Search Results (2,815)

Background/Objectives: Estimating mortality risk in older adults presenting to the emergency department (ED) is challenging because risk is shaped by comorbidity burden, frailty, and systemic inflammation. We examined the individual and combined prognostic contributions of the Age-adjusted Charlson Comorbidity Index (CCI), the Clinical Frailty Scale (CFS), and the Systemic Immune-Inflammation Index (SII) to 30-day mortality. Methods: This prospective observational cohort included 326 adults aged ≥65 years evaluated in a tertiary ED from September 2025 through April 2026. CCI, CFS, and SII were assessed at admission, with SII analyzed after logarithmic transformation [ln(SII)]. Associations with 30-day mortality were examined using logistic regression. Discriminative performance was evaluated by receiver operating characteristic analysis, and the integrated model underwent bootstrap internal validation and calibration assessment. Results: Thirty-day mortality occurred in 60 patients (18.4%). CCI, CFS, and ln(SII) were each independently associated with mortality in the integrated multivariable model (all p < 0.05). Individual AUCs were 0.741 for CCI, 0.703 for CFS, and 0.662 for ln(SII), with CCI showing the highest value. The integrated model achieved the highest observed discrimination (AUC 0.793; optimism-corrected AUC 0.783) and showed acceptable internal calibration (Brier score 0.127; bootstrap-corrected calibration intercept 0.003; calibration slope 0.957). Conclusions: Comorbidity burden, frailty, and systemic inflammation each provided independent prognostic information for 30-day mortality in older ED patients. Although CCI was the strongest individual predictor, the integrated model achieved the highest observed discrimination. However, its incremental benefit over some two-predictor models was modest and not statistically significant. Full article

Objective: This prospective study evaluated systolic and diastolic cardiac functions in pediatric cirrhosis using conventional and tissue Doppler imaging (TDI), and explored their correlation with liver severity scores. Methods: Conducted at Inonu University, we enrolled 75 pediatric cirrhotic patients (44 compensated, 31 decompensated) from Pediatric Gastroenterology and 61 controls of similar age and sex without structural/functional heart disease from Pediatric Cardiology outpatients. All underwent clinical, laboratory, and echocardiographic assessments. To address multiple testing, Bonferroni correction was applied. Adjustments for age, sex, and disease severity were integrated into the correlation matrices to control for confounding parameters. Results: The cirrhosis cohort exhibited significantly higher heart rates, ejection fractions, and aortic/pulmonary velocity-time integrals than controls (p < 0.05). Conversely, growth-independent diastolic indices revealed distinct relaxation impairment: transmitral E/A and annular e′/a′ ratios were significantly lower, while Mitral E/e′ was elevated (p < 0.05). Deceleration time, isovolumetric relaxation time, and Myocardial Performance Index (MPI) were significantly prolonged (p < 0.01). Decompensated patients demonstrated significantly higher left ventricular filling pressures (Mitral E/e′) than compensated patients and controls (p = 0.002). After adjusting for age and sex, the Pediatric End-Stage Liver Disease (PELD) score maintained significant positive correlations with heart rate (r = 0.408) and Mitral E/e′ (r = 0.433) (p ≤ 0.002). FIB-4 and APRI scores showed positive correlations with VTI and TAPSE, markers of right ventricular volume overload (p < 0.05). Conclusions: Hyperdynamic circulation appears to mask resting systolic metrics in pediatric cirrhosis. However, prolonged diastolic phases and increased MPI suggest presence of subclinical myocardial dysfunction, especially during clinical decompensation. Routine non-invasive liver staging scores show potential clinical utility as ancillary screening tools for early cardiovascular risk evaluation. Full article

Breast cancer continues to represent a major global health burden, highlighting the need for effective approaches to risk stratification and clinical decision support. Conventional methods, including the Breast Imaging Reporting and Data System (BI-RADS) and histopathological classifications, primarily rely on clinical assessments and may not fully account for relevant demographic and behavioral characteristics. To overcome these limitations, we present an integrated framework combining K-Means clustering, Random Forest classification, and Explainable Artificial Intelligence (XAI) to support breast cancer risk stratification and patient prioritization. The proposed methodology uses clustering to stratify patients into low-, medium-, and high-risk groups, followed by supervised machine learning to reproduce the cluster-derived risk categories, achieving an accuracy of 98%. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) are incorporated to identify the variables that most strongly influence individual classifications, including body mass index (BMI), breastfeeding practices, and maternal age. By integrating multiple dimensions of patient information, the framework provides a more comprehensive characterization of risk while increasing the transparency of the decision-making process. Its relatively simple and scalable architecture also facilitates potential implementation in healthcare environments with limited resources. Simulation experiments further provide a proof-of-concept evaluation of the proposed prioritization approach. Compared with random patient selection, the strategy achieved a substantially higher average severity score (1.66 vs. 0.92) and prioritized 4.3 times more high-risk patients. These findings suggest that the proposed framework can serve as an intelligent decision-support tool for prioritizing breast cancer patients and improving resource allocation when healthcare capacity is constrained. Full article

Background/Objectives: Lymph-node metastasis remains central to staging, prognosis, surveillance, and treatment planning in malignant melanoma. At present, most statistical models assessing nodal metastatic risk in malignant melanoma consider pathological descriptors, inflammatory markers, metabolic alterations, clinical data, and related variables independently of each other. Therefore, we developed a transparent artificial intelligence (AI)-based approach to assess whether the propensity for nodal metastasis is determined by a single layer of local histopathological aggressiveness or by the integration of different biological levels, including local histopathological aggressiveness, systemic inflammatory–metabolic dysregulation, biological heterogeneity, or a clinicobiological pattern. Methods: In this retrospective study, we assessed 73 adult patients undergoing surgical removal of malignant melanoma. The primary endpoint was histopathologically confirmed lymph-node metastasis. Routinely collected patient-related data, including clinical, anatomical, operative, histopathological, nodal, comorbidity, biological, clinical course, and available staging data, were structured into interpretable constructs. These included the Histopathologic Aggressiveness Index (HAI), the Inflammatory–Metabolic Dysregulation Index (IMDI), the Biological–Histological Discordance Score (BHDS), model-estimated nodal metastatic probability, integrated clinicobiological risk, and explanation stability. The AI-based framework was evaluated by applying bias-reduced and penalized logistic regression, machine learning benchmarking, leave-one-out cross-validation, bootstrap estimation, permutation testing, decision curve analysis, rule extraction, feature stability evaluation, network analysis, similarity-based retrieval, conformal uncertainty estimation, and unsupervised phenomapping. Results: For 72 out of 73 patients, nodal histopathology results were available. Among these patients, 22 had positive nodal status. Positive nodal status was associated with a higher Breslow thickness, an increased mitotic rate, ulceration, lymphovascular invasion, a nodular subtype, and palpable adenopathy. The HAI demonstrated the strongest discriminative signal between node-positive and node-negative patients (median values of 67.8 vs. 45.3; p < 0.001) and retained an independent association with nodal metastasis within the bias-reduced logistic model (odds ratio [OR] per 10-point increase: 2.74; 95% confidence interval [CI]: 1.58–4.75; p < 0.001). The IMDI showed a weak exploratory relationship and did not retain an independent association after adjustment. Similarly, the BHDS did not show significant differences in separating the two endpoint groups. The penalized logistic model including only the HAI showed good performance under leave-one-out cross-validation, with ROC AUC = 0.889, PR-AUC = 0.706, and Brier score = 0.137. Through rule extraction, we found a cohort-specific HAI threshold value > 58.6, above which all node-positive cases were located. With respect to explainability, feature stability, network analysis, similarity retrieval, conformal prediction, and phenomapping, there was convergence toward a dominant high-risk phenotype defined primarily by histopathological criteria. Conclusions: Routine melanoma registries may be transformed into internally evaluated melanoma intelligence frameworks. Histopathologically confirmed lymph-node metastasis among patients with malignant melanoma was organized primarily along an axis of local histopathological aggressiveness, while systemic inflammatory–metabolic dysregulation provided subordinate contextual biological information. Full article

Páramo ecosystems are among the most biodiverse and hydrologically critical landscapes on Earth, yet their long-term sustainability is increasingly threatened by climate-driven wildfires. Vegetation Cover Fires (VCFs) in these high-altitude environments degrade carbon stocks, disrupt freshwater regulation, and undermine biodiversity conservation goals central to the UN Sustainable Development Goals (SDGs 13, 15, and 6). Between 2001 and 2023, 128 fire events consumed approximately 815 ha in the Sumapaz locality (the world’s largest continuous páramo), representing 64.9% of all fires recorded across Bogotá’s 20 localities. Despite this disproportionate ecological and social impact, no spatially explicit, operational risk management framework has been available for the region, representing a critical sustainability governance gap. This study addresses that gap by proposing an integrated climate-adaptive risk assessment and management strategy based on (i) the Canadian Forest Fire Danger Rating System Fire Weather Index (FWI), derived from ERA5 reanalysis climate data; (ii) spatial and temporal hotspot analysis of MODIS FIRMS active fire detections; and (iii) IDEAM’s multi-component vulnerability and threat scoring protocol. Spatial data were processed using ArcGIS, and FWI sub-indices were computed for each month of the 2001–2023 period. The FWI averaged 0.78 (low danger) across the study period yet peaked at 13.7 in February 2010 (moderate-to-high danger), consistent with the year of highest recorded fire activity (19 events). High- and very high-risk areas (3.70% combined) coincide with slopes >25%, the presence of the invasive and pyrogenic Ulex europaeus, and proximity to populated and agricultural lands. This study concludes with a three-pillar risk management framework—risk knowledge, risk reduction, and disaster management—providing spatially targeted, operationally viable strategies for local and institutional actors that directly support the sustainable conservation of páramo ecosystem services (water supply, carbon sequestration, biodiversity). The framework is designed to be updatable on a monthly basis using freely available ERA5 data, enabling continuous adaptive governance of wildfire risk as a contribution to long-term territorial sustainability. Limitations regarding MODIS detection uncertainty, ERA5 spatial resolution in complex terrain, and the need for probabilistic modeling are explicitly acknowledged. Full article

Optimizing irrigation amount and frequency is critical for maize production in arid sandy loam soils, which are characterized by severe water scarcity and high leaching risks. This study aimed to determine the physiological and multi-objective synergistic responses of spring maize to different drip irrigation regimes in the sandy soil region of Southern Xinjiang and to identify the optimal water management strategy. A two-year field experiment evaluated four irrigation amounts (W₁: 350 mm, W₂: 500 mm, W₃: 650 mm, W₄: 800 mm) combined with three irrigation frequencies (F₄: 4-day, F₇: 7-day, F₁₀: 10-day intervals). The results indicated that increasing irrigation amount and frequency generally improved canopy physiological performance (leaf area index, SPAD values, net photosynthetic rates, and photosystem II photochemical efficiency), though the response magnitudes varied among specific physiological parameters and irrigation levels. Grain yield increased with irrigation amount and was highest at 800 mm under F₄ or F₇ frequency; however, excessive water inputs led to reduced water productivity. Conversely, nutrient use efficiencies for nitrogen, phosphorus, and potassium exhibited a parabolic response, reaching their maxima under W₃ (650 mm) and F₇ (7-day interval). To reconcile the trade-offs between maximum productivity and resource conservation, the entropy weight method coupled with TOPSIS (EWM-TOPSIS) was applied. The results showed that grain yield and water productivity were the dominant indicators determining overall system efficacy, with the W₃F₇ treatment consistently achieving the highest relative closeness (over 76%) to the ideal solution in both years. While this optimal recommendation relies on the multi-criteria weighting framework of the EWM-TOPSIS model, integrating a 650 mm irrigation quota with a 7-day interval offers a practical agronomic strategy to balance yield, water productivity, and nutrient efficiency in arid agroecosystems. Full article

Musculoskeletal injuries are the leading cause of time loss in professional football, yet machine learning models integrating multi-protocol force-plate data for injury-risk classification are absent from the Latin American professional football literature. Purpose: This study aims to develop and internally cross-validate a supervised Random Forest model for injury risk classification using full-season VALD ForceDecks data from a professional Costa Rican club, with thresholds derived from receiver operating characteristic (ROC) analyses. Methods: We utilized an observational longitudinal study (September 2024 to April 2026). One hundred and twenty-six male footballers from five competitive categories underwent 518 dual-force-plate assessments across four protocols (Nordic Hamstring, isometric mid-thigh pull, isometric adductor squeeze, countermovement jump), yielding 21 variables; one assessment per player entered the model. Record linkage against 262 surveillance-documented time-loss events identified 53 players with pre-injury assessments as the positive class (42.1%; ratio 1:1.4). SMOTE (k = 5) was applied within each training fold only. A Random Forest model (500 trees; depth 4) used 5-fold stratified cross-validation. The HIGH boundary was set at the Youden index, and the LOW boundary was set as the first score quartile. Results: The mean AUC-ROC across folds was 0.683 +/− 0.050, the only estimate of generalisation reported. Refitted on the complete dataset and applied back to the same players, the model gave at the Youden cut-point (score ≥ 45.2) an apparent sensitivity of 100% and specificity of 95.9%; these resubstitution values are optimistically biased. The leading predictors were Pull RFD (10.0%), CMJ power per body mass (7.2%) and eccentric CMJ peak force (6.6%). The risk tiers were HIGH 56 (44.4%), MEDIUM 39 (31.0%), and LOW 31 (24.6%). Conclusions: Multi-protocol force-plate data yield moderate internally cross-validated discrimination of injury risk. The near-perfect threshold metrics are apparent values, not generalisation performance. Rate of force development and eccentric force capacity outranked asymmetry indices. Prospective external validation is required before clinical use. Full article

The Berca–Arbănași region of Buzău County (Romania) is of exceptional geochemical interest due to diapiric structures, active mud volcanoes, and historical subsurface hydrocarbon deposits. This study presents the first comprehensive geochemical assessment of surface soils from the Berca–Arbănași Subcarpathian zone, based on the analysis of 27 soil samples collected along three north–south transects and the determination of 12 potentially toxic elements (As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, V, Zn) using aqua regia digestion and ICP-MS. The local geochemical background was calculated using the iterative median ± 2MAD method. Twelve pollution and ecological risk indices were computed: the Pollution Index (PI), Contamination Factor (CF), Geoaccumulation Index (Igeo), Enrichment Factor (EF), Pollution Load Index (PLI), Modified Degree of Contamination (mCd), Nemerow Integrated Pollution Index (PINemerow), Ecological Risk Factor (E_rⁱ), Ecological Risk Index (RI), Mean Effect Range-Median Quotient (MERMQ), Degree of contamination (Cdeg), and the V/Ni petroleum origin indicator. Results show that 66.7% of samples are classified as polluted (PLI ≥ 1; mean = 1.102), with moderate enrichment in Cd, Cu, Hg, Pb, and Zn, attributable to diffuse anthropogenic sources. The ecological risk index (RI) remains low across all samples (mean = 35.96; all < 150), indicating that, despite moderate pollution, ecological risk is currently low. The V/Ni ratio (0.391–0.884, mean = 0.608) is below 1.0 for all samples, indicating a lithogenic (not petroleum) origin of vanadium and nickel and confirming a negligible geochemical impact of mud volcanoes and hydrocarbon extraction activities in the area at the sampled locations. The study establishes baseline geochemical reference values for the Berca–Arbănași area and provides data for sustainable land management, environmental monitoring, and evidence-based policymaking. These results directly support the objectives of the EU Soil Strategy 2030 and align with the United Nations Sustainable Development Goals on food security (SDG 2), good health and well-being (SDG 3), and life on land (SDG 15). Full article

We develop a predictive–prescriptive decision-support framework for football squad composition under budget and positional constraints, in which mixed-integer optimization is the central methodological contribution. The framework integrates four components: (i) construction of a Sports Performance Index (SPI) via principal component analysis (PCA) of standardized per-90 performance features; (ii) leakage-free, player-wise, five-fold cross-fitted prediction of seasonal goals plus assists using ordinary least squares (OLS) as the primary input model, regularized linear models as additional baselines, and gradient boosting (XGBoost) as a nonlinear benchmark and uncertainty-estimation component, with each player-season receiving an out-of-fold prediction; (iii) a mixed-integer linear program (MILP) that selects a player-season portfolio under budget, position composition, squad-size, and duplicate-player constraints, with a tunable weight balancing predicted attacking output against the performance index; and (iv) a robust counterpart that incorporates prediction uncertainty into the optimization. The framework is implemented on English Premier League data covering the 2023–2024 and 2024–2025 seasons (N = 218 forward and midfielder player-seasons with at least 1800 min of playing time). Cross-fitted OLS attains a five-fold mean R² = 0.76 ± 0.03 with stable performance across folds. The exact MILP outperforms a benefit-to-cost greedy heuristic by up to 9.4% and the best of 1000 random feasible rosters by 41.0–53.0% in objective value, while guaranteeing feasibility of all composition and budget constraints. Sensitivity analyses across budget tightness, position bounds, squad size, and cost proxy demonstrate qualitative robustness. The robust counterpart with risk-aversion parameter κ ∈ {0, 0.5, 1.0} produces a Jaccard roster overlap of 0.75 relative to the nominal solution and reduces average selected-player prediction uncertainty from 2.65 to 2.49. The contribution is a mathematically rigorous, integer-programming-centered decision-support framework that integrates machine learning with optimization for sports business analytics and is directly applicable to club-level squad planning. Full article

Spatiotemporal prediction of groundwater quality is of great significance for regional water environmental safety assessment, pollution risk identification, and urban groundwater resource management. To address the difficulty of existing methods in simultaneously characterizing multi-indicator coupling relationships, temporal evolution processes, and spatial heterogeneity, this study proposes a spatiotemporal groundwater quality prediction model under multi-indicator coupling constraints. First, indicators including dissolved oxygen, total nitrogen, electrical conductivity, dissolved organic carbon, pH, permanganate index, and total phosphorus are uniformly mapped into a risk space to construct an integrated groundwater quality risk index. Then, based on monthly groundwater monitoring data from Yiyang City during 2000–2023, continuous regional grid sequences are generated. In terms of model design, the Temporal Difference Interaction Module (TDIM) is introduced to enhance multi-scale temporal variation modeling, Region-Guided Feature Modulation (RGFM) is used to strengthen regional heterogeneity representation, and Spatiotemporal Boundary-Aware Loss (STB Loss) is adopted to maintain spatiotemporal boundary consistency. The experimental results show that the proposed method achieves a Structural Similarity Index Measure (SSIM) of $0.9814\pm 0.0085$, a Peak Signal-to-Noise Ratio (PSNR) of $40.47\pm 2.19$, a Mean Absolute Error (MAE) of $2.80\times {10}^{-3}\pm 1.50\times {10}^{-3}$, and a Root Mean Square Error (RMSE) of $9.70\times {10}^{-3}\pm 2.69\times {10}^{-3}$, outperforming comparison models overall and providing effective support for dynamic groundwater quality prediction and water environmental safety assessment. Full article

As financial institutions become increasingly reliant on similar foundational AI models, cloud infrastructure, data providers, and AI middleware platforms, diverse firms may begin to interpret and respond to market signals in increasingly similar ways. This paper examines model monoculture risk as a potential source of system-level exposure in banking and financial markets. Drawing on a structured, purposive conceptual review of the academic and regulatory literature, it develops the M³ Framework—Model, Market, and Middleware—to integrate three interacting mechanisms: Model Similarity, Market Synchronisation, and Middleware Concentration. Rather than treating these as a fixed causal sequence, the framework identifies their independent and cross-layer effects and distinguishes AI-mediated synchronisation from correlation arising from common exposures, conventional herding, and shared macroeconomic shocks. The paper further proposes the Model Monoculture Risk Index (MMRI) as a prototype index architecture and conceptual supervisory screening framework for characterising configurations of AI-related convergence, rather than as a calibrated quantitative measure of realised systemic risk. An illustrative application demonstrates how exposure classifications vary across alternative M³ configurations and attribution assumptions. The paper argues that governance should prioritise cross-institutional monitoring, dependency mapping, meaningful substitutability, and targeted stress testing rather than diversification by default. The framework provides a structured basis for future empirical research on AI-driven convergence and financial stability. Full article

Background: Major spine surgery carries an inherent risk of hemodynamic instability due to prone positioning, significant blood loss, and the strict necessity to maintain adequate spinal cord perfusion. Hemodynamic management mainly relies on a reactive approach, treating hypotension only after it occurs, which increases the risk of postoperative complications such as acute kidney injury and ischemic events. This narrative review evaluates the clinical impact, current evidence, and future perspectives of integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into perioperative hemodynamic care. Methods: A comprehensive literature search was conducted through PubMed, EMBASE, and the Cochrane Library, spanning from inception to January 2026. The search strategy employed combinations of Medical Subject Headings terms and keywords related to “Artificial Intelligence,” “Machine Learning,” “Hypotension Prediction Index,” “hemodynamic monitoring,” and “major spine surgery.” Studies were selected based on their relevance to predictive hemodynamic algorithms, goal-directed fluid therapy (GDFT), and automated closed-loop systems within the perioperative setting of complex spinal interventions. Results: Five studies show that AI/ML tools can improve hemodynamic management in spine surgery: an hypotension prediction index (HPI)-guided algorithm reduced intraoperative hypotension during prone spinal fusion; a machine learning model accurately predicted massive blood loss in metastatic spinal disease; an AutoML framework linked intraoperative hypertension to worse neurological recovery after spinal cord injury (SCI); a case report showed HPI-guided goal-directed therapy enabled safe, transfusion-free major spine surgery; and topological network analysis identified a narrow optimal mean arterial pressure (MAP) range for neurological recovery after SCI. Collectively, these preliminary findings suggest a potential role for AI/ML in reducing hemodynamic instability and enabling more individualized perioperative management in spine surgery. Rather than converging on a single verdict, these five studies fall into three distinct evidentiary categories when appraised using a structured model-validation (V1–V4) and clinical-translation (T0–T4) framework applied within each category: a real-time monitoring technology (HPI) with a substantial extra-spinal evidence base but a limited spine-specific replication record; a single, externally validated but clinically unproven preoperative prediction model; and two retrospective, hypothesis-generating discovery frameworks that remain exploratory irrespective of surgical domain. Conclusions: The evidence identified does not support a single, unified statement about “AI/ML in spine surgery.” Instead, it points to three distinct situations that warrant separate research priorities: consolidating spine-specific replication of an otherwise mature monitoring technology (HPI); externally confirming the clinical utility, rather than only the discriminative accuracy, of a single preoperative prediction model; and prospectively testing the retrospectively derived targets generated by discovery-oriented analytic frameworks. Considered together, these findings should inform hypothesis-driven research design rather than a single implementation-readiness judgment. Full article

Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle loss, affecting 30–60% of patients with advanced renal cell carcinoma (RCC). It significantly impacts treatment tolerance, quality of life, and prognosis, yet its diagnosis and management remain challenging due to fragmented RCC-specific evidence, particularly in the era of immune checkpoint inhibitor (ICI)-based therapy. The pathogenesis involves persistent systemic inflammation, metabolic reprogramming, and tumor–host interactions. The IL-6/STAT3 and TNF-α/NF-κB pathways are central to muscle catabolism, while tumor-derived mediators such as GDF15 and PTHrP, along with mitochondrial dysfunction, further drive cachexia progression. For prognostic assessment, CT-derived skeletal muscle mass evaluation combined with systemic inflammatory and nutritional biomarkers—including neutrophil-to-lymphocyte ratio (NLR), modified Glasgow Prognostic Score (mGPS), prognostic nutritional index (PNI), and cachexia index (CXI)—has improved risk stratification in advanced RCC. Preclinical and emerging clinical data suggest that targeted therapies may partially attenuate cachexia by modulating inflammatory signaling, while multimodal interventions integrating nutritional support and exercise rehabilitation remain the cornerstone of management. Novel strategies, such as inhibition of the GDF15/GFRAL axis, are under active investigation. Future research should prioritize identification of early biomarkers, standardization of cachexia assessment, and prospective evaluation of cachexia-directed interventions in the immunotherapy era. Integrating cachexia assessment into routine practice may ultimately enable personalized treatment and improve long-term outcomes for patients with advanced RCC. Full article

Background/Objectives: Adolescents with type 1 diabetes mellitus (T1DM) are particularly vulnerable to psychological distress, disordered-eating (DE) risk and negative self-perceived health status (SPHS), which may compromise metabolic control and self-care. Although these factors have been studied individually, the usefulness of brief and clinically feasible screening tools to detect these dimensions remains underexplored. This study aimed to: (1) examine associations between distress, DE risk and SPHS; (2) explore their relationships with glycemic control, clinical profile and self-care; and (3) assess the influence of age and diabetes duration on these outcomes. Methods: A cross-sectional study was conducted in 37 adolescents with T1DM in Spain. Sociodemographic, anthropometric and clinical variables were obtained from medical records. Distress, DE risk and self-care were assessed using validated questionnaires, while SPHS was measured using single-item question. Pearson and Spearman correlations were performed. Results: Distress was present in 48.6% of participants and DE risk in 29.7%, while 67.6% reported positive SPHS. Distress correlated with higher HbA1c and lower TIR. DE risk correlated with BMI. Lower SPHS was associated with higher HbA1c. Age and diabetes duration were associated with greater glycemic variability, and diabetes duration also correlated with higher glucose and TyG-based insulin resistance indexes. No significant associations were found for self-care behaviors. Conclusions: Distress, DE risk and SPHS, assessed through feasible screening tools, showed meaningful associations with metabolic outcomes in adolescents with T1DM. Integrating brief psychological screening tools into routine care may facilitate early identification of psychological vulnerability profiles in this population during routine practice. Full article

Background/Objectives: Cerebral small vessel disease (SVD) is a heterogeneous condition in which similar conventional MRI findings may be associated with different clinical manifestations and pathogenetic mechanisms. Previously, hierarchical clustering of structural MRI features in patients with severe white matter hyperintensities (Fazekas 3) identified two MRI phenotypes, designated MRI Type 1 and MRI Type 2. Diffusion MRI (dMRI) may provide additional information about the microstructural differences between these phenotypes. To compare white matter microstructure between MRI Type 1 and MRI Type 2 of sporadic age-related SVD using signal-based and biophysical dMRI models. Methods: This cross-sectional study included 75 patients with SVD and 36 age- and sex-matched healthy controls. Among the patients with SVD, 43 had MRI Type 1 and 32 had MRI Type 2. All participants underwent structural and multi-shell dMRI on a 3 Tesla MRI scanner. Diffusion metrics were derived using multiple models: Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), White Matter Tract Integrity (WMTI), and the Multi-compartment Spherical Mean Technique (MC-SMT). Tract-profile analysis was performed in three corpus callosum segments: the forceps major, forceps minor, and body. Group differences were assessed using age- and sex-adjusted general linear models with correction for multiple comparisons. The combined discriminative value of dMRI metrics was evaluated using regularized Elastic Net logistic regression with repeated nested five-fold cross-validation. Results: After adjustment for age and sex, the overall group effect remained significant for 45 of 48 global dMRI measures following Benjamini–Hochberg correction. Compared with MRI Type 2, MRI Type 1 showed lower fractional anisotropy (FA), neurite density index (NDI), intra-axonal volume fraction (INTRA), axonal water fraction (AWF), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK), and higher mean diffusivity (MD), radial diffusivity (RD), extra-axonal mean diffusivity (EXTRA_MD), extra-axonal transverse diffusivity (EXTRA_TRANS), and extra-axonal radial diffusivity (radEAD). These differences were generally most pronounced in the body of the corpus callosum. In the segmental analysis, 131 of 144 values showed a significant overall group effect after correction, and 108 demonstrated significant differences between MRI Type 1 and MRI Type 2. The largest effects were observed in the 60–80% interval of the corpus callosum body, particularly for AWF, MK, INTRA, EXTRA_TRANS, RK, FA, RD, radEAD, and MD. An Elastic Net model combining age, sex, and 48 global dMRI measures discriminated MRI Type 1 from MRI Type 2 with an internally validated area under the curve of 0.866 (95% CI, 0.762–0.953), accuracy of 86.7%, sensitivity of 75.0%, and specificity of 95.3%. Ten dMRI features showed a selection frequency of at least 70% across repeated model construction. Conclusions: MRI Type 1 is characterized by more severe and spatially extensive corpus callosum microstructural abnormalities than MRI Type 2, despite broadly similar vascular risk-factor profiles. The findings support the heterogeneity of sporadic age-related SVD and indicate that combined signal-based and biophysical dMRI metrics may improve MRI phenotyping. The observed associations should be interpreted as indirect markers of tissue microstructure and require confirmation in larger, independent, and longitudinal cohorts. Full article