Search Results (7,225)

Cardiopulmonary Exercise Testing (CPET) is the gold standard for the functional assessment in patients with heart failure (HF), providing objective parameters that reflect the integrated response of the cardiovascular, respiratory, and muscular systems, in addition several CPET-derived variables have shown independent prognostic value in patients with both reduced (HFrEF) and preserved ejection fraction (HFpEF) HF. This review aims to critically analyze the main CPET prognostic variables in heart failure, highlighting their underlying pathophysiological mechanisms, their predictive capacity for mortality and hospitalizations, and their integration into clinical decision-making models. Parameters such as peak oxygen uptake (VO₂), minute ventilation/carbon dioxide production (VE/VCO₂) slope, periodic breathing (or exercise oscillatory ventilation—EOV), anaerobic threshold (AT), oxygen pulse, and VO₂/work slope provide complementary insights into clinical risk; moreover, the combination of multiple CPET variables allows for more accurate risk stratification compared to the isolated use of each parameter. Multiparametric prognostic models such as the Metabolic Exercise Cardiac Kidney Index (MECKI) score, the Seattle Heart Failure Model, and the Heart Failure Survival Score (HFSS) incorporate these variables alongside clinical and laboratory data to guide advanced management and therapeutic decisions, including heart transplantation or left ventricular assistant device (LVAD) implantation. For these reasons, CPET-derived variables are essential prognostic tools in heart failure. Beyond improving risk stratification, their integration into multiparametric models supports a more personalized therapeutic approach, including tailored pharmacological management. Full article

Woven composite structures are inherently influenced by uncertainties across multiple scales, ranging from constituent material properties to mesoscale geometric variations. These uncertainties give rise to both spatial autocorrelation and cross-correlation among material parameters, resulting in stochastic strength performance and damage morphology at the macroscopic structural level. This study established a comprehensive multiscale uncertainty quantification framework to systematically propagate uncertainties from the microscale to the macroscale. A novel dual-correlation sampling approach, based on multivariate random field (MRF) theory, was proposed to simultaneously capture spatial autocorrelation and cross-correlation with clear physical interpretability. This method enabled a realistic representation of both inter-specimen variability and intra-specimen heterogeneity of material properties. Experimental validation via in-plane tensile tests demonstrated that the proposed approach accurately predicts not only probabilistic mechanical responses but also discrete damage morphology in woven composite structures. In contrast, traditional independent sampling methods exhibited inherent limitations in representing spatially distributed correlations of material properties, leading to inaccurate predictions of stochastic structural behavior. The findings offered valuable insights into structural reliability assessment and risk management in engineering applications. Full article

Continuous Test-Time Adaptation aims to adapt a source model to continuously and dynamically changing target domains. However, previous studies focus on adapting to each target domain independently, treating them as isolated, while ignoring the interplay of interference and promotion between domains, which limits the model’s sustained capability, often causing it to become trapped in local optima. This study highlights this critical issue and identifies two key factors that limit the model’s sustained capability: (1) The update of parameters lacks constraints, where domain-sensitive parameters capture domain-specific knowledge, leading to unstable channel representations and interference from old domain knowledge and hindering the learning of domain-invariant knowledge. (2) The decision boundary lacks constraints, and distribution shifts, which carry significant domain-specific knowledge, cause features to become dispersed and prone to clustering near the decision boundary. This is particularly problematic during the early stages of domain shifts, where features are more likely to cross the boundary. To tackle the two challenges, we propose a Dual Constraints method: First, we constrain updates to domain-sensitive parameters by minimizing the representation changes in domain-sensitive channels, alleviating the interference among domain-specific knowledge and promoting the learning of domain-invariant knowledge. Second, we introduce a constrained virtual decision boundary, which forces features to move away from the original boundary, and with a virtual margin to prevent features from crossing the decision boundary due to domain-specific knowledge interference caused by domain shifts. Extensive benchmark experiments show our framework outperforms competing methods. Full article

This study models different hybrid systems based on renewable energies that can be supported by diesel generators to meet the energy needs of isolated homes in the Canary Islands. The research will cover the energy requirements of a residential house, including the production of fresh water using a reverse osmosis desalination plant. The system is designed to operate independently of the electrical grid. The HOMER software package was used to model and optimize the hybrid systems. The model was fed with data on the electrical demands of residential homes (including the consumption by the small reverse osmosis desalination plant) as well as the technical specifications of the various devices and renewable energy sources, such as solar radiation and wind speed potentials. The software considers various configurations to optimize hybrid systems, selecting the most suitable one based on the available renewable energy sources at the selected location. The data used in the research were collected on the eastern islands of the Canary Islands (Gran Canaria, Lanzarote and Fuerteventura). Based on the system input parameters, the simulation and optimization performed in HOMER, taking into account the lowest “Levelized Cost of Energy”, it can be concluded that the preferred hybrid renewable energy system for this region is a small wind turbine with a nominal power of 1.9 kW, eight batteries, and a small diesel generator with a nominal power of 1.0 kW. The knowledge from this research could be applied to other geographical areas of the world that have similar conditions, namely a shortage of water and plentiful renewable energy sources. Full article

In remote sensing imagery, objects smaller than $32\times 32$ pixels suffer from three persistent challenges that existing detectors inadequately resolve: (1) their weak signal is easily submerged in background clutter, causing high miss rates; (2) the scarcity of valid pixels yields few geometric or textural cues, hindering discriminative feature extraction; and (3) successive down-sampling irreversibly discards high-frequency details, while multi-scale pyramids still fail to compensate. To counteract these issues, we propose MS-YOLOv11, an enhanced YOLOv11 variant that integrates “frequency-domain detail preservation, lightweight receptive-field expansion, and adaptive cross-scale fusion.” Specifically, a 2D Haar wavelet first decomposes the image into multiple frequency sub-bands to explicitly isolate and retain high-frequency edges and textures while suppressing noise. Each sub-band is then processed independently by small-kernel depthwise convolutions that enlarge the receptive field without over-smoothing. Finally, the Mix Structure Block (MSB) employs the MSPLCK module to perform densely sampled multi-scale atrous convolutions for rich context of diminutive objects, followed by the EPA module that adaptively fuses and re-weights features via residual connections to suppress background interference. Extensive experiments on DOTA and DIOR demonstrate that MS-YOLOv11 surpasses the baseline in mAP@50, mAP@95, parameter efficiency, and inference speed, validating its targeted efficacy for small-object detection. Full article

Background/Objectives: Intravesical Bacillus Calmette–Guérin (BCG) is the standard adjuvant treatment for high-risk non-muscle-invasive bladder cancer (NMIBC), but treatment-related urinary toxicity may compromise quality of life (QoL) and adherence. The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has been linked to oncologic outcomes in bladder cancer, but its association with urinary symptom burden during BCG therapy remains unclear. We aimed to assess whether baseline NLR is associated with early deterioration in urinary symptoms and urinary QoL following BCG induction. Methods: This prospective study included male patients with NMIBC treated with intravesical BCG. Baseline demographics, comorbidities, laboratory parameters, and urinary symptoms were recorded. Patients were stratified into two groups according to baseline NLR (<3 vs. ≥3). Urinary outcomes were assessed at baseline and 8 weeks using the International Prostate Symptom Score (IPSS) and the IPSS-related QoL item. Univariable and multivariable linear regression analyses were performed. Results: A total of 96 patients were analyzed. Median baseline NLR was 2.6 (IQR: 2.1–3.8). Patients with NLR ≥ 3 (n = 34) and NLR < 3 (n = 62) had comparable baseline characteristics and urinary scores. At 8 weeks, patients with NLR ≥ 3 experienced a greater worsening of urinary symptoms (median IPSS 24 vs. 21, p = 0.02; median change +5 vs. +2, p = 0.01) and QoL (median 5 vs. 4, p = 0.03). Univariable regression confirmed the association of NLR ≥ 3 with worse QoL (β = +0.74; p = 0.003) and higher IPSS (β = +2.20; p = 0.021). Modeled as a continuous variable, each one-unit increase in NLR corresponded to a +0.20 worsening in QoL (p = 0.008). In the multivariable analyses adjusted for baseline IPSS and concomitant CIS, NLR remained independently associated with QoL decline. Conclusions: Baseline NLR was independently associated with worsening urinary symptoms and QoL during BCG induction in NMIBC patients. NLR may represent a simple and accessible biomarker for early risk stratification during BCG induction, warranting validation in larger, longer-term prospective trials. Full article

(1) Background: Physical activity, altered metabolic parameters, and socio-economic status may affect electrocardiographic (ECG) parameters in children. However, a direct comparison of their effects on resting ECG has not yet been performed. (2) Methods: A total of 139 participants (60 male), aged 10–17 years, were recruited. Resting 1-minute ECG recordings and clinical and laboratory investigations were obtained, while socio-economic status and physical activity were assessed using a questionnaire. Associations between these factors and ECG parameters were analyzed using analysis of covariance (ANCOVA). (3) Results: Age, sex, metabolic syndrome, and physical activity significantly influenced the average RR interval (η² = 0.292, 0.070, 0.078, and 0.070, respectively). Similar effects were observed on the T_end–P interval. The PR, QRS, QTc, and T_peak–T_end intervals were moderately influenced by age (η² = 0.084, 0.056, 0.072, and 0.049, respectively). QTc was additionally affected by sex (η² = 0.060). None of the modifiable factors had any effect on depolarization or repolarization parameters. Socio-economic status had no significant effect on resting ECG. (4) Conclusions: Physical activity exerts similar effects on resting ECG in both sexes, while metabolic syndrome is an independent determinant of several ECG parameters. Further studies are warranted to clarify the clinical relevance of these findings. Full article

Background/Objectives: There is clear evidence for the higher prevalence of varicose veins (VVs) among women. In this regard, the research on sex differences affecting this condition is very important for sex-specific health care. We aimed to assess how male or female sex may contribute to the changes to gene expression profiles in the vein wall during varicose transformation. Methods: Paired varicose vein (VV) and non-varicose vein (NV) segments were harvested from patients with VVs after venous surgery. Processed RNAs from those samples were subjected to gene expression analysis by reverse transcription quantitative polymerase chain reaction (RT-qPCR) followed by further data analysis. Multiple linear regression (MLR) analysis was performed to identify and characterize relationships among multiple factors (relative mRNA levels of a gene in NV or VV or their ratio, as dependent variables) and sex (independent variable, used individually or in combination with other patient’s characteristics). For sex-specific gene regulation analysis, all potential binding sites for sex hormone receptors were identified in each gene’s regulatory region sequence. Results: Using the independent method and a replicative patient sample set, we validated our previous data on 23 genes’ differential expression in VVs and obtained insights on their sex-specific regulation. Sex (as an individual independent variable or in combination with other parameters—patient characteristics such as Age, BMI, CEAP class, Height, VVD manifestation and duration) was a moderate predictor (0.40 < R < 0.59; p (R) < 0.05) for the STK38L expression in VVs (with its higher mRNA level in NVs and VVs of women compared to men); sex was a strong predictor (0.6 < R < 0.79; p (R) < 0.05) for the TIMP1 expression in VVs (with its lower mRNA level in VVs of women compared to men); sex was a moderate predictor (0.40 < R < 0.59; p (R) < 0.05) for the EBF1 expression in NVs (with its lower mRNA level in NVs of women compared to men). Conclusions: Confirmed differential expression of the studied genes in VVs indicates their plausible participation in vein wall remodeling. Sex-specific expression in veins for the subset of those genes suggests their hormonal regulation as well as other mechanisms involved in VV pathogenesis. This work enriches our understanding of sex features for the development of VVs and may provide the foundation for future investigations and beneficial treatment options. Full article

Additive manufacturing involves numerous independent parameters, often leading to inconsistent print quality and necessitating costly trial-and-error approaches to optimize input variables. Machine learning offers a solution to this non-linear problem by predicting optimal printing parameters from a minimal set of experiments. Using Fused Deposition Modeling (FDM) as a case study, this work develops a machine learning-powered process to predict porosity defects. Specimens in two geometrical scales were 3D-printed and CT-scanned, yielding raw datasets of grayscale images. A machine learning image classifier was trained on the small-cube dataset (~2200 images) to distinguish exploitable images from defective ones, averaging over 97% accuracy and correctly classifying more than 90% of the large-cube exploitable images. The developed preprocessing scripts extracted porosity features from the exploitable images. A repeatability study analyzed three replicate specimens printed under identical conditions, and quantified the intrinsic process variability, showing an average porosity standard deviation of 0.47% and defining an uncertainty zone for quality control. A multi-layer perceptron (MLP) was independently trained on 1709 data points derived from the small-cube dataset and 3746 data points derived from the large-cube dataset. Its accuracy was 54.4% for the small cube and increased to 77.6% with the large-cube dataset, due to the larger sample size. A rigorous grouped k-fold cross-validation protocol, relying on splitting data per cube, strengthened the ML algorithms against data leakage and overfitting. Finally, a dimensional scalability study further assessed the use of the pipeline for the large-cube dataset and established the impact of geometrical scaling on defect formation and prediction in 3D-printed parts. Full article

This paper presents a unified modeling framework for quantifying power and energy consumption in motor-driven systems operating under variable frequency control and soft starter conditions. By formulating normalized expressions for voltage, current, and power factor as functions of motor speed, the model enables accurate estimation of instantaneous and cumulative energy use using only measurable electrical quantities. The effect of soft starter operation during startup is incorporated through ramp-based profiles, while variable frequency control is modeled through dynamic speed modulation. Analytical results show that variable speed control can achieve energy savings of up to 36.1% for sinusoidal speed profiles and up to 42.9% when combined with soft starter operation, with the soft starter alone contributing a consistent 8.6% reduction independent of the power factor. To support energy optimization under uncertain demand scenarios, a two-stage stochastic optimization framework is developed for motor sizing and control assignment, and four physics-guided machine learning models—MLP, LSTM, GRU, and XGBoost—are benchmarked to forecast normalized energy ratios from key electrical parameters, enabling rapid and interpretable predictions. The proposed framework provides a scalable, interpretable, and practical tool for monitoring, diagnostics, and smart energy management of industrial motor-driven systems. Full article

This paper deals with static stability in planar grasps of an object by multiple fingers. Differently from previous research, we focus on the case that each finger has redundant links and joints. Based on contact constraints between the object and fingers, the relationships among displacements of object’s pose, contact positions, and joint positions are formulated. Using the constraints, the redundant joints are reduced to independent parameters. The relationship between the displacement and reaction torque of each joint is modeled as a linear spring, and potential energy of the grasp is formulated. Not only for frictionless sliding contact but also for pure rolling contact, we derive stable conditions on the contact positions and joint positions. Based on the conditions, partially differentiating the potential energy, a wrench (force and moment) vector and a stiffness matrix applied to the object by each finger are derived. Summing up the wrenches and matrices of all the fingers, we obtain a wrench vector and a stiffness matrix of the grasp, and we evaluate the grasp stability. Because of our analytical formulation, grasp parameters such as local curvatures at contact points, joint stiffnesses, etc., are explicitly included in the derived matrices. Partially differentiating the wrenches and matrices by the grasp parameters, we clarify effects of the parameters on the stability. Moreover, the difference between the frictionless sliding contact and pure rolling contact is derived in the wrench vector and the stiffness matrix. Using numerical examples, we validate our analysis. Full article

Background and Objectives: Maternal telomere length (TL) has been proposed as a potential biomarker of biological aging and pregnancy outcomes, yet evidence in Central and Eastern European populations remains scarce. This study aimed to investigate the association between maternal TL and neonatal parameters in a clinically healthy cohort. Materials and Methods: We conducted a prospective observational study including 134 mother–infant pairs at the “Pius Brînzeu” Emergency County Clinical Hospital, Timișoara. All deliveries were performed by cesarean section for maternal indications unrelated to fetal condition. Maternal blood samples were collected at admission, and relative TL was measured by quantitative PCR. Neonatal outcomes included birth weight, length, head circumference, gestational age, and Apgar scores. Results: Longer maternal TL was positively correlated with birth weight (r = 0.515, p < 0.001), length (r = 0.559, p < 0.001), head circumference (r = 0.468, p < 0.001), gestational age (r = 0.444, p < 0.001), and Apgar scores at 1 (r = 0.714, p < 0.001) and 5 min (r = 0.684, p < 0.001). Logistic regression showed that shorter maternal TL independently predicted suboptimal 1 min Apgar (<8), with an adjusted odds ratio of 0.68 (95% CI: 0.51–0.91). Conclusions: Maternal TL is strongly associated with neonatal growth and vitality measures, supporting its potential as a simple, non-invasive biomarker for perinatal risk assessment. Full article

Background: The aim of this study was to systematically explore point-of-care biomarkers as diagnostic indicators for the detection and exclusion of mild traumatic brain injury (mTBI) in an emergency room (ER) setting using Eye-Tracking and Virtual Reality (ET/VR) technology. The primary target group included patients who had suffered an acute trauma to the head and presented within 24 h to the emergency department. Methods: The BG Unfallkrankenhaus Berlin and the BG Klinikum Hamburg participated in this explorative, prospective, single-arm accuracy study. This study included patients who presented to the emergency department with suspected mTBI and were examined using ET/VR glasses. All further steps corresponded to clinical routine (e.g., decision on hospital admission, imaging diagnostics). After the completion of treatment, the patients were divided into mTBI and non-TBI subgroups by consensus between two independent clinical experts, who were blinded to the results of the index test (examination using ET/VR glasses) in the form of a clinical synopsis. The diagnosis was based on all clinical, neurological, neurofunctional, neuropsychological, and imaging findings. Routine trauma and neurological history, examination, and diagnosis were performed in each case. All statistical analyses were performed with exploratory intent. Results: The use of ET/VR glasses was found to be predominantly unproblematic. Two of the fifty-two analyzed parameters can be statistically distinguished from a random decision. No difference in oculomotor function was found between the two subgroups, and no correlations between the parameters recorded by the VR goggles and the detection of mTBI were found. Conclusions: At present, the use of VR goggles for the diagnosis of mTBI in an ER setting cannot be recommended. Full article

The BEST1 gene encodes a transmembrane protein in the retinal pigment epithelium (RPE) in the eye, that functions as a calcium-dependent chloride channel (CaCC). Pathogenic variants in BEST1 are the underlying cause for bestrophinopathies, a group of inherited retinal disorders that vary in their pattern of inheritance, clinical appearance, and underlying molecular disease mechanisms. Currently, there are no treatments available for any of the bestrophinopathies, and gene therapy represents an attractive strategy due to the accessibility of the eye and slow disease progression. While gene augmentation may be effective for a subset of bestrophinopathies, others require allele-specific silencing or correction of the disease-causing variant to reconstitute expression of the BEST1 protein. This review aims to give an overview of the clinical diversity of bestrophinopathies and proposes the molecular disease mechanism of the pathogenic BEST1 variant as an important parameter for the choice of treatment strategy. Furthermore, we discuss the potential of different mutation-specific and mutation-independent CRISPR/Cas9-based gene editing strategies as a future treatment approach for bestrophinopathies. Full article

Background/Objective: Malnutrition and sarcopenia are common complications after ischemic stroke and have a negative impact on prognosis. The C-reactive protein/albumin ratio (CAR) reflects both inflammation and nutritional status, but its predictive role in this setting has not been widely studied. This study aimed to investigate the predictive value of CAR (C-reactive protein/albumin ratio) for malnutrition risk and probable sarcopenia in patients with ischemic stroke. Methods: In this prospective observational study, 197 patients with acute ischemic stroke were evaluated. Patients with chronic renal or hepatic failure, malignancy, active infection, and hand disability preventing grip strength measurement were excluded. Demographic data (age, sex), vascular risk factors, the NIHSS score, and laboratory parameters were recorded. The nutritional status of patients was assessed using the Nutritional Risk Screening-2002 (NRS-2002), and sarcopenia risk was evaluated with the SARC-F questionnaire. Handgrip strength was measured in patients with high SARC-F scores to define probable sarcopenia. CAR was calculated from serum CRP and albumin levels. Logistic regression was applied to identify independent predictors, and receiver operating characteristic (ROC) analyses were performed to determine the discriminatory ability and cut-off values of CAR. The nutritional status of patients admitted to the neurology clinic with acute ischemic stroke was assessed using the Nutritional Risk Screening-2002 (NRS-2002), and sarcopenia risk was evaluated with the SARC-F questionnaire. Handgrip strength was measured in patients with high SARC-F scores to define probable sarcopenia. CAR was calculated from serum CRP and albumin levels. Logistic regression and receiver operating characteristic (ROC) analyses were performed. Results: Malnutrition risk was identified in 32.5% of patients, and probable sarcopenia was identified in 19.3% of patients. ROC analysis showed that CAR had acceptable discriminatory power for both conditions. In multivariate analysis, CAR was consistently identified as an independent predictor of malnutrition risk and possible sarcopenia. ROC analysis for malnutrition risk showed an AUC of 0.750 (cut-off: 0.306; sensitivity 68.8%; specificity 75.2%). In regression analysis, CAR (OR = 2.13; 95% CI: 1.39–3.26; p < 0.001), age (OR = 1.05; 95% CI: 1.02–1.09; p = 0.003), and NIHSS (OR = 1.11; 95% CI: 1.01–1.23; p = 0.026) were independent predictors. For probable sarcopenia, ROC analysis revealed an AUC of 0.814 (cut-off: 0.320; sensitivity 81.6%; specificity 71.7%). Multivariate analysis identified CAR (OR = 1.73; 95% CI: 1.19–2.52; p = 0.004), age (OR = 1.11; 95% CI: 1.05–1.18; p < 0.001), and NIHSS (OR = 1.19; 95% CI: 1.05–1.35; p = 0.007) as independent predictors. Conclusions: CAR was identified as an independent predictor of both malnutrition risk and probable sarcopenia in ischemic stroke patients. CAR may serve as a reliable biomarker for early nutritional and functional risk stratification in clinical practice. Full article