Search Results (449)

Background/Objectives: Nivolumab monotherapy is a standard of care in previously treated advanced renal cell carcinoma (aRCC) and was approved based on the results of the randomized clinical trial Checkmate-025. The non-interventional study (NIS) NORA collected data on the effectiveness and safety of nivolumab monotherapy in real-world clinical routine. Its final, long-term follow-up data are presented here. Methods: NORA was a prospective, multicentre NIS recruiting at 54 German sites, evaluating the effectiveness and safety of nivolumab monotherapy in pre-treated patients with aRCC. Endpoints included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), duration of response (DOR), safety, and patient-reported outcomes (PROs). Results: A total of 232 patients were eligible. Of the patients, 15% had favourable, 58% had intermediate, and 15% had poor risk according to the International Metastatic RCC Database Consortium. Of the patients, 77% received nivolumab as second-line, 15% as third-line, and 8% as ≥fourth-line therapy. With a median long-term follow-up of 76 months (minimum 62 months), median OS was 22.2 months (95% confidence interval [CI] 16.5–25.9) and median PFS was 4.1 months (95% CI 3.2–5.4). The ORR was 21% with a median DOR of 27.9 months (95% CI 15.9—not evaluable). Of the patients, 47% and 16% had treatment-related adverse events of all grades and of grades 3–4, respectively. One patient died from autoimmune hepatitis related to treatment. PROs did not reveal any new signals. Conclusions: The long-term follow-up of NORA confirms that nivolumab monotherapy is an effective and safe therapy in patients with aRCC after prior therapy. With comparable follow-up times, our real-world data were not substantially different from the final long-term results of the pivotal Checkmate-025 study. Full article

Background: Dedicated artificial intelligence (AI) systems for fracture detection already exist, yet general-purpose multimodal models are increasingly accessible to clinicians despite not being developed or formally validated as medical devices. Their behavior in focused orthopedic imaging tasks remains insufficiently characterized. Purpose: To characterize how two accessible general-purpose multimodal models interpret AP pelvis radiographs with hip fractures, focusing on context dependence, overconfidence, and complementary error patterns within a surgically confirmed positive-only cohort. This was a behavioral characterization study of a fracture-positive cohort, not a diagnostic accuracy evaluation. Methods: In April 2026, we retrospectively studied 214 surgically confirmed hip fractures on AP pelvis radiographs using two general-purpose multimodal models under six prompting conditions. In runs A–D, the models were explicitly told that a hip fracture was present and were asked to classify it; in runs E–F, they were not told whether a hip fracture was present. Each image was rerun de novo in a separate chat session through vendor APIs using a fixed base prompt and no image preprocessing. We recorded hip-fracture detection, correct laterality, coarse fracture pattern, intracapsular displacement, AO/OTA grading, subtrochanteric identification, and self-reported confidence. Because the cohort contained hip fractures only, we report fracture-detection rates and classification performance within a positive-only cohort rather than full diagnostic-accuracy metrics. Results: Using the more conservative endpoint of hip-fracture detection with correct laterality, GPT-5.4 was correct in 79.0% and 86.4% of cases in runs E and F, whereas Gemini was correct in 80.4% and 93.5%, respectively. When outputs from both models were combined, this endpoint reached 89.7% in run E and 96.7% in run F, indicating complementary rather than redundant error patterns. Incorrect laterality cues markedly degraded performance, from 90.7% to 66.4% in GPT-5.4 and from 97.7% to 57.0% in Gemini. Performance remained limited for treatment-relevant subtyping, particularly AO/OTA grading and subtrochanteric identification. Both models frequently remained highly confident when wrong, and self-reported confidence did not reliably distinguish correct from incorrect outputs. Conclusions: Accessible general-purpose multimodal models showed partial capability for coarse hip-fracture interpretation, but they remained context-sensitive, unreliable for treatment-relevant subtyping, and highly confident even when incorrect. Their complementary error patterns are hypothesis-generating rather than evidence of clinical readiness. On the basis of these findings, we do not support unvalidated or uncontrolled clinical use of such models. As access to these tools expands, explicit usage boundaries, minimum performance expectations, repeated local revalidation, and sustained human oversight become increasingly necessary. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Candida auris is a multidrug-resistant fungal pathogen associated with high mortality in healthcare settings. Although colonization is recognized as the harbinger of invasive infection, predicting which patients will develop bloodstream infection (BSI) and when this transition will occur remains a clinical challenge. In this study, patients aged ≥18 years with C. auris colonization identified at İzmir City Hospital between January 2023 and June 2025 were retrospectively analyzed. Colonization was confirmed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Of 71 colonized patients (median age 65 years; 69.0% male; 93.0% intensive care unit (ICU)-admitted), 31 (43.7%) developed bloodstream infection (BSI). In-hospital mortality was 62.0%, rising to 74.2% in the BSI group, though this difference did not reach statistical significance (p = 0.105). Competing risks analysis using the Aalen–Johansen method showed a cumulative BSI incidence of 38.2% (95% confidence interval (CI): 28–50%) by day 10 and 43.0% (95% CI: 32–54%) by day 30 following colonization detection. On multivariate logistic regression, diabetes mellitus was the sole variable independently associated with a lower risk of BSI development (adjusted odds ratio (OR): 0.19; 95% CI: 0.06–0.68; p = 0.010); this finding was directionally consistent but did not reach statistical significance in the multivariable Fine–Gray competing risks model (subdistribution hazard ratio (SHR): 0.334; 95% CI: 0.108–1.040; p = 0.057). All 40 tested isolates had high fluconazole minimum inhibitory concentration (MIC) values; micafungin susceptibility was 92.5%, while anidulafungin resistance was observed in 32.5% of isolates. Our findings demonstrate that nearly half of colonized patients developed BSI, with no identifiable safe window for intervention, underscoring the necessity of sustained infection control measures and susceptibility-guided antifungal therapy. Full article

Multi-view point cloud registration is a fundamental technology for 3D reconstruction and indoor robot navigation and remains a core challenge for robust environmental perception. Its key difficulty lies in achieving globally consistent alignment of multiple partially overlapping point clouds efficiently and reliably. To address the limitations of existing methods, including low registration accuracy under small overlaps, severe error accumulation in long sequences, and the difficulty of balancing computational efficiency with global consistency, this paper proposes a multi-view point cloud registration framework that integrates spanning tree-based global topology constraints with a multi-scale pyramid-based local refinement strategy, specifically validated for indoor environments. First, a Voxel-Guided Normal Consistency Keypoint Extraction (VG-NCKE) method is presented. It leverages voxel grids to guide stable computation of local geometric features and filters candidate keypoints using a neighborhood normal direction consistency metric, effectively improving keypoint repeatability and spatial uniformity on unevenly distributed point clouds. Second, a coarse registration strategy with global constraints is constructed based on the Overlap Confidence-weighted Minimum Spanning Tree (OC-WST). It quantifies inter-frame overlap reliability as edge weights and employs Prim’s algorithm to build the minimum spanning tree as the topological skeleton for global registration. By prioritizing high-overlap frame pairs, the method suppresses error propagation and reduces the complexity of multi-view registration. Additionally, a multi-scale pyramid ICP fine registration algorithm is designed. It adopts a point-to-plane error model instead of the traditional point-to-point distance metric and performs progressive optimization through a three-layer point cloud pyramid from coarse to fine. This expands the convergence basin and gradually improves alignment accuracy, mitigating the sensitivity of single-scale ICP to initial poses. Extensive experiments on the indoor 3DMatch dataset and real indoor LiDAR sequences demonstrate that the proposed method outperforms competing approaches in terms of registration accuracy, computational efficiency, and long-sequence robustness, validating its effectiveness for indoor multi-view point cloud registration tasks. Full article

Hydrothermal alteration mapping is a critical component of porphyry copper exploration because alteration assemblages provide important vectors toward mineralization. This study presents a systematic evaluation of supervised machine learning algorithms for delineating hydrothermal alteration zones using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave infrared (SWIR) surface reflectance data (AST_07XT). The investigation focuses on the Nain region within the central Urumieh–Dokhtar Magmatic Arc (UDMA), Iran, a major metallogenic belt hosting numerous porphyry copper systems. Representative spectral endmembers corresponding to Al–OH-bearing and Mg–OH-bearing hydrothermal alteration minerals were extracted using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-dimensional visualization techniques. These endmembers were subsequently used to train and evaluate a comprehensive suite of supervised machine learning classifiers, including linear, kernel-based, tree-based, ensemble, probabilistic, boosting, and neural-network algorithms for pixel-wise hydrothermal alteration mapping. Model performance was evaluated using multiple statistical metrics, including overall accuracy (OA), average accuracy (AA), precision, recall, F1-score, Cohen’s kappa coefficient, area under the ROC curve (AUC), spatial cross-validation accuracy, uncertainty analysis, and spatial agreement analysis. Among the evaluated classifiers, SVM_Linear, SVM_RBF, LDA, and MLP achieved the highest classification performance, with overall accuracies exceeding 94% and strong spatial consistency between classified maps. The resulting alteration maps display spatially coherent distributions of Al–OH and Mg–OH minerals that are consistent with established hydrothermal alteration zoning models in porphyry–epithermal systems. The mapped hydrothermal alteration zones show strong spatial correspondence with known mineralized areas and alteration patterns within the Urumieh–Dokhtar Magmatic Arc, confirming the geological reliability of the classification results. Uncertainty analysis further indicates high model confidence across most alteration zones, with higher uncertainty values mainly restricted to transitional and spectrally heterogeneous regions. The results demonstrate that integrating ASTER SWIR imagery with supervised machine learning algorithms provides a robust, scalable, and transferable framework for regional-scale hydrothermal alteration mapping and mineral exploration in porphyry copper provinces. Full article

Background: Metabolite identification and annotation remain major bottlenecks in untargeted metabolomics because mass spectral features often lack sufficient specificity. High-confidence annotation requires experimental validation using authentic standards analyzed under matched chromatographic and ionization conditions, providing greater reliability than in silico predictions or database matching alone. This study aimed to develop a practical and scalable workflow for constructing a high-quality mass spectral library using a commercially available analytical standards kit. Methods: A total of 603 metabolites from the MSMLS kit were organized into 42 mixtures, each containing approximately 15 compounds. Mixture design was based on molecular mass and distribution coefficient values, specifically logD at pH 3.1, with a minimum logD spacing of 0.15 to improve chromatographic separation and reduce co-elution. This strategy was used to minimize the total number of injections while maintaining spectral quality. The resulting spectra were evaluated against online spectral resources and in silico fragmentation predictions. A preliminary proof-of-concept analysis was also performed using human serum samples. Results: Using this workflow, 471 metabolites, corresponding to approximately 78% of the standards, were successfully detected and incorporated into the spectral library. Comparison with online resources and in silico fragmentation predictions demonstrated improved spectral quality and reliability. The proof-of-concept serum analysis enabled identification of endogenous metabolites using the constructed library. In addition, the robustness and applicability of the workflow were further supported by a method validation study using metabolites derived from this library. Conclusions: This workflow provides a scalable strategy for constructing mass spectral libraries that balances spectral quality with analytical throughput. By using rational mixture design and authentic standards analyzed under matched experimental conditions, the approach enables substantial metabolite coverage while maintaining data reliability and minimizing experimental effort. Full article

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R² = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article

Possible violations of the no-signaling constraint in Weinberg-type nonlinear extensions motivate the question of whether entanglement could, under suitable conditions, become a resource for operational signaling. A minimal binary model is introduced in which, in each run, a sender selects a binary input bit and a receiver locally records a binary output bit. Signaling is defined operationally as a dependence of the local output statistics on the remote input and is summarized by a single channel parameter that can be estimated from data. An estimator and a corrected confidence interval are then introduced to assess this dependence quantitatively, while transmission reliability is expressed through the minimum decision error. On this basis, a conservative criterion is formulated, using an upper bound on the error and a threshold fixed in advance, to characterize a near-identity channel regime. Minimal reporting requirements are also proposed to document the conditions under which artifacts and classical leakage may reasonably be excluded. Full article

High renewable penetration introduces stochastic variability in distribution-network operation, requiring probabilistic AC power-flow tools that remain accurate in the tails while avoiding the computational burden of large Monte Carlo simulation. This paper presents a fully reproducible non-intrusive polynomial chaos expansion (PCE) framework for uncertainty propagation through nonlinear Newton–Raphson AC power flow. The method uses sparse-grid quadrature to train PCE surrogates from deterministic power-flow evaluations and is benchmarked against high-fidelity Monte Carlo simulations. In the validation, the IEEE 33-bus feeder is evaluated using up to 50,000 Monte Carlo samples, 95% bootstrap confidence intervals, PCE orders 2–5, correlated uncertainty scenarios, realistic thermal-loading recalibration, reactive-power sensitivity of renewable injections, multi-feeder testing on IEEE 33-bus, CIGRE MV, CIGRE LV, and IEEE 118-bus networks, and a 365-snapshot full-year daily screening. For the base IEEE 33-bus case, third-order PCE required only 494 deterministic power-flow evaluations and reproduced the 50,000-sample Monte Carlo benchmark with relative mean errors of 0.014% for minimum voltage, 0.119% for active losses, and 0.113% for substation import. The corresponding wall-clock speed-up was 13.29×, while reducing deterministic evaluations by approximately 101×. Correlated load–PV uncertainty increased the upper tail of substation import from 6.06 MW to 6.30 MW, and realistic thermal recalibration revealed line-loading p99 values above 100% for the 60% target case, demonstrating the operational value of physically meaningful ampacity settings. The proposed workflow provides an open, scalable, and tail-aware basis for uncertainty-informed distribution-network planning under renewable variability. Full article

Background/Objectives: Obstructive sleep apnea (OSA) and related sleep disorders affect a substantial proportion of hospitalized patients, with an estimated 48% pooled prevalence of undiagnosed OSA in cardiac inpatients and up to 80% of moderate-to-severe community OSA cases carrying no formal diagnosis at the time of hospital admission. In parallel, frailty—a state of heightened physiological vulnerability arising from cumulative multi-system biological decline—is present in 40–80% of inpatients and shares deep, bidirectional neurobiological pathways with sleep-disordered breathing through circadian dysregulation, intermittent hypoxia, hypothalamic–pituitary–adrenal axis activation, and chronic low-grade inflammation. Despite this convergence, no prior study has integrated validated, administratively computable frailty phenotyping with a machine learning framework specifically designed to predict inpatient sleep disorder diagnosis—and OSA in particular—at the point of hospital admission. The present study addresses this gap by developing an admission-time, explainable machine learning framework for the prediction of inpatient sleep disorder diagnoses (ICD-10 G47.x, encompassing OSA G47.3, insomnia G47.0, hypersomnia, and circadian rhythm disorders) and of insomnia specifically (ICD-10 G47.00). Methods: We developed and evaluated a suite of five binary classification models—XGBoost, Random Forest, LightGBM, CatBoost, and Decision Tree—using 9682 balanced hospitalization episodes from the MIMIC-IV (version 2.2) database. The predictor set comprised 23 admission-time structured features across three domains: (i) frailty and comorbidity burden, including the Hospital Frailty Risk Score (HFRS) derived from ICD-10 codes, the Elixhauser comorbidity index, prior admission history, and six binary disease flags (obesity, hypertension, type 2 diabetes, heart failure, COPD, and depression/anxiety); (ii) physiological and laboratory biomarkers from the first 24 h of care, including minimum SpO₂, heart rate variability, hemoglobin, creatinine, albumin, and arterial blood gas parameters; and (iii) sociodemographic and administrative variables encompassing age, sex, ethnicity, insurance type, and admission acuity. Model performance was assessed through five-fold stratified cross-validation and bootstrap confidence intervals (n = 1000 iterations), with predictor importance quantified using SHapley Additive exPlanations (SHAP). Results: XGBoost achieved the strongest aggregate performance across all evaluation metrics, attaining an area under the receiver operating characteristic curve (AUC) of 0.871 (95% CI: 0.856–0.887), accuracy of 79.6%, F1-score of 0.820, and sensitivity of 94.9%, correctly identifying 903 of 952 true positive cases in the held-out test set; all gradient boosting frameworks substantially outperformed the Decision Tree baseline (AUC 0.836). SHAP analysis identified the HFRS and Elixhauser index as the two dominant predictors, followed by depression/anxiety, obesity, hypertension, and minimum SpO₂—a hierarchy that recapitulates the canonical clinical phenotype of obstructive sleep apnea in frail inpatients rather than that of primary insomnia, indicating that the model is preferentially capturing the OSA–frailty axis within the broader G47.x outcome. The predicted probability outputs were well-calibrated across all risk deciles. Conclusions: Frailty-derived features, in combination with admission-time clinical and physiological data, can predict inpatient sleep disorder diagnoses—predominantly OSA—with high sensitivity and well-calibrated risk estimates. The deployable, interpretable nature of the XGBoost model makes it directly suitable for integration into clinical decision support systems, offering a screening tool that requires no dedicated instrumentation beyond routine admission data. By flagging high-risk patients at the moment of admission, the framework provides a concrete mechanism for accelerating referral for definitive diagnostic confirmation (overnight oximetry, polysomnography) and earlier initiation of CPAP and related therapies, with direct implications for reducing the persistent diagnostic gap, perioperative risk, and preventable adverse outcomes in frail hospitalized populations. Full article

Background: Gene fusions play a pivotal role in the pathogenesis and classification of hematologic malignancies. RNA sequencing (RNA-seq) has emerged as a powerful tool for detecting gene fusions; however, many clinical studies have focused on targeted RNA-seq, and optimal parameters for whole transcriptome RNA-seq remain uncertain. Methods: We retrospectively analyzed whole RNA-seq data from 301 patients diagnosed with acute leukemia between October 2022 and May 2025 to characterize the landscape of pathogenic gene fusions. Fusions were identified using the Arriba algorithm, and subsampling analyses were performed on cases with recurrent fusions to determine the minimum sequencing output required for reliable detection. Results: Pathogenic gene fusions were identified in 113 of 301 patients (37.5%). Whole RNA-seq detected fusions that were not identifiable by conventional assays, including UBTF::ATXN7L3, and highlighted frequent fusion events, such as ZNF384 rearrangements. Subsampling analysis demonstrated that a sequencing output ≥ 100 million reads (moderate confidence) or ≥300 million reads (high confidence) was sufficient for 100% detection of recurrent fusions. Conclusions: Whole RNA-seq reliably detects clinically relevant gene fusions in acute leukemia, aligns well with conventional karyotyping results, and surpasses targeted RNA-seq in comprehensiveness. A sequencing output of at least 100 million reads is recommended for clinical fusion detection. Full article

This study examined how digital equity conditions and bundled professional development policies are associated with sustainable teacher learning, self-efficacy, and student engagement in Greek primary schools. A total of 460 in-service teachers from urban, suburban, and rural areas participated in the study. Data were collected through Likert-scale measures assessing information systems use, TPACK-aligned professional development outcomes, teacher self-efficacy, implementation challenges, and student engagement. The analysis included ANOVA, MANOVA, OLS regression with interaction terms, and theory-informed indirect-pathway models. The findings indicated that infrastructure funding alone was not significantly associated with teacher capacity or student engagement after the introduction of relevant controls. More consistent associations emerged when funding was combined with mandated and time-protected professional development, together with minimum connectivity standards. Teacher self-efficacy was consistent with a partial indirect pathway between information systems use and student engagement, while stronger indirect associations were observed among early-career teachers. In addition, a bundled governance index was associated with narrower urban–rural disparities in teacher capacity. The findings suggest that sustainable digital equity in primary education depends not only on access to resources but also on coherent professional support structures that are associated with teacher confidence, instructional continuity, and long-term engagement. Implications are discussed for the design of sustainable professional development policies in teacher education and primary schooling. Full article

Background/Objectives: In 2018, a classification system for periodontal and peri-implant diseases and conditions defined an endo-periodontal lesion (EPL) as a pathological communication between the endodontic and periodontal tissues of a given tooth. As the evidence to define the etiology, diagnosis, prognosis and treatment was considered limited, a cross-sectional study was carried out to evaluate its prevalence in a population treated at the Periodontics Department of Faculty of Dentistry of University of Buenos Aires (FOUBA). The primary objective was to evaluate the prevalence of EPL. The secondary objective was to identify potential risk indicators associated with their prevalence. Methods: Patients referred for first time to the Periodontics Department of FOUBA during April to June 2025 were consecutively selected. Clinical and radiographic examination was carried out. Categorical outcomes were described using proportions. The crude association between the prevalence of EPL and each of the recorded factors was determined by means of the chi-square test and a logistic regression analysis. Results: A total of 182 participants (128 women and 54 men) with a mean age of 50.8 (standard deviation = 15.6) years were included. The prevalence of participants with EPL was 14.8%. The average was 1.7 teeth per participant with a minimum of 1 tooth and a maximum of 5 teeth. In total, 85.2% of participants with EPL had stage III–IV generalized periodontitis, grade B or C. The logistic regression analysis identified periodontitis stage III–IV (OR = 5.9; 95% Confidence interval [1.9: 18.9] (p = 0.003)) as a potential risk indicator for EPL. Conclusions: The prevalence of participants with EPL in a teaching hospital of the University of Buenos Aires was 14.8%. EPL was more frequently found in participants with periodontitis stage III–IV. Periodontitis stage III–IV was considered a potential risk indicator of EPL. Full article

Accurate prediction of loan defaults is essential for financial institutions seeking to minimize credit losses and maintain portfolio stability. In the vehicle financing segment of emerging markets, real-world datasets frequently exhibit extreme class imbalance ratios that far exceed those encountered in standard benchmark corpora, posing severe challenges for conventional machine learning pipelines. This study introduces a gradient boosting framework integrating Weight of Evidence (WoE) transformation, Bayesian hyperparameter optimization, and three complementary classifiers—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)—to predict vehicle loan default risk. The methodology is evaluated on a large-scale, fully anonymized Turkish vehicle loan dataset ($N=207,572$) with an extreme imbalance ratio of 1:1133 (183 defaults versus 207,389 non-defaults). A strict three-way data partition (60% training, 20% validation, 20% test) is adopted to ensure leakage-free model selection and unbiased performance estimation. A multi-stage experimental pipeline is developed encompassing: (i) statistical feature selection via Mann–Whitney U and chi-square tests with adaptive thresholding, (ii) a comparative analysis of seven resampling strategies including Synthetic Minority Oversampling Technique (SMOTE) variants, Adaptive Synthetic Sampling (ADASYN), and focal loss weighting, (iii) a greedy forward selection ensemble procedure for heterogeneous model fusion, and (iv) a systematic training-set size sensitivity analysis across eight majority undersampling ratios. Under the leakage-free evaluation protocol, the highest-AUC individual model (LightGBM with SMOTE-ENN) achieves an Area Under the Curve (AUC) Receiver Operating Characteristic (ROC) of 0.710 (95% bootstrap CI: 0.614–0.798), while CatBoost with cost-sensitive weighting exhibits superior operational metrics (KS $=0.389$, PR-AUC $=0.011$). The greedy ensemble procedure exhibits high selection instability with only 37 validation-set positives, providing a methodological finding on the minimum sample requirements for reliable ensemble construction under extreme scarcity. Ablation results confirm that WoE encoding contributes 3.1 percentage points to the overall AUC gain. Tree SHAP-based interpretability analysis identifies the financing-to-age ratio, WoE-encoded occupation group, and log financing amount as the primary predictive drivers, with cross-model stability confirmed via Spearman rank correlation. A decision support analysis provides precision–recall curves, a Brier score of 0.0082, reliability diagrams, and threshold-dependent performance at operationally plausible review rates. Fairness evaluation across gender and marital status subgroups demonstrates that threshold-dependent metrics such as Disparate Impact Ratio and Equalized Odds Gap are inherently compromised under extreme minority scarcity, whereas rank-based subgroup AUC analysis with bootstrap 95% confidence intervals preserves meaningful discriminative assessment. These findings provide an empirically validated framework for credit default prediction in highly imbalanced and data-scarce financial environments. Full article