Search Results (5,890)

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

For hydrological parameterization in high-Andean catchments, it is necessary to understand whether near-surface hydro-structural soil properties can provide a surrogate signal of particle-size composition when direct texture information is sparse. This study evaluated the extent to which sand, silt, and clay fractions can be approximated from organic matter ($\mathrm{OM}$), bulk density (${\rho }_b$), and saturated hydraulic conductivity ($K_{\mathrm{sat}}$) in the Zamora Huayco (ZH) and Irquis catchments, southern Ecuador. A harmonized dataset ($n=44$) was analyzed through exploratory statistics, compositional assessment, correlation analysis, PCA, fraction-wise regression, $\mathrm{ILR}$-based modeling, AIC/BIC term reduction, sensitivity analysis excluding $\mathrm{OM}$, nested $\mathrm{LOOCV}$, and bootstrap-based uncertainty intervals. Among LULC classes, samples classified as paramo occupied a distinct high-Andean hydro-edaphic domain, characterized by a differentiated relationship between soil physical properties and hydrological behavior. PCA showed that the dominant covariance structure involved $\mathrm{OM}$, ${\rho }_b$, $K_{\mathrm{sat}}$, and the redistribution between sand and silt. The BIC-reduced $\mathrm{ILR}$ model provided the most balanced formulation, with positive nested $\mathrm{LOOCV}$ performance for sand, silt, and clay ($R_{\mathrm{LOOCV}}^2=0.147$, $0.704$, and $0.124$, respectively) and exact $100\%$ compositional closure after inverse transformation. Silt was the most stable predicted fraction, whereas sand and clay retained larger residual uncertainty, stronger tail departures, and partial compression of the observed variability. The proposed equations provide local hydro-pedotransfer support, although their predictive signal remains dependent on further refinement, uncertainty assessment, and external validation before regional application. Full article

Background/Objectives: This preliminary, single-centre study evaluated infectious diseases consultation (IDC) patterns as indicators of hospital workflow and care complexity, aiming to characterise routinely available variables that may inform future organisational research and EHR-based clinical decision support development. Methods: In this retrospective study, 39,275 IDC requests from 16,430 patients were analysed using hospital information management system records. Paediatric patients and specialised immunosuppressed patient units were excluded. Request volumes, diagnostic categories, consultation purposes, and factors associated with in-hospital mortality were evaluated. Multivariable logistic regression models were constructed separately for two hospital blocks. Results: A total of 39,275 IDC records for 16,430 unique patients were reviewed. Mean consultation access time was 82.2 ± 64.3 min. Requests originated from surgical clinics (43.8%), followed by intensive care units (37.6%) and medical/internal clinics (18.6%). Pneumonia was the most common indication (30.5%), followed by unspecified infections (25.4%) and skin/soft tissue infections (17.2%). Consultation objectives included treatment, diagnostic assessment, and clinical guidance as non-mutually exclusive components. Significant block-level differences were observed in consultation timing, ICU-related consultation, diagnostic profiles, consultation purposes, and mortality. Age and ICU-related consultation were independently associated with mortality in both blocks, whereas consultation access time and COVID-19 diagnosis showed block-specific associations. Conclusions: IDC patterns may reflect not only diagnostic demand but also case severity, ICU-related care, consultation timing, and hospital location. As a preliminary single-centre study, these hypothesis-generating findings highlight the importance of integrating clinical, organisational, and contextual variables in future prospective, multi-centre studies aimed at developing EHR-based decision-support models. External validation, incorporation of comorbidity indices and microbiological data, and assessment of explainability are required before clinical implementation. Full article

Background/Objectives: Chronic non-specific low back pain (CNLBP) is a leading cause of disability worldwide. Although several randomized trials have evaluated treatment effectiveness, less attention has been given to the responsiveness of outcome measures used to assess clinical change. This study aimed to evaluate the internal and external responsiveness of commonly used outcome measures in individuals with CNLBP. Methods: This study is a secondary analysis of a randomized controlled trial. Participants were analyzed as active and placebo groups and assessed at baseline, post-intervention, and follow-up. Internal responsiveness was evaluated using standardized mean differences (SMD) and standardized response means (SRM). External responsiveness was assessed using anchor-based approaches, including correlations with the Global Rating of Change Scale (GRCS) and receiver operating characteristic (ROC) curve analysis. Results: Outcome measures demonstrated moderate to high internal responsiveness, with large effect sizes observed for pain intensity (NRS) and quality of life (EQ-5D-3L). However, external responsiveness was limited, with all instruments presenting area under the curve (AUC) values below 0.70. The Bournemouth Questionnaire showed the highest discriminative performance among the instruments. Conclusions: The evaluated instruments were sensitive to detecting change at the group level but showed limited ability to discriminate clinically meaningful improvement at the individual level. These findings support the use of combined outcome measures to improve clinical interpretation and decision-making in CNLBP. Full article

Background: A subgroup of adolescent and young adult patients (AYAs; 18 to 39 years at diagnosis) face an uncertain or poor cancer prognosis (UPCP). Previous qualitative research identified dual coping pathways in this population: engagement in life versus the reality of premature death. This study examines whether similar psychosocial profiles can be identified through quantitative data, aiming to differentiate patient experiences and identify characteristic features of each cluster. Additionally, this study examines the association between cluster membership and social support needs to understand psychosocial disparities. Methods: Eligible participants completed questionnaires assessing physical, psychosocial, and existential outcomes related to their disease and prognosis. An ensemble clustering approach was applied, including evaluation of clustering tendency and multiple algorithms, with stable clusters identified through majority voting. Associations with social support needs were analyzed using Fisher’s exact test. Results: Data from 155 AYAs with a UPCP were included. The mean age at diagnosis was 31.2 years, with glioma (34.8%) and breast cancer (17.4%) as the most common diagnoses. Two distinct clusters were identified: one (22%) characterized by poorer functional outcomes and fewer protective factors (e.g., hope, meaning in life), and another cluster (78%) with better functioning and less frequent needs for social support (p < 0.00043). Conclusions: Findings revealed divergent psychosocial profiles within the AYA-UPCP population, highlighting the importance of early identification of vulnerable subgroups. Strengthening protective factors may enhance resilience and reduce unmet support needs. Validation in larger, external datasets is needed to confirm these pathways and guide tailored supportive care strategies. Full article

The evaluation of wearable robotic systems remains a challenge, particularly in real-world environments where laboratory-based methods are impractical. Existing approaches rely on external instrumentation, such as surface electromyography (sEMG) or motion capture, which are difficult to deploy continuously and do not directly measure key internal metrics such as joint loading or spinal forces. This work introduces a new paradigm for exoskeleton evaluation in which biomechanical assessment is embedded directly within the device’s sensing and computational architecture. We present the ExoMetrix system, a platform that integrates onboard sensing, real-time data acquisition, cloud-based processing, and user-facing analytics into a unified workflow for continuous evaluation of human–exoskeleton interaction. Sensor data from the device are streamed and processed using physics-based models. The resulting outputs are translated into estimates of internal biomechanical quantities, including joint torques, spinal compression and shear forces, and muscle loading. By enabling real-time feedback and longitudinal monitoring without external instrumentation, this approach transforms evaluation from an external, episodic process into an embedded and continuous capability, supporting safer and more scalable deployment of exoskeleton technologies. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor control, balance, and gait impairments that significantly elevate fall risk. Traditional gait analysis focuses on spatiotemporal parameters, while gait variability, asymmetry, and balance measures offer more sensitive indicators of PD-related motor deficits. Machine learning studies using wearable gait data frequently report high classification accuracy but lack biomechanical interpretability and methodological rigor. Using the PhysioNet Gait in Parkinson’s Disease database, 93 individuals with PD and 72 healthy controls were analyzed during level-ground walking. Key biomechanical differences were identified: stride time coefficient of variation was significantly higher in PD bilaterally (left p = 0.001; right p = 0.003); swing-phase time was significantly reduced in both limbs (left p = 0.003; right p = 0.001); anterior–posterior center of pressure (COP) variability was significantly lower in PD for both limbs (p < 0.001); and COP path symmetry index was the most prominent asymmetry marker, significantly elevated in PD relative to controls (p = 0.003). A machine-learning analysis pipeline identified HistGradientBoosting as the best-performing classifier (AUC = 0.992; accuracy = 97.6%), but leave-one-study-out evaluation exposed substantial cross-protocol heterogeneity (AUC: 0.500–1.000), indicating that the model relied partly on dataset-specific patterns and may not generalize to independent acquisition protocols. Shapley Additive Explanations (SHAP) analysis showed classification was driven by a multimodal combination of clinical severity measures and biomechanical gait features rather than wearable metrics alone. A pre-specified gait-only sensitivity analysis that excluded clinical severity variables (UPDRS, UPDRSM, Hoehn and Yahr) confirmed that biomechanical features alone retained moderate, but substantially reduced, discriminative ability (gait-only holdout AUC = 0.844), supporting the interpretation that the headline performance reflects multimodal clinical separation rather than a stand-alone wearable-gait biomarker. These findings indicate that Parkinsonian gait impairment is characterized by timing instability and constrained forward COP progression. The combination of biomechanical analysis with interpretable predictive modeling represents a structured analysis pipeline for gait-based PD assessment; however, external validation in independent cohorts and prospective testing across acquisition protocols are required before such a pipeline can be deployed as a clinically generalizable digital biomarker. Full article

Background: Spinal cord stimulation (SCS) is an established intervention for refractory chronic neuropathic pain, but response to trial stimulation and long-term benefit remain heterogeneous. Clinicians need practical tools to document patient-selection domains discussed in the neuromodulation literature without overstating the precision of currently available evidence. Methods: We conducted a narrative synthesis of randomized trials, cohort studies, registry analyses, systematic reviews, and consensus recommendations addressing SCS outcomes and candidate selection. The objective was not to derive or validate a multivariable prediction model, but to construct a transparent, bedside-oriented framework organizing clinically accessible domains relevant to SCS trial candidacy. Results: Six domains were incorporated into the proposed SCS Trial Success Score (STSS): primary indication, psychological status, smoking status, opioid burden, body mass index, and pain duration. The resulting 0 to 12 point score is presented as an evidence-informed clinical profile rather than a validated prognostic instrument. Three descriptive categories are proposed: more favorable profile, optimization-sensitive profile, and less favorable profile. These categories are intended to guide documentation, shared decision-making, and optimization of modifiable factors, not to determine eligibility automatically. Conclusions: Pending prospective validation, checklist-mode use is the preferred interim application of the STSS. The framework may support structured pre-trial assessment, identification of modifiable factors, and shared decision-making. It should not be used as a standalone numerical decision rule, to deny access to neuromodulation, or to replace multidisciplinary judgment. Prospective derivation, calibration, external validation, and decision-curve analysis are required before the STSS can be considered a clinical prediction rule. Full article

Shear-wave velocity (Vs) estimation from the Standard Penetration Test (SPT) can support preliminary site characterization when direct geophysical data are limited, but empirical correlations require validation schemes that reflect transferability between sites. This study evaluates Vs prediction using an interval-paired dataset derived from geotechnical investigations of school foundations in Piura, Peru. Its novelty lies in comparing the raw SPT blow count (NSPT) and the overburden- and energy-corrected SPT blow count ((N1)60) on the same strict common sample, using grouped cross-validation by school, thereby emphasizing transferability across sites rather than only internal fit. Five predictive scenarios were tested, from penetration-only formulations to geotechnically enriched specifications. The lowest grouped out-of-fold error among the evaluated models was obtained by a generalized power baseline using (N1)60 and the integral geotechnical predictor set, yielding root mean square error (RMSE) = 80.48 m/s, mean absolute error (MAE) = 60.15 m/s, and coefficient of determination (R2) = 0.338. This moderate R2 indicates limited standalone predictive capacity under transfer to unseen schools; therefore, the model is interpreted as a preliminary transfer-oriented correlation rather than as a substitute for direct Vs measurements or as an independent design equation. In the complementary full-data analysis, the strongest descriptive fit was obtained with Hist Gradient Boosting, whereas the strongest explicit equation corresponded to the log-semi baseline. Overall, the findings show that externally validated transferability, descriptive full-data fit, and equation-based interpretability represent different analytical roles in Vs-SPT modeling. Full article

With intensifying measures related to investor and policy requirements, corporate governance and sectoral environmental performance became a focal point for sustainability disclosure, especially in energy-intensive industries with high environmental externalities. This study evaluates whether corporate environmental governance practices in key sectors correspond to their pollution intensity and economic output, analysing a panel dataset across EU member states, for the 2000–2021 period. The empirical methodology includes ordinary least squares (OLS), fixed- and random-effects models, and dynamic system generalised method of moments (GMM) panel estimation to account for sectoral heterogeneity. Results prove that sectoral value added is an influential factor of greenhouse gas emissions, with carbon dioxide exhibiting the highest elasticity to economic activity, followed by methane emissions, and nitrous oxide displaying cross-country variations due to structural and regulatory differences. While services and manufacturing sectors partially decouple via cleaner technologies, overall growth positively correlates with emissions, and renewable energy offers limited mitigation due to scale and integration challenges. Conclusions emphasise robust governance frameworks in high-value energy sectors to meet EU climate-neutrality goals, as stronger environmental accountability attracts capital and supports sustainable development, underscoring the needs for targeted decarbonisation, regulatory coordination, and accelerated technological innovation within persistent industry disparities. Full article

Single-molecular white circularly polarized luminescence emitters show promise for use in chiral displays and solid-state lighting, but their design remains challenging because broadband emission, exciton utilization, color balance, and chiroptical activity must be integrated within one molecule. Herein, we report a chiral single-molecular white emitter, DCz-PTZ, constructed through a π-interrupted strategy by combining a rigid spiro framework, an oxygen-bridged carbazole/cyanobenzene segment, and a phenothiazine donor. The interrupted conjugation suppresses excessive charge-transfer (CT) domination and enables dual emissive channels, including short-wavelength locally excited (LE) emission and long-wavelength CT emission. DCz-PTZ exhibits near-ideal white emission in dilute toluene solution with CIE coordinates of (0.33, 0.33), and maintains balanced dual emission in 5 wt% doped films with CIE coordinates of (0.32, 0.34). Photophysical studies support the assignment of the yellow emission to a thermally activated delayed fluorescence (TADF)-active CT state. The enantiomers show mirror-image circularly polarized signals with |g_lum| up to 2.9 × 10⁻³. Optimized white organic light-emitting diodes (WOLEDs) achieve color rendering index (CRI) up to 92 and a maximum external quantum efficiency (EQE_max) of 1.3%. This work demonstrates a π-interrupted molecular strategy for integrating dual emission, TADF exciton utilization, and circularly polarized electroluminescence (CPEL) in a single chiral emitter. Full article

Background: The diagnostic value of surface electromyography (sEMG) for identifying muscles affected by myofascial trigger points (TrPs) remains controversial. However, advances in pain neurophysiology and discussions regarding TrPs within the International Classification of Diseases (ICD-11) have renewed interest in objective diagnostic approaches. Objective: To synthesize current evidence on the diagnostic utility of sEMG for detecting TrP-related muscle alterations across different electromyographic signal analysis domains. Methods: A scoping review was conducted following JBI guidance and PRISMA-ScR guidelines. PubMed, Scopus, Web of Science, CINAHL and Cochrane were searched for studies involving adults with symptomatic or asymptomatic TrPs, myofascial pain syndrome, or TrP-related referred pain. Fifteen studies met the inclusion criteria. Analyses included amplitude-, frequency-, time–frequency-, and spatial-domain sEMG parameters. Results: Muscles affected by TrPs showed increased resting electromyographic activity and reduced activation during maximal voluntary contraction in several studies. Frequency domain analyses indicated changes in median frequency and muscle fatigue index, whereas time–frequency analyses suggested redistribution of sEMG signal energy toward lower-frequency components or altered spectral power during experimentally provoked referred pain. Spatial analyses revealed altered activation patterns, although these findings did not consistently correspond with TrP anatomical locations. Overall, the limited number of studies assessing diagnostic sensitivity and specificity prevents firm conclusions. Conclusions: sEMG may be useful as a non-invasive complementary tool for functional assessment and monitoring of TrP-related muscle dysfunction. However, current evidence does not support its use as a standalone diagnostic method. Time–frequency, machine learning-supported and spatial analyses appear promising for future clinical research, but standardized protocols and external validation are required before clinical diagnostic criteria can be proposed. Full article

The study examines the relationship between Corporate Social Responsibility (CSR) adoption and firms’ future CSR investment, with a particular focus on the mechanisms and conditions that shape this relationship, drawing on the business case perspective and the resource-based view. A quantitative research design was employed using survey data collected from 568 business executives in Greece. Structural Equation Modeling (SEM) was applied to test the proposed relationships. The findings indicate that CSR adoption has a significant positive impact on future CSR investment, confirming that CSR engagement evolves into a sustained strategic commitment. Perceived benefits are found to play significant mediating roles, suggesting that firms increase future CSR investment when they recognize the value generated by CSR. In contrast, institutional barriers negatively moderate this relationship, weakening the effect of CSR adoption. The study demonstrates that the continuation of CSR investment is driven by internal reinforcement mechanisms and external conditions rather than purely by financial constraints. It offers empirical evidence that CSR adoption initiates a self-reinforcing process supported by perceived value decisions. The findings provide practical insights, emphasizing the importance of strengthening institutional frameworks and enhancing the perceived benefits of CSR to foster long-term investment in sustainable business practices. Full article

Background/Objectives: Cipepofol is a novel intravenous anesthetic whose pharmacokinetics (PK) may vary with dosing regimens, sampling sites, and physiological differences across populations. However, clinical PK data remain fragmented across study settings and are limited for special populations and individualized perioperative use, highlighting the need for a mechanistic modeling framework. This study aimed to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for cipepofol across diverse populations. Methods: Clinical data from nine studies were included, comprising 371 subjects and 3521 plasma concentration measurements. The model was established in healthy adults using HSK3486-101, qualified using healthy-adult data from HSK3486-111 and anesthesia induction datasets, and extrapolated to hepatic impairment, renal impairment, and elderly populations using pathophysiology-informed adjustments. Individualized external validation was further performed in adult and pediatric surgical patients using actual clinical dosing histories. Model performance was evaluated using concentration–time profiles, goodness-of-fit plots, fold error, and geometric mean fold error (GMFE) for C_max and AUC_0-t. Results: The model adequately described both arterial and venous plasma concentration–time profiles across the establishment, qualification, extrapolation, and external validation datasets. Most predicted concentrations were within two-fold of the observed values, and the overall GMFE values were 1.22 for C_max and 1.21 for AUC_0-t. Simulated exposure differences in hepatic impairment, renal impairment, and elderly subjects were generally limited, suggesting no clinically meaningful PK changes from a PK exposure perspective in these populations. The model also reproduced arterial–venous concentration differences and supported the major contributions of UGT1A9 and CYP2B6 to cipepofol clearance. Conclusions: This PBPK model provides a mechanistic framework for characterizing cipepofol disposition and may inform future model-informed dosing studies. Full article

Conventional span-morphing wings are often constrained by structural complexity, heavy weight, and discontinuous aerodynamic surface. Although flexible honeycomb and lattice structures offer lightweight solutions, they usually require external loads to maintain the deformed configuration and often exhibit limited stability under large deformation. In this study, a span-morphing wing section based on multistable honeycomb structures is proposed. The multistable honeycomb acts as the core deformation–load-bearing module, enabling multistage reversible spanwise reconfiguration through the bistable transition of cosine curved beams and the support of honeycomb structures. An equivalent nonlinear force–displacement model is derived to describe the structural response. Finite element analysis and fluid–structure interaction analysis are conducted to evaluate its mechanical and aerodynamic performance, while prototype fabrication and bidirectional morphing experiments are performed to demonstrate its functional feasibility. The results show that the proposed wing section achieves prescribed multistage state transitions, effectively regulates lift through span variation, and maintains good structural strength under typical aerodynamic loads. These findings demonstrate the potential of multistable honeycomb structures for lightweight and stable span-morphing wing design. Full article