Search Results (806)

Background: Direct oral anticoagulants (DOACs) are now the preferred anticoagulant over vitamin K antagonists for patients with atrial fibrillation (AF) and venous thromboembolism (VTE). Variability in drug exposure raises concerns about bleeding and thrombotic events, highlighting the potential value of therapeutic drug monitoring (TDM). Methods: This systematic review and meta-analysis conducted a systematic search of PubMed, Embase, Web of Science, Scopus, Cochrane Library, and ClinicalTrials.gov (from inception to May 2025) and identified studies reporting DOAC levels and clinical outcomes. Two reviewers independently performed screening, data extraction, and risk-of-bias assessment (RoB 2.0, Newcastle–Ottawa Scale). Random-effects meta-analytical models generated pooled estimates, with meta-regression exploring potential sources of variability (DOAC type, drug levels) and exposure–response relationships. Results: Nineteen studies comprising 5770 patients were included in the review. The pooled event rates were 8% for major bleeding (95% CI: 0.05–0.11), 7% for thrombotic events (95% CI: 0.05–0.09), and 3% for mortality (95% CI: 0.03–0.04). Heterogeneity was substantial for bleeding and thrombotic events (I² = 95.6% and 87.3%, respectively) but negligible for mortality (I² = 0%). Meta-regression analyses showed no significant association between mean DOAC concentration and either major bleeding (β = −0.00021, p = 0.35, Adj R² ≈ 0%) or thrombotic events (β = 0.00005, p = 0.78, Adj R² ≈ 0%), indicating that variations in measured plasma levels did not meaningfully explain event rate differences across studies. Conclusions: In this systematic review and meta-analysis, measured DOAC concentrations show limited and inconsistent association with clinical outcomes. While the present synthesis does not demonstrate a statistically robust linear correlation between DOAC plasma concentrations and adverse outcomes, it highlights the multifactorial determinants of bleeding and thrombosis risk underscores the potential value of selective TDM in individualized care. Further prospective, standardized studies are needed to define clinically actionable thresholds and to validate TDM-guided strategies that optimize the delicate balance between safety and efficacy in DOAC therapy. Full article

The objective of this study was to compare subjective manifest refraction with wavefront-based automated refraction using iTrace (ray tracing) and LadarWave (Hartmann–Shack) in eyes implanted with two enhanced monofocal intraocular lenses (IOLs) and a standard aspheric monofocal IOL, emphasizing agreement and refractive variability across optical designs. This retrospective cohort included 84 eyes from 42 patients implanted with Tecnis Eyhance (DIB00), RayOne EMV (RAO200E), or Tecnis ZCB00 IOLs. Postoperative evaluation (1–3 months) included uncorrected and corrected distance visual acuity and subjective manifest refraction, followed by automated refraction with iTrace and LadarWave. Outcomes were sphere, cylinder, and spherical equivalent (SE). Agreement was assessed using mean signed difference, mean absolute error, root mean square error, Bland–Altman limits of agreement, proportions within clinically relevant thresholds, and vector astigmatism (J0, J45). Linear mixed-effect modeling evaluated SE differences across methods and IOL types while accounting for within-subject correlation. Subjective SE differed among IOLs (p = 0.027), with RAO200E more myopic than ZCB00 (−0.20 ± 0.32 D vs. −0.08 ± 0.44 D, p = 0.035). Automated refraction showed greater variability and poorer agreement in enhanced monofocal IOLs, particularly for cylinder and SE, with wider limits of agreement and fewer eyes within ±0.50 D compared with ZCB00. In mixed-effect contrasts (three-method repeated-measures model), iTrace and LadarWave showed a consistent myopic bias versus manifest refraction in DIB00 and RAO200E, whereas in ZCB00 the iTrace–manifest difference was not significant and LadarWave retained a significant myopic bias. Enhanced monofocal IOLs exhibit reduced agreement between wavefront-based automated and subjective manifest refraction compared with a standard aspheric monofocal IOL. Manifest refraction remains essential for postoperative assessment, and automated measurements should be interpreted as complementary, particularly in IOL designs that modify aberrations. Full article

Prevention of complications related to diabetic foot (DF) can now be performed using smartphone-connected thermal cameras. However, the absolute error associated with these devices remains particularly high, compromising measurement reliability, especially under variable environmental conditions. To address this, we introduce a physiologically motivated two-region segmentation task (forehead + plantar foot) to enable stable temperature correction. First, we developed a fully automated joint method for this task, building upon a new multimodal thermal–RGB dataset constructed with detailed annotation procedures. Five deep learning methods (U-Net, U-Net++, SegNet, DE-ResUnet, and DE-ResUnet++) were evaluated and compared to traditional baselines (Adaptive Thresholding and Region Growing), demonstrating the clear advantage of data-driven approaches. The best performance was achieved by the DE-ResUnet++ architecture (Dice score: 98.46%). Second, we validated the correction approach through a clinical study. Results showed that the variance of corrected temperatures was reduced by half compared to absolute values (p < 0.01), highlighting the effectiveness of the correction approach. Furthermore, corrected temperatures successfully distinguished DF patients from healthy controls (p < 0.01), unlike absolute temperatures. These findings suggest that our approach could enhance the performance of smartphone-connected thermal devices and contribute to the early prevention of DF complications. Full article

Background/Objectives: To develop and validate a predictive model that combines morphological features and dual-energy CT (DECT) parameters to non-invasively distinguish metastatic from benign axillary lymph nodes in patients with breast cancer (BC). Methods: In this retrospective study, 117 patients (median age, 65 years; 111 women and 6 men) who underwent DECT followed by axillary lymphadenectomy between April 2015 and July 2023, were analyzed. A total of 375 lymph nodes (180 metastatic, 195 benign) were evaluated. Two radiologists recorded morphological criteria (adipose hilum status, cortical appearance, extranodal extension, and short-axis diameter) and placed regions of interest to measure dual-energy parameters: attenuation at 40 and 70 keV, iodine concentration, water concentration and spectral slope. Normalized iodine concentration was calculated using the aorta as reference. Univariate analysis identified variables associated with metastasis. Multivariate logistic regression with cross-validation was used to construct two models: one based solely on morphological features and one integrating water concentration. Results: On univariate testing, all DECT parameters and morphological criteria differed significantly between metastatic and benign nodes (p < 0.01). In multivariate analysis, water concentration emerged as the only independent DECT predictor (odds ratio = 0.97; p = 0.002) alongside cortical abnormality, absence of adipose hilum, extranodal extension and short-axis diameter. The morphologic model achieved an area under the receiver operating characteristic curve (AUC) of 0.871. Increasing water concentration increased the AUC to 0.883 (ΔAUC = 0.012; p = 0.63, not significant), with internal cross-validation confirming stable performance. Conclusions: A model combining standard morphologic criteria with water concentration quantification on DECT accurately differentiates metastatic from benign axillary nodes in BC patients. Although iodine-based metrics remain valuable indicators of perfusion, water concentration offers additional tissue composition information. Future multicenter prospective studies with standardized imaging protocols are warranted to refine parameter thresholds and validate this approach for routine clinical use. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Digital rural construction (DRC), as a crucial intersection of the rural revitalization strategy and the construction of Digital China, is a key path to addressing the imbalance and inadequacy in the urban–rural income gap (URIG). Based on provincial panel data from 2011 to 2023, this paper systematically examines the relationship and mechanism of action between the two using an econometric model. This study finds that DRC significantly reduces the URIG overall, and this effect is achieved through increasing urbanization levels, accelerating employment, and promoting social consumption. Spatial effect tests indicate that DRC has a spatial spillover effect; construction in one province reduces the URIG in neighboring provinces. Further research shows that, against the backdrop of human capital level acting as a threshold variable, the effect of DRC on the URIG exhibits an inverted “U”-shaped characteristic, first increasing and then decreasing. Therefore, this paper proposes countermeasures and suggestions, including constructing a digital-enabled urban–rural integration mechanism, promoting cross-regional coordinated development of DRC, and implementing a tiered and categorized digital literacy improvement project. Full article

This study explores the potential for afforestation in Portugal that could balance wood and non-wood forest production under future climate change scenarios. The Climate Envelope Models (CEM) approach was employed with three main objectives: (1) to model the current distribution of key Portuguese forest species—eucalypts, maritime pine, umbrella pine, chestnut, and cork oak—based on their suitability for wood and non-wood production; (2) to project their potential distribution for the years 2070 and 2090 under two Shared Socioeconomic Pathway (SSP) scenarios: SSP2–4.5 (moderate) and SSP5–8.5 (high emissions); and (3) to generate integrated species distribution maps identifying both current and future high-suitability zones to support afforestation planning, reflecting climatic compatibility under fixed thresholds. Species’ current CMEs were produced using an additive Boolean model with a set of environmental variables (e.g., temperature-related and precipitation-related, elevation, and soil) specific to each species. Species’ current CEMs were validated using forest inventory data and the official Land Use and Land Cover (LULC) map of Portugal, and a good agreement was obtained (>99%). By the end of the 21st century, marked reductions in species suitability are projected, especially for chestnut (36%–44%) and maritime pine (25%–35%). Incorporating future suitability projections and preventive silvicultural practices into afforestation planning is therefore essential to ensure climate-resilient and ecologically friendly forest management. Full article

Background/Objectives: Endolymphatic sac surgery (ELSS) is a non-destructive surgical option for medically refractory Ménière’s disease (MD), yet factors influencing surgical outcomes remain poorly understood. This exploratory study aimed to describe clinical outcomes following ELSS and identify potential associations between preoperative characteristics and surgical success. Methods: This retrospective, bicentric cohort study included 45 patients with definite MD who underwent ELSS (predominantly endolymphatic duct blockage) between 2019 and 2024. Vertigo control was assessed using AAO-HNS criteria. Hearing outcomes were evaluated through pure-tone and speech audiometry. Univariate analyses explored associations between demographic, clinical, imaging, and surgical variables and treatment outcomes. Results: Surgical success (Class A/B vertigo control) was achieved in 66.7% of patients (95% CI: 51.0–80.0%). In a post hoc exploratory analysis, longer disease duration (>5 years) showed an association with better outcomes (87.5% vs. 55.2%, p = 0.029), though this threshold was not prespecified and requires validation. Hearing was preserved in 77.5% of patients at 45-day follow-up but declined progressively to 50% at 2 years. Seven patients developed postoperative Tumarkin attacks, with five requiring non-conservative interventions. ELSS demonstrated low morbidity, with one labyrinthitis as the only significant complication. Conclusions: ELSS was associated with vertigo control in two-thirds of patients with refractory MD, with a favorable safety profile. Longer disease duration before surgery may be associated with improved outcomes, though this exploratory finding requires confirmation in prospective studies. The progressive hearing decline may reflect both natural disease progression and potential surgical effects. Further research with larger cohorts is needed to establish robust predictive criteria for patient selection. Full article

Artificial neural networks (ANNs) are widely used in engineering prediction, but excessive input dimensionality can reduce both accuracy and efficiency. This study proposes a two-stage feature-reduction framework, Feature Importance Ranking and Redundancy Elimination (FIRRE), to optimize ANN inputs by removing weakly informative and redundant variables. In Stage 1, four complementary ranking methods, namely Pearson correlation, recursive feature elimination, random forest importance, and F-test scoring, are combined into an ensemble importance score. In Stage 2, highly collinear features (ρ > 0.95) are pruned while retaining the more informative variable in each pair. FIRRE is evaluated on 32 civil engineering datasets spanning materials, structural, and environmental applications, and benchmarked against Principal Component Analysis, variance-threshold filtering, random feature selection, and K-means clustering. Across the benchmark suite, FIRRE consistently achieves competitive or improved predictive performance while reducing input dimensionality by 40% on average and decreasing computation time by 10–60%. A dynamic modulus case study further demonstrates its practical value, improving R² from 0.926 to 0.966 while reducing inputs from 25 to 7. Overall, FIRRE provides a practical, robust framework for simplifying ANN inputs and improving efficiency in civil engineering prediction tasks. Full article

This paper reviews the fracture mechanics parameters associated with the variability in the crack growth curves associated with forty-two different tests that range from additively manufactured (AM) steels to cold spray additively manufactured (CSAM) 316L steel. As a result of this review, it is found that, to a first approximation, the effects of different building processes and R-ratios on the relationship between ΔK and the crack growth rate (da/dN) can be captured by allowing for changes in the fatigue threshold and the apparent cyclic toughness in the Schwalbe crack driving force (Δκ). Whilst this observation, when taken in conjunction with similar findings for AM Ti-6Al-4V, Inconel 718, Inconel 625, and Boeing Space Intelligence and Weapon Systems (BSI&WS) laser powder bed (LPBF)-built Scalmalloy^®, as well as for a range of CSAM pure metals, go a long way in making a point; it is NOT a mathematical proof. It is merely empirical evidence. As a result, this review highlights that for AM and CSAM materials, it is advisable to plot the crack growth rate (da/dN) against both ΔK and Δκ. The observation that, for the AM and CSAM steels examined in this study, the da/dN versus Δκ curves are similar, when coupled with similar observation for a range of other AM materials, supports a prior study that suggested using fracture toughness measurements in conjunction with the flight load spectrum and the operational life requirement to guide the choice of the building process for AM Ti-6Al-4V parts. The observations outlined in this study, when taken together with related findings given in the open literature for AM Ti-6Al-4V, AM Inconel 718, AM Inconel 625, and BSI&WS LPFB-built Scalmalloy^®, as well as for a range of CSAM-built pure metals, have implications for the implementation and certification of limited-life AM parts. Full article

Background/Objectives: Accurate identification of dental malocclusions from routine clinical photographs can be time-consuming and subject to interobserver variability. A YOLOv11-based deep learning approach is presented and evaluated for automatic malocclusion detection on routine intraoral photographs, testing the hypothesis that training on a structured annotation protocol enables reliable detection of multiple clinically relevant malocclusions. Methods: An anonymized dataset of 5854 intraoral photographs (frontal occlusion; right/left buccal; maxillary/mandibular occlusal) was labeled according to standardized instructions derived from the Index of Orthodontic Treatment Need (IOTN) A total of 17 clinically relevant classes were annotated with bounding boxes. Due to an insufficient number of examples, two malocclusions (transposition and non-occlusion) were excluded from our quantitative analysis. A YOLOv11 model was trained with augmented data and evaluated on a held-out test set using mean average precision at IoU 0.5 (mAP50), macro precision (macro-P), and macro recall (macro-R). Results: Across 15 analyzed classes, the model achieved 87.8% mAP50, 76.9% macro-P, and 86.1% macro-R. The highest per-class AP₅₀ was observed for Deep bite (98.8%), Diastema (97.9%), Angle Class II canine (97.5%), Anterior open bite (92.8%), Midline shift (91.8%), Angle Class II molar (91.1%), Spacing (91%), and Crowding (90.1%). Moderate performance included Anterior crossbite (88.3%), Angle Class III molar (87.4%), Head bite (82.7%), and Posterior open bite (80.2%). Lower values were seen for Angle Class III canine (76%), Posterior crossbite (75.6%), and Big overjet (75.3%). Precision–recall trends indicate earlier precision drop-off for posterior/transverse classes and comparatively more missed detections in Posterior crossbite, whereas Big overjet exhibited more false positives at the chosen threshold. Conclusion: A YOLOv11-based deep learning system can accurately detect several clinically salient malocclusions on routine intraoral photographs, supporting efficient screening and standardized documentation. Performance gaps align with limited examples and visualization constraints in posterior regions. Larger, multi-center datasets, protocol standardization, quantitative metrics, and multimodal inputs may further improve robustness. Full article

Chromosome dynamics, recombination, and nucleolar organization intersect during meiotic prophase I, yet how the recombination context influences nucleolar architecture remains unclear. We analyzed the nucleolar pool of Cdc14 in Saccharomyces cerevisiae under matched prophase I gating and a uniform, frame-based operational definition of transient two-focus episodes. In a prophase-arrest reference, Cdc14–mCherry formed a predominant single nucleolar focus with occasional, reversible two-focus episodes that Nop56–GFP placed within the nucleolar compartment (nucleolar splitting). Splitting rose sharply when interhomolog recombination was compromised and remained elevated when Spo11 catalytic activity was abolished, indicating that increased DSB formation is not required and pointing instead to the homolog engagement state as a key variable. Population checkpoint readouts did not map onto the phenotype: Hop1 phosphorylation differed strongly across genotypes, yet splitting remained high in recombination-defective and DSB-free contexts and low in the reference. Timing analyses showed that events concentrated early and declined in the reference, whereas recombination-defective and DSB-free backgrounds retained activity into later windows across thresholds. We propose that nucleolar splitting reflects a rheological response of the nucleolus to chromosome-scale forces that vary with homolog engagement, consistent with contributions from DSB-independent chromosome dynamics such as telomere clustering, telomere-led rapid prophase movements, and centromere coupling/pairing. Together, these data support the nucleolus as a mesoscale, mechanically sensitive readout of meiotic chromosome dynamics. Full article

Breast cancer remains a leading cause of mortality among women worldwide, motivating the development of transparent and reproducible risk models for clinical decision making. Using the open-access SEER Breast Cancer dataset (November 2017 release), we analyzed 4005 women diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma (ICD-O-3 8522/3). Thirty-one clinical and demographic variables were preprocessed with one-hot encoding and z-score standardization, and the lymph node ratio was derived to characterize metastatic burden. Two supervised models, L1-regularized logistic regression and a feedforward artificial neural network, were compared under identical preprocessing, fixed 60/20/20 data splits, and stratified five-fold cross-validation. To define clinically meaningful endpoints and handle censoring, we reformulated mortality prediction as fixed-horizon classification at 3 and 5 years, and evaluated discrimination, calibration, and operating thresholds. Logistic regression demonstrated consistently strong performance, achieving test ROC-AUC values of 0.78 at 3 years and 0.75 at 5 years, with substantially superior calibration (Brier score less than or equal to 0.12, ECE less than or equal to 0.03). A structured hyperparameter search with repeated-seed evaluation identified optimal neural network architectures for each horizon, yielding test ROC-AUC values of 0.74 at 3 years and 0.73 at 5 years, but with markedly poorer calibration (ECE 0.19 to 0.23). Bootstrap analysis showed no significant AUC difference between models at 3 years, but logistic regression exhibited greater stability across folds and lower sensitivity to feature pruning. Overall, L1-regularized logistic regression provides competitive discrimination (ROC-AUC 0.75 to 0.78), markedly superior probability calibration (ECE below 0.03 versus 0.19 to 0.23 for the neural network), and approximately 40% lower cross-validation variance, supporting its use for scalable screening, risk stratification, and triage workflows on structured registry data. Full article

Background/Objectives: Oral function impairment negatively impacts nutrition, health, and quality of life in older adults. While retaining ≥20 natural teeth is often recommended for maintaining oral function, its validity is uncertain, particularly for those who adapt to tooth loss with dentures. This study aimed to determine the minimum number of remaining functional teeth necessary to prevent oral hypofunction in older adults, focusing on two diagnostic criteria: decreased masticatory function and reduced occlusal force. Methods: A total of 154 participants (≥60 years) were included. Oral examination assessed the number of remaining functional teeth. To assess masticatory function, masticatory performance was objectively measured using a visual scoring method of gummy jelly, and occlusal force was quantified with pressure-sensitive film. Pearson’s correlation analyzed relationships among variables, while receiver operating characteristic (ROC) analysis identified optimal tooth number cut-offs for detecting decreased masticatory function (score ≤ 2) and reduced occlusal force (<500 N). Results: Significant positive correlations were found between the number of remaining functional teeth and both masticatory performance (r = 0.591, p < 0.001) and occlusal force (r = 0.453, p < 0.001). ROC indicated that 17 teeth was the optimal threshold for identifying both decreased masticatory performance and reduced occlusal force, with sensitivities of 0.79 and 0.72 and specificities of 0.93 and 0.88, respectively. Conclusions: Retention of 17 or more remaining functional teeth may be sufficient to maintain adequate masticatory performance and occlusal force. These findings serves as a preliminary guide for treatment planning and targeted interventions focused on preserving tooth retention and improving oral function in aging populations. Full article

Structural Health Monitoring (SHM) of large-scale civil infrastructure is essential to ensure safety, minimise maintenance costs, and support informed decision-making. Unsupervised anomaly detection has emerged as a powerful tool for identifying deviations in structural behaviour without requiring labelled damage data. The study initially reproduces and implements a state-of-the-art methodology that combines local density estimation through the Cumulative Distance Participation Factor (CDPF) with Semi-parametric Extreme Value Theory (SEVT) for thresholding, which serves as an essential baseline reference for establishing normal structural behaviour and for benchmarking the performance of the proposed anomaly detection framework. Using modal frequencies extracted via Stochastic Subspace Identification from the Z24 bridge dataset, the baseline method effectively identifies structural anomalies caused by progressive damage scenarios. However, its performance is constrained when dealing with subtle or non-linear deviations. To address this limitation, we introduce an innovative ensemble anomaly detection framework that integrates two complementary unsupervised methods: Principal Component Analysis (PCA) and Autoencoder (AE) are dimensionality reduction methods used for anomaly detection. PCA captures linear patterns using variance, while AE learns non-linear representations through data reconstruction. By leveraging the strengths of these techniques, the ensemble achieves improved sensitivity, reliability, and interpretability in anomaly detection. A comprehensive comparison with the baseline approach demonstrates that the proposed ensemble not only captures anomalies more reliably but also provides improved stability to environmental and operational variability. These findings highlight the potential of ensemble-based unsupervised methods for advancing SHM practices. Full article