Search Results (4,721)

Physical climate hazards—floods, storms, heatwaves, and wildfires—are increasingly disrupting banking operations and generating growing litigation and legal-risk exposures, yet operational risk remains one of the least studied channels through which climate change may affect financial institutions. This paper provides a critical review of the emerging literature on climate-related operational risk in banking, covering both physical disruptions and the legal risk dimension explicitly recognised within the Basel operational risk framework. We map the empirical evidence, critically evaluate the methodological toolkit—event studies, fixed-effects regressions, difference-in-differences, dynamic panel estimators, and logit models—and assess their suitability for a domain characterised by data scarcity, rare events, and non-linearity. Building on this assessment, we outline a conceptual methodological roadmap intended to guide future research, organised around three stages: (i) machine learning-based variable selection and anomaly detection applied to operational loss and climate databases; (ii) econometric modelling of climate-related operational events with explicit identification strategies; and (iii) agent-based modelling to simulate system-wide propagation of climate shocks. Each stage can be conceptually related to elements of the Basel operational risk framework, offering a structured research programme for academics and a diagnostic toolkit for supervisors and risk managers. Full article

Structural atrophy and metabolic dysfunction provide complementary biomarkers for Alzheimer’s disease (AD), and their joint modeling may support diagnostic assessment and cognitive score estimation. However, many multimodal methods rely on global fusion and insufficiently enhance cross-modal consistency before interaction, limiting the discriminative quality and clinical relevance of learned representations. We propose CGMF-Net, a cross-modal gated learning framework for joint AD classification and clinical score estimation using paired structural MRI (sMRI) and fluorodeoxyglucose PET (FDG-PET) data. CGMF-Net extracts multi-scale representations from both modalities, introduces a Cross-Modal Similarity Gate to strengthen consistent structural–metabolic responses before fusion, and employs bi-directional cross-attention to capture complementary interactions. The shared representation is optimized with classification supervision, MMSE-based auxiliary regression, and HSIC regularization to improve discriminability and reduce redundant coupling between directional representations. Experiments on ADNI demonstrate that CGMF-Net achieves the best overall classification performance among the compared methods, with 94.22% ACC and 97.74% AUC for AD vs. CN, and 86.67% ACC and 94.84% AUC for AD vs. MCI, while also showing favorable ADNI-2 to ADNI-1 generalization and competitive estimation of ADAS13, CDRSB, and MMSE. These results suggest that cross-modal gated learning provides clinically relevant multimodal representations for AD diagnosis and cognitive score estimation. Full article

Cardiac computed tomography (CT) has evolved into a pivotal tool in precision cardiology, enabling comprehensive, non-invasive evaluation of coronary anatomy, plaque composition, vascular function, and inflammation. From calcium scoring to advanced physiological imaging, CT now integrates multiple layers of cardiovascular information within a unified diagnostic framework. Coronary artery calcium (CAC) quantification provides a robust, reproducible measure of atherosclerotic burden and refines risk estimation beyond traditional algorithms, particularly in asymptomatic individuals with an intermediate likelihood. Building upon this anatomical foundation, coronary CT angiography (CCTA) extends evaluation to the anatomical and morphological characterization of coronary artery disease (CAD), identifying both obstructive and non-obstructive plaques with high prognostic accuracy. The addition of CT-derived fractional flow reserve (FFR-CT) and stress perfusion CT (CTP) bridges anatomy and physiology, improving identification of flow-limiting stenoses and guiding revascularization decisions while reducing unnecessary invasive procedures. Beyond luminal assessment, CT-derived biomarkers such as the perivascular fat attenuation index (pFAI) have introduced a new dimension of vascular inflammation imaging, revealing residual risk even in patients without significant stenosis and suggesting novel pathways for individualized therapeutic targeting. Driven by advances in artificial intelligence and photon-counting detector technology, cardiac CT is transitioning from a purely diagnostic modality to an integrative platform for cardiovascular phenotyping. Taken as a whole, this integration of structural, functional, and biological data provides a genuinely holistic view of coronary health. In practical terms, it shifts clinical decision-making from population-based risk models toward precision-guided patient-specific strategies. Full article

Background/Objectives: Shift work is associated with circadian disruption, sleep disturbances, and psychological distress, all of which may influence both reflux-related symptoms and temporomandibular disorder (TMD)-related complaints. However, the relationships among reflux-related symptom burden, TMD severity, oral parafunctional behaviors, and psychological distress in healthcare professionals remain insufficiently understood. To evaluate the association between reflux-related symptom burden, assessed using the Gastroesophageal Reflux Disease Health-Related Quality of Life (GERD-HRQL) questionnaire, and TMD severity among healthcare professionals, and to investigate the potential roles of shift work, oral parafunctional behaviors, and psychological distress in this relationship. Methods: This cross-sectional observational study included healthcare professionals working at a tertiary hospital. Data were collected using validated questionnaires, including the GERD-HRQL, Fonseca Anamnestic Index, Oral Behavior Checklist (OBC), Depression Anxiety Stress Scale-21 (DASS-21), and Epworth Sleepiness Scale (ESS). Participants were categorized as shift workers (≥4 night shifts/month) and non-shift workers. Correlation and multivariable regression analyses were performed. Results: A total of 240 participants were included. Higher GERD-HRQL scores were positively correlated with TMD severity (r = 0.31, 95% CI: 0.19 to 0.42, p < 0.001), oral parafunctional behavior scores (r = 0.28, 95% CI: 0.16 to 0.39, p < 0.001), and DASS-21 stress scores (r = 0.35, 95% CI: 0.23 to 0.46, p < 0.001). Shift workers demonstrated significantly higher GERD-HRQL scores and TMD severity scores than non-shift workers, with small-to-moderate effect sizes. In multivariable analysis, higher TMD severity, OBC score, stress score, and shift-work exposure showed adjusted associations with higher GERD-HRQL scores. The model explained 32% of the variance in reflux-related symptom burden (R² = 0.32; adjusted R² = 0.29). Conclusions: Higher GERD-HRQL scores, reflecting reflux-related symptom burden rather than objectively confirmed GERD, showed weak to small-to-moderate associations with TMD severity, oral parafunctional behaviors, psychological distress, and shift-work exposure among healthcare professionals. These findings indicate co-occurrence of gastrointestinal, temporomandibular, behavioral, and psychosocial symptom domains within this occupational population. Longitudinal studies using objective diagnostic methods are required to clarify the directionality and clinical significance of these associations. Full article

Background: This study aims to assess associations between occurrence of delayed cerebral ischemia (DCI) in spontaneous subarachnoid hemorrhage (SAH) and possible dysfunction of autonomic cardiovascular modulation compared to transcranial Doppler ultrasound (TCD). Methods: In this prospective observational study, 53 patients with spontaneous SAH were enrolled, and 17 patients met DCI criteria, i.e., new cerebral infarction > 72 h after SAH onset on follow-up CT scans. Autonomic modulation as well as TCD-frequencies were monitored within 24 h after SAH onset and then daily until day 10. From 5 min time-series of R–R-interval (RRI) and blood-pressure (BP) recordings, parameters of sympathetic, parasympathetic and total autonomic cardiovascular modulation were calculated, including time- and frequency-domain parameters. Data were compared between patients with and without DCI. Further subgroup analyses were performed according to functional outcome after 3 to 6 months (i.e., favorable outcome, modified Rankin Scale (mRS) ≤ 3 vs. unfavorable outcome, mRS > 3) regardless of DCI. Results: RRI and BP values as well as TCD frequencies did not differ between patients with and without DCI. Compared to No DCI patients, the cohort of DCI patients had significantly lower values of sympathetic modulation (RRI-LF powers, SBP-LF powers) on days 5 and 9 after SAH, significantly lower values of total autonomic modulation (RRI-SD, RRI-CV, RRI-total powers) and insignificantly lower values of parasympathetic modulation (RMSSDs, RRI-HF powers) on day 5 after SAH. Parameters of sympathetic, parasympathetic, and total autonomic modulation did not differ significantly between patients with favorable and unfavorable outcomes, but showed slightly lower values in the unfavorable outcome group. Yet, additionally calculated value of normalized RRI-LF and normalized RRI-HF powers, as well as LF/HF ratios were significantly different in the unfavorable outcome cohort. Conclusions: Not only within the acute phase, but also during the first days after disease onset, spontaneous SAH induces a decrease in sympathetic, parasympathetic and total autonomic cardiovascular modulation. In contrast to standard diagnostic evaluation for detecting clinically relevant vasospasms—i.e., TCD—autonomic dysfunction was associated with development of DCI and poor clinical outcome. Thus, assessment of heart rate variability may predict augmented risk of cardiovascular complications and may represent a promising adjunctive marker within multimodal neuromonitoring in SAH patients. Full article

Mining of sulfide-rich deposits enhances the oxidation of sulfide minerals, generating acid mine drainage (AMD) characterized by high sulphate and dissolved metal concentrations and the formation of secondary iron minerals (hematite, goethite, and jarosite). As these minerals display diagnostic features in the visible–near-infrared (VNIR) region, multispectral satellite data provide a cost-effective means of monitoring. Here, the performances of Sentinel-2 and the VNIR bands from WorldView-3 are assessed and compared for the mapping and discrimination of secondary iron minerals in Sierra Minera de Cartagena–La Unión (SE Spain). Both datasets were analyzed using a band ratio and a parabola fitting technique focused on reflectance maxima. Band ratio results were interpreted as broad spectral patterns rather than definitive mineral identifications. Mineral maps were validated by applying X-ray diffraction on 74 surface soil samples. Although both sensors were able to reproduce the main spatial patterns of iron mineral distribution, Sentinel-2 data better discriminated hematite, goethite, and jarosite, especially when using the parabola fitting approach, whereas WorldView-3 VNIR data distinguished mainly hematite from the combined goethite–jarosite group. The better performance of Sentinel-2 is attributed to its red-edge and near-infrared band configuration. These findings indicate that freely available Sentinel-2 imagery can support systematic monitoring of oxidation processes in mining environments and contribute to environmental risk assessment in degraded landscapes. Full article

Background/Objectives: Pulmonary hypertension (PH) is an increasingly recognized complication in patients with end-stage renal disease (ESRD) undergoing hemodialysis, particularly those utilizing arteriovenous fistulas (AVF) or grafts (AVG) for vascular access. The prevalence and clinical impact of PH in this population remain unclear due to methodological heterogeneity and variable diagnostic criteria. This systematic review and meta-analysis aimed to quantify the association between AVF/AVG use and PH prevalence in ESRD patients and to explore sources of heterogeneity. Methods: A systematic search of PubMed, Embase, Scopus, and Web of Science was conducted for studies published through 31 December 2024, without language or date restrictions. Eligible studies included adults (≥18 years) with ESRD on dialysis, comparing those with AVF/AVG access to non-AVF/AVG controls (e.g., tunneled dialysis catheters or peritoneal dialysis), and reporting PH prevalence or mean pulmonary artery pressures. Study quality was assessed using the Newcastle–Ottawa Scale, and risk of bias was evaluated. A random-effects meta-analysis calculated pooled odds ratios (OR) for PH prevalence, with heterogeneity assessed by I² and Cochran’s Q. Sensitivity analyses and tests for publication bias (Egger’s and Begg’s) were performed. Secondary analysis compared pooled mean pulmonary artery pressures between groups. Results: Eleven observational studies (1299 dialysis patients) met the inclusion criteria; ten studies (1224 patients) contributed to the quantitative meta-analysis after exclusion of one study with a zero-event control arm. Most studies were small, predominantly cross-sectional, and of moderate methodological quality. The pooled analysis showed a statistically significant association between AVF/AVG use and PH (OR 2.06, 95% CI: 1.69–2.52), with low statistical heterogeneity (I² = 0%). This estimate was sensitive to individual studies: in leave-one-out analysis the association lost statistical significance when the single most influential study was removed indicating that the pooled result is driven in part by a small number of studies rather than being uniformly robust. No statistical evidence of publication bias was detected. Five studies reported continuous pulmonary artery pressures, which were directionally higher in AVF/AVG patients but were not pooled because of extreme heterogeneity (I² = 99.4%). Conclusions: In this synthesis of observational data, AVF/AVG use was associated with higher odds of pulmonary hypertension than non-AVF/AVG access. Because all included studies were observational and the pooled estimate is sensitive to individual influential studies, these findings indicate a possible association rather than a causal effect and should be interpreted with caution. They support the rationale for prospective hemodynamic studies and for evaluating—rather than presuming the benefit of—PH monitoring and individualized access strategies in higher-risk dialysis patients. Full article

Background/Objectives: Dilated cardiomyopathy is a heterogeneous disorder with a substantial genetic contribution from a variety of pathogenic variants. Hereditary isolated DCM is often caused by variants in genes encoding sarcomere proteins, as well as proteins involved in desmosomes or other cardiac cell functions. Identifying genetic causes improves our understanding of DCM pathophysiology, facilitates prognostic assessment, and enables more personalized disease management. Methods: We retrospectively analyzed genetic data from adult patients with a clinical diagnosis of isolated DCM evaluated at a Lithuanian tertiary university hospital between 2019 and 2024. All patients were tested with a next-generation sequencing cardiovascular gene panel. Results: We gathered 169 patients and initially reached a 16.0% (n = 27) genetic testing diagnostic yield. We performed all genetic variant reanalyses with the most current classification guidelines, and we found an additional eight positive cases. Our final diagnostic yield was 20.7% (n = 35). TTN was the most frequently affected gene (n = 30), whereas variants in BAG3 (n = 2), DSP (n = 1), LMNA (n = 1), and FLNC (n = 1) were rare. In total, 15 variants were novel—not described in the literature or databases. We did not observe significant clinical differences between patients with pathogenic variants and those without pathogenic variants. We expected a different clinical course with variants in genes like BAG3 or LMNA, but there were only a few cases. Conclusions: Genetic testing remains an important tool for confirming complex DCM cases and allows earlier disease management for relatives at risk. Full article

Postural control is a multilevel adaptive system that stabilizes the body in space through dynamic sensory rebalancing and predictive neuromotor regulation. This review synthesizes current data on the neural mechanisms of balance maintenance, individual strategies for integrating multisensory information, and the role of immersive virtual reality as a controlled experimental and neuromodulation platform, synthesising evidence from 73 studies across neuroscience, cognitive psychology, and VR-based rehabilitation. Particular attention is paid to the cognitive style of “field dependence/independence”, which reflects stable preferences for the dominance of visual or vestibular-proprioceptive signals and consistently predicts the selection of postural strategies. We show that traditional averaged models ignore interpersonal variability, whereas immersive VR allows for the parametric induction of sensory conflicts, quantitative assessment of sensory dependence profiles, and facilitates the study of adaptive reorganization at the cortical, brainstem, and spinal levels. The review substantiates the need to move from standardized protocols to personalized approaches in diagnostics and neurorehabilitation that consider individual patterns of input rebalancing. The integration of behavioural metrics with neurophysiological markers in a VR environment provides a foundation for developing predictive balance models and targeted training interventions. However, the translational application of VR-based approaches requires careful consideration of methodological limitations, including cybersickness, hardware latency, and ecological validity, to ensure robust and generalisable outcomes. Full article

This article examines the Integrevise^TM platform through a repeated cross-sectional, multi-cycle pilot case study of viva-based verification in AI-mediated assessment environments. Integrevise^TM pairs a submitted written artifact with a short adaptive viva in which students explain their work, reasoning, and application in their own words. Rather than functioning as an AI detector or automated grading system, the platform operates as a diagnostic assessment layer intended to surface comprehension, authorship confidence, and disengagement risk before final grades become the only available signal. The pilot was conducted across Fall 2025 and Spring 2026 at a private liberal arts college in the Midwest; these phases involved different student groups and are therefore treated as iterative implementation cycles rather than a longitudinal cohort. Results should be interpreted as preliminary pilot evidence. In Spring 2026, 52 vivas were completed, but formal student survey data were limited to seven respondents and showed mixed perceptions: only 14.3% agreed that the oral assessment helped them think more deeply about the assignment, whereas 57.1% disagreed or strongly disagreed. Platform feedback was also incomplete, with 20 of 52 vivas (38.5%) producing no student feedback record. Qualitative feedback, tutor observations, and implementation notes nevertheless suggest that viva-based verification may help identify some comprehension gaps and implementation barriers that written artifacts alone may not reveal. The findings, therefore, support continued investigation of Integrevise^TM as a process-rich assessment intervention, but not broad claims of efficacy or scalability without larger, more systematic validation. Full article

Background: The financial burden of diagnostic testing for obstructive sleep apnea (OSA) represents a substantial barrier to treatment initiation, with cost-related access disparities disproportionately affecting the low-income and underinsured population. Home sleep testing (HST) offers a cost-effective diagnostic alternative, yet economic patterns across provider types remain unclear. This study assessed whether board-certified sleep medicine provider (BCSMP) status is associated with differences in provider-billed HST charges and evaluated how organizational and payment contexts influence these charges. Methods: A retrospective cross-sectional analysis was conducted using 2019 data from Optum’s de-identified Clinformatics^® Data Mart Database (N = 61,531 adult HST claims). The main exposure was provider status (BCSMP vs. non-BCSMP). The outcome was total provider-requested charge per HST procedure. Generalized Linear Models with a gamma distribution estimated adjusted charge differences, controlling for organizational context, place of service, and payer type. Results: BCSMP encounters had significantly lower adjusted mean HST charges than non-BCSMPs (mean difference: −$78.04; 95% CI: −$89.06 to −$67.02; p < 0.001). Individual practitioners charged $168.48 less than hospital-affiliated providers, while group practices and other facilities charged more (all p < 0.001). Fee-for-service arrangements were associated with lower charges than commercial and Medicare Advantage plans (p < 0.001). Conclusions: Board-certified sleep medicine providers and individual practice settings were associated with lower billed charges for home sleep testing; however, these findings do not necessarily reflect actual cost reduction. To translate these baseline charge differences into equitable clinical protocols and healthcare policies, future research must analyze negotiated reimbursement rates, billing structures, and practice environments to determine how these cost parameters impact the overall cost of an OSA diagnosis. Full article

Clinical prediction rules often suffer from a translation gap, balancing high-dimensional statistical accuracy against practical bedside interpretability. This study presents the Surgical Complication Risk Index (S-CRI), an explainable, data-decoupled risk-stratification framework designed to predict post-operative complications using multi-center electronic health registry records (N = 19,965). To ensure strict validation integrity, data partitioning (70% development, n = 13,975; 30% independent holdout testing, n = 5990) was executed before any engineering or risk-tier group isolation. A parsimonious multivariate logistic regression model was fitted within the development cohort, utilizing five predictors: length of stay (LOS) accrued up to the morning of assessment, two institutional categorical groupings, and two historical entry-diagnosis empirical risk tiers. To bridge the translational gap, all fractional regression coefficients were scaled by the baseline anchor and rounded to the nearest whole integer, yielding a simple bedside scorecard where 1 point = 1 inpatient day. On the completely blinded independent holdout cohort, the whole-integer S-CRI demonstrated robust discriminative performance with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.8741 (95% CI: 0.864–0.884) and a Precision–Recall AUC of 0.5785. Setting a baseline operational threshold ≥ 0 yielded an accuracy of 88.18%, a specificity of 96.43%, and a sensitivity of 35.43%, while an optimized integer screening cutoff score of ≥−4 maximized screening capacity (sensitivity: 63.95%; specificity: 91.68%). By enforcing strict temporal landmark constraints to eliminate reverse causality and removing all out-of-sample data leakage, the S-CRI provides an objective, transparent, and interpretable clinical decision support mechanism for early inpatient risk stratification, designed as a supplementary clinical decision-support aid, rather than as a definitive diagnostic replacement for independent clinical judgment. Full article

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency. Full article

Introduction: Prostate cancer (PC) is a highly prevalent malignant tumour associated with significant morbidity and mortality. While physical activity has been linked to a lower risk of PC and exercise has been shown to reduce mortality, the evidence for the association between physical activity and mortality is limited. Objective: This study aimed to assess the association between physical activity and mortality risk in men with or at risk of PC. Methods: A systematic search was conducted in Medline, Scopus and Web of Science from inception until April 2026. Observational studies analysing physical activity and all-cause and PC-specific mortality were included. The data were synthesised and interpreted using a synthesis without meta-analysis (SWiM) approach. The quality of the studies was assessed using the NHLBI tool. The certainty of the evidence was assessed using the GRADE framework. Results: Fifteen observational studies were included. The hazard ratio (HR) was the predominant effect measure. Physical activity was associated with a reduction in all-cause mortality (HRs: 0.40–0.88; highest versus lowest categories), and a dose–response gradient was observed within two cohorts. Associations with PC-specific mortality were less consistent, with significant inverse findings concentrated in post-diagnosis assessments. The quality of the studies was generally poor, and the certainty of the evidence was very low for both outcomes. Conclusions: Physical activity was associated with lower all-cause mortality risk in men with or at risk of PC, and the most consistent inverse estimates were observed in post-diagnostic assessments. These findings are observational and should not be interpreted as a clinical recommendation. A dose–response pattern was noted within individual studies, although the certainty of evidence was very low for this outcome. Additionally, evidence for PC-specific mortality was inconsistent and of very low certainty. Prospective studies with standardised, objective measures of physical activity are required. Full article

Liver disease represents a major global health burden, and early diagnosis is essential for reducing mortality. Machine learning (ML) approaches offer non-invasive alternatives to conventional diagnostics, yet existing studies on liver disease prediction often lack systematic feature selection, apply resampling before data splitting (introducing leakage), and report results from single train-test splits without statistical testing. This study proposes a Hybrid Feature Selection (HFS) framework combining Pearson-correlation-based redundancy elimination with a weighted Information Gain–Gain Ratio scoring function, integrated with SMOTE within a leakage-free pipeline. The framework is evaluated on two benchmarks—the Indian Liver Patient Dataset (ILPD, n = 583) and the BUPA Liver Disorders Dataset (n = 345)—across ten classifiers and ten independent train-test splits, with significance assessed via paired Wilcoxon signed-rank tests. On ILPD, the HFS + SMOTE pipeline produced statistically significant ROC-AUC improvements (p < 0.05) in five of ten classifiers and resolved majority-class collapse, raising mean Specificity from 0.00–0.33 to 0.61–0.92. A 2 × 2 ablation study confirmed that HFS and SMOTE contribute independently, with SMOTE driving the Specificity transformation and HFS reducing feature-space noise. Sensitivity analyses demonstrated robustness to the weighting parameter w and confirmed k = 6 as the optimal feature count. Replication on BUPA—which exhibits near-perfect class balance and no feature redundancy—produced a principled null result, confirming that the pipeline’s effectiveness is mechanistically linked to dataset characteristics. The HFS algorithm consistently identified four clinically meaningful core features (AST, ALT, Total Bilirubin, Age) across all runs, validated by SHAP and Permutation Importance stability analysis. Full article