work-up consisting of SPTs, ICTs and DPTs in real-life settings was 90%. Conclusions: Skin tests and intravenous drug provocation tests with ICMs are characterised by high negative predictive value, and enable the determination of safe ICMs for most patients. Full article

As Artificial Intelligence (AI) evolves from passive tools into proactive actors within socio-technical systems, traditional social network theories face fundamental limitations in explaining AI’s structural power. Drawing on the Network Capabilities framework, this study investigates the mechanism of AI power generation within homogeneous communities from a structural hole perspective. This study analyzes a COVID-19 vaccine interaction network (N = 9314) on X via social network analysis, Propensity Score Matching (PSM), counterfactual simulations, and weighted Independent Cascade Model (ICM) dynamics. The results reveal that bot-like agents do not rely on traditional brokerage to acquire power; instead, they execute a Tight Integration strategy by filling micro-structural holes. After isolating the confounding effects of connection scale via rigorous Propensity Score Matching, it creates an anomalous high-density, high-constraint configuration, with these algorithmic agents exhibiting significantly higher network constraint (0.514) than comparable human users (0.453). Counterfactual removal experiments demonstrate a profound structural dependence of the social system on AI: their removal triggers a systemic cascade collapse, decreasing the largest connected component (LCC) size by a factor of 82.9 and topologically isolating 79.7% of human users. Furthermore, transitioning from static structural analysis to dynamic simulations, ICM simulations confirm AI’s topological redundancy translates into substantial information diffusion dominance (Cohen’s d = 1.081). Revealing AI’s power generation mechanism provides essential governance insights and strategic approaches for mitigating AI-driven information cocoons and group polarization. Full article

Aerospace die forgings are safety-critical structural products whose service performance is governed by coupled microstructural evolution across multiple length scales rather than by any single descriptor. This review critically synthesizes recent progress in multiscale characterization and process–structure–property analysis of aerospace die forgings, with emphasis on forged titanium alloys, wrought nickel-based superalloys, and high-strength aluminum alloys. A practical framework is first established by linking macroscale metal-flow integrity and defect control with mesoscale gradients, microscale grain-boundary and texture evolution, and nanoscale precipitation, segregation, and interface states. The principal characterization routes are then discussed, including X-ray diffraction, EBSD/3D-EBSD, TEM/STEM, atom probe tomography, tomography-based defect evaluation, and correlative workflows. The alloy-specific sections are organized around mechanisms and property consequences rather than isolated micrographs. Finally, the review discusses how multiscale descriptors can support crystal-plasticity, phase-field, cellular-automata, and ICME-oriented modeling, and identifies future priorities in three-dimensional characterization, quantitative descriptor extraction, uncertainty-aware modeling, environmental degradation assessment, and closed-loop process optimization. Overall, the performance of aerospace die forgings is shown to depend on coordinated control of phase stability, grain-boundary network evolution, precipitation state, defect population, and location-dependent heterogeneity across the full manufacturing route. Full article

To address jagged boundaries in conventional fixed-mesh topology optimization and the discontinuous transfer of topology information after remeshing, this study proposes an improved Movable Morphable Smooth-Boundary (MMSB) method for displacement-constrained continuum topology optimization. A topology optimization model is established using the Independent Continuous Mapping (ICM) method, with structural weight minimization as the objective and displacement as the constraint. In the proposed framework, a triangular mesh is adopted as the current analysis mesh, threshold boundary points are identified using a holographic scanning strategy, and a fixed background mesh is introduced as an intermediate carrier for topology-variable transfer before and after remeshing. Three numerical examples are used to validate the proposed method and to compare it with the original MMSB method. The results show that the proposed method produces clearer boundary representations and more distinct load-transfer paths. In Examples 1–3, the number of result analyses is reduced from 60 to 24, from 144 to 36, and from 112 to 24, respectively. The number of boundary movements is also reduced from 5 to 4, from 8 to 6, and from 7 to 4, respectively. Meanwhile, the final structural weights are 22.0 kg, 101.4 kg, and 1.64 kg, which are close to or slightly lower than those obtained by the original MMSB method. These results indicate that the proposed method improves topology-information continuity and boundary representation while maintaining structural performance. Full article

Background and Objectives: Anterior cruciate ligament reconstruction (ACLR) is a very common procedure in young, active individuals. One of the rarest, but potentially life-threatening complications is symptomatic venous thromboembolism (VTE). Despite that, international guidelines offer conflicting advice on routine thromboprophylaxis. This review aims to summarize key international recommendations to support clinical decision-making. Materials and Methods: The role of thromboprophylaxis after ACL reconstruction remains controversial. Rather than performing a systematic review, relevant documents were identified from official publications, consensus statements, and registry-based recommendations issued by recognized orthopaedic and thrombosis-related organizations. Documents were included if they represented formal guidelines, consensus statements, or national/registry-based recommendations with direct clinical relevance to ACLR and thromboprophylaxis, and if they reflected contemporary practice across different healthcare systems. Recommendations from CHEST, NICE, BOA/BASK/BOSTAA, AAOS, ICM-VTE, ICS, SFAR, HAOST, and the Swedish Knee Ligament Registry were included. These were analyzed with respect to indications for pharmacological and mechanical prophylaxis, as well as risk stratification strategies. Results: This narrative review identified nine major international guidelines addressing thromboprophylaxis in ACLR. Most guidelines, including CHEST and NICE, advise against routine anticoagulation for low-risk patients, reserving it for those with specific risk factors. BOA recommends prophylaxis only when multiple comorbidities are present. The Swedish registry indicates that anticoagulation is primarily used in patients with prior VTE or oral contraceptive use. While ICM-VTE supports the use of aspirin or LMWH in high-risk cases, SFAR stands out by recommending routine LMWH for all patients. HAOST emphasizes early mobilization and selective prophylaxis, including aspirin for low-risk groups. Conclusions: Current evidence does not support universal thromboprophylaxis following ACLR. A risk-stratified approach is recommended, with mechanical measures or aspirin for low-to-moderate-risk patients and LMWH or DOACs for high-risk individuals. Future research may focus on genetic and patient-specific factors to better explain existing heterogeneity. Full article

While several dosing protocols for iodinated contrast media (ICM) exist, a consensus strategy for optimising the physical imaging signal in abdominal CT is lacking. This systematic review and meta-analysis evaluated the performance of individualised dosing protocols, specifically focusing on technical signal optimisation, clinical safety, potential material savings, and environmental sustainability. Electronic databases (Cochrane, Embase, Medline) were searched up to January 2026. Systematic synthesis of 23 studies (11,680 participants) compared protocols based on lean body weight (LBW), total body weight (TBW), fixed volume (FV) and software-assisted dosing. Meta-analyses assessed volume optimisation and hepatic enhancement, with evidence certainty evaluated via the GRADE framework. TBW-based dosing significantly reduced contrast volume by −8.74 mL compared to FV protocols (p = 0.02), while the −4.04 mL reduction in LBW versus TBW groups represented a non-significant trend (p = 0.11); however, a sensitivity analysis revealed a significant effect (−5.41 mL, 95% [CI: −10.43, −0.39]; p = 0.03). Pooled hepatic enhancement showed no statistically significant differences for LBW vs. TBW (−1.36 HU, p = 0.43) or FV vs. TBW (−2.74 HU, p = 0.13). Individualised ICM dosing, particularly LBW-based, may potentially offer a foundational strategy for greener and material savings in clinical radiology by minimising population-level iodine load. Despite modest individual volume reductions, these protocols may potentially facilitate standardised imaging enhancement, though higher-quality randomised trials are required to confirm safety and economic benefits. Full article

Reliable indoor localization is required for automated guided vehicles (AGVs), robot validation, and industrial digital-twin applications, but ultrasonic positioning can degrade sharply when acoustic visibility changes. This paper evaluates Marvelmind Super-Beacon localization in controlled laboratory experiments involving both AGV tracking and UR10 robot-arm positioning. The non-inverse architecture (NIA) and inverse architecture (IA) configurations are included as parallel validation scenarios to assess the robustness of the proposed mitigation framework across different Marvelmind deployment modes. The baseline analysis identifies the dominant acoustic failure modes, including multipath-induced scatter, crossover-zone handover jumps, update-rate degradation, complete non-line-of-sight (NLoS) outages, and height-dependent 3D jitter. To mitigate these effects, an embedded ultrasonic–inertial pipeline is implemented on an ESP32-S3-WROOM-1 module. The system combines UART packet validation, interrupt-driven ICM-20948 inertial acquisition at 500 Hz, sliding-window kinematic outlier rejection, and a 15-state error-state Kalman filter (ESKF). The embedded estimator logic is designed to maintain motion continuity during intermittent or corrupted acoustic positioning while reintroducing validated ultrasonic absolute corrections. Using recorded AGV and UR10 datasets, mitigation performance was quantitatively assessed through a firmware-consistent replay of the recorded measurements, using the same gating, inertial propagation, and measurement-update logic as the real-time ESP32-S3 implementation. Across ten trials per configuration, the replay-based trial-mean RMSE in the 2D AGV scenarios decreased from 101.2–104.1 mm for raw ultrasonic data to 47.2–48.7 mm after fusion, while peak failure-interval errors were reduced by 64.2–65.7%. In the 3D UR10 scenarios, replay-based trial-mean RMSE decreased from 157.6–158.4 mm to 80.2–80.5 mm, and peak height-sensitive 3D errors were reduced by 58.8–60.0%. The results demonstrate the feasibility of embedded ultrasonic–inertial robustness enhancement for localization in controlled laboratory AGV and robot-arm scenarios. While the proposed approach shows promising performance under the investigated conditions, further validation is required before extending the conclusions to larger-scale and dynamically changing industrial environments. Full closed-loop online robot localization and control based directly on the fused localization output remain subjects for future investigation. Full article

Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to bridge microstructural features with engineering-scale mechanical behaviour. However, their practical application is hindered by two major challenges: high computational costs of physics-based simulations, and the labour-intensive, trial-and-error nature of parameter calibration. These challenges are amplified in additively manufactured (AM) materials, where location-dependent properties require calibration to be repeated at multiple points to produce a detailed property map. Additionally, a limited understanding of how individual parameters of the CP models influence stress–strain predictions across the strain spectrum compounds these issues, making it challenging to utilise CP models for efficient materials design. To address these limitations, we developed an integrated framework that combines deep neural network (DNN) surrogates, optimisation algorithms (OAs), and explainable AI (XAI) techniques. We also utilised experimental tensile data from AM AlSi10Mg alloy as ground truth since AM materials are expected to benefit the most from our investigation. We demonstrate that, by using OAs such as a Natural Evolutionary Strategy or a Genetic Algorithm, the calibration process can be made more accurate and significantly accelerated. We also investigated the utility of employing deep neural network (DNN) surrogates of CP simulations in the calibration process. The fast-solving DNN surrogates achieved substantial time savings in the absence of OAs, i.e., during exhaustive parameter searches mandated by trial-and-error strategies. However, their effectiveness in parameter discovery was context-dependent when used in conjunction with OAs, since OAs can sometimes converge with fewer simulations than required for DNN training. Furthermore, we applied Shapley Additive exPlanations (SHAP), an XAI method, which revealed intricate interactions among some CP parameters, offering insight into why conventional trial-and-error calibration approaches often prove challenging. Our study contributes to strengthening the practical relevance of CP models for modelling-informed materials engineering and optimisation applications. Finally, our integrated framework offers broad applicability beyond materials modelling, enabling accelerated discovery of tuneable parameters in phenomenological models and providing deeper insight into their contributions to predictions. Full article