Search Results (13,229)

Background/Objectives: Sleep state misperception (SSM) is a common phenomenon in insomnia disorder (ID), characterized by a discrepancy between subjective and objective sleep metrics. Recent studies have revealed microstructural EEG alterations specifically in misperceiving ID patients, yet clinically accessible SSM markers remain limited. This study aimed to characterize SSM within ID by integrating standard polysomnography (PSG) features and cognitive-affective traits, focusing on accessible clinical tools. Methods: Twenty patients with ID and twenty healthy controls (HC) underwent a night of PSG recording and completed both sleep diaries and a comprehensive psychological assessment. SSM was quantified using the Total Sleep Time misperception index (TSTm), analyzed both dimensionally and categorically Results: IDs reported significantly altered sleep parameters compared to HCs, both subjectively and objectively. Within the ID sample, although underestimators and normoestimators had similar objective TST, underestimators showed significantly more cortical arousal density (CAd), a higher percentage of sleep stage 1 and higher non-acceptance of emotions. Notably, none of the HC reached the threshold for being classified as underestimators. Regression analyses identified CAd, latency to sleep stage 3 and to REM, percentage of REM sleep and lack of emotional clarity, as key predictors of TSTm. Conclusions: SSM in insomnia reflects a dimensional vulnerability grounded in subtle sleep fragmentation and emotional dysregulation. Recognizing SSM as a clinically meaningful phenomenon may guide more targeted, emotion-focused, interventions for insomnia. Full article

The long-distance high-voltage transmission tower-line system, frequently traversing active fault zones, is vulnerable to severe symmetry-breaking damage during earthquakes due to asymmetric permanent ground displacements. However, the seismic performance of such systems, particularly concerning symmetry-breaking effects caused by asymmetric fault displacements, remains inadequately studied. This study investigates the symmetry degradation mechanisms in a 1:40 scaled 500 kV tower-line system subjected to cross-fault ground motions via shaking table tests. The testing protocol incorporates representative fault mechanisms—strike-slip and normal/reverse faults—to systematically evaluate their differential impacts on symmetry response. Measurements of acceleration, strain, and displacement reveal that while acceleration responses are spectrally controlled, structural damage is highly fault-type dependent and markedly asymmetric. The acceleration of towers without permanent displacement was 35–50% lower than that of towers with permanent displacement. Under identical permanent displacement conditions, peak displacements caused by normal/reverse motions exceeded those from strike-slip motions by 50–100%. Accordingly, a fault-type-specific amplification factor of 1.5 is proposed for the design of towers in dip-slip fault zones. These results offer novel experimental insights into symmetry violation under fault ruptures, including fault-specific correction factors and asymmetry-resistant design strategies. However, the conclusions are subject to limitations such as scale effects and the exclusion of vertical ground motion components. Full article

(1) Background: Low back pain (LBP) is the most prevalent cause of disability worldwide, yet current assessment relies mainly on subjective questionnaires, underscoring the need for objective and interpretable biomarkers. Bioelectrical impedance parameter (BIP), quantified by resistance (R), impedance magnitude (Z), and phase angle (PA), reflects tissue hydration and cellular integrity and may provide physiological correlates of pain; (2) Methods: This cross-sectional study used lumbar BIP and demographic characteristics from 83 participants (38 with lumbar BIP and 45 normal controls). We applied Extreme Gradient Boosting (XGBoost), a regularized tree-based machine learning (ML) algorithm, with stratified five-fold cross-validation. Model interpretability was ensured using SHapley Additive exPlanations (SHAP), which provide global importance rankings and local feature attributions. Outcomes included classification of LBP versus healthy status and regression-based prediction of pain scales: the Visual Analog Scale (VAS), Oswestry Disability Index (ODI), and Roland–Morris Disability Questionnaire (RMDQ); (3) Results: The classifier achieved high discrimination (ROC–AUC = 0.996 ± 0.009, sensitivity = 0.950 ± 0.068, specificity = 0.977 ± 0.049). Pain prediction showed best performance for VAS (R² = 0.70 ± 0.14; mean absolute error = 1.23 ± 0.27), with weaker performance for ODI and RMDQ; (4) Conclusions: These findings suggest that explainable ML models applied to BIP could discriminate between LBP and healthy groups and could estimate pain intensity, providing an objective complement to subjective assessments. Full article

Face recognition systems typically face actual challenges like facial pose, illumination, occlusion, and ageing that significantly impact the recognition accuracy. In this paper, a robust face recognition system that uses Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and face alignment with an enhanced FaceNet for facial embedding extraction is presented. The enhanced FaceNet uses attention mechanisms to achieve more discriminative facial embeddings, especially in challenging scenarios. In addition, an Adaptive Feature Fusion module synthetically combines identity-specific embeddings with context information such as pose, lighting, and presence of masks, hence enhancing robustness and accuracy. Training takes place using the CelebA dataset, and the test is conducted independently on LFW and IJB-C to enable subject-disjoint evaluation. CelebA has over 200,000 faces of 10,177 individuals, LFW consists of 13,000+ faces of 5749 individuals in unconstrained conditions, and IJB-C has 31,000 faces and 117,000 video frames with extreme pose and occlusion changes. The system introduced here achieves 99.6% on CelebA, 94.2% on LFW, and 91.5% on IJB-C and outperforms baselines such as simple MTCNN-FaceNet, AFF-Net, and state-of-the-art models such as ArcFace, CosFace, and AdaCos. These findings demonstrate that the proposed framework generalizes effectively between datasets and is resilient in real-world scenarios. Full article

The ubiquitous presence of smartphones has led to new phenomenon such as “phubbing” (the act of ignoring one’s immediate surroundings in favor of using a mobile phone). This behavior has become increasingly common among university students, making it an important subject of study due to its potential negative impact on learning environments. The aim of the present study is to analyze the prevalence of phubbing among university students, the existence of significant differences as a function of specific sociodemographic variables (such as gender, age, academic performance, and connection frequency), and, lastly, the predictive capacity of these elements with the different levels of phubbing experienced. The sample was composed of 1121 Spanish university students, and the instrument selected for the collection of data was the Phubbing Scale, which was divided into three factors, “attachment to the mobile phone”, “communication disturbance”, and “smartphone obsession”, through different validity and reliability tests. The results indicated a moderately high prevalence of phubbing among the population studied. Likewise, statistically significant differences were identified at a multivariate level in the three dimensions. Lastly, it is notable that the frequency of smartphone usage significantly and positively predicted the three dimensions of phubbing. Full article

Background: The Macklin effect recently demonstrated a high positive predictive value for barotrauma in the COVID-19 ARDS population. However, there was less evidence available regarding the ARDS population without SARS-CoV-2 infection. We aim to analyze COVID-19 and non-COVID-19 ARDS subjects to assess the sensitivity and specificity of the Macklin effect in predicting the development of barotrauma in both groups. Methods: We retrospectively analyzed subjects with ARDS admitted to our center from January 2018 to November 2022. Experienced radiologists examined the presence of the Macklin effect on chest computed tomography scans. Subjects were then divided into two groups based on the presence or absence of the Macklin effect to assess its predictive power regarding barotrauma. Finally, we analyzed the impact of the Macklin effect and barotrauma on Intensive Care Unit and in-hospital mortality. Results: We analyzed 225 patients; the Macklin effect was observed in 44 subjects. In our cohort, the Macklin effect exhibited a sensitivity of 44.6% and a specificity of 90.6% in predicting barotrauma. After excluding the COVID-19 ARDS cases, the Macklin effect showed a sensitivity of 34.7% and a specificity of 93.6%. Nonetheless, in our population, the presence of the Macklin effect or the occurrence of barotrauma did not lead to increased ICU or in-hospital mortality. Conclusions: Our analysis highlighted that the Macklin effect demonstrates high specificity in predicting barotrauma but a low sensitivity; moreover, the development of barotrauma did not impact mortality, possibly due to the exclusion of mild to moderate ARDS and the inclusion of a significant number of ECMO recipients. Finally, the Macklin effect appears early during ARDS and may serve as an early indicator of lung frailty, potentially becoming an additional criterion for referral to centers for advanced ARDS treatment. Full article

Background and Objectives: Sarcopenia is a progressive loss of skeletal muscle mass and function in elderly adults, posing a significant risk of frailty, falls, and morbidity. The current study designs and evaluates SarcoNet, a novel artificial neural network (ANN)-based classification framework developed in order to classify Sarcopenic from non-Sarcopenic subjects using a comprehensive real-time dataset. Methods: This pilot study involved 30 subjects, who were divided into Sarcopenic and non-Sarcopenic groups based on physician assessment. The collected dataset consists of thirty-one clinical parameters like skeletal muscle mass, which is collected using various equipment such as Body Composition Analyser, along with ten kinetic features which are derived from video-based gait analysis of joint angles obtained during walking on three terrain types such as slope, steps, and parallel path. The performance of the designed ANN-based SarcoNet was benchmarked against the traditional machine learning classifiers utilised including Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), and Random Forest (RF), as well as hard and soft voting ensemble classifiers. Results: SarcoNet achieved the highest overall classification accuracy of about 94%, with a specificity and precision of about 100%, an F1-score of about 92.4%, and an AUC of 0.94, outperforming all other models. The incorporation of lower-limb joint kinetics such as knee flexion, extension, ankle plantarflexion and dorsiflexion significantly enhanced predictive capability of the model and thus reflecting the functional deterioration characteristic of muscles in Sarcopenia. Conclusions: SarcoNet provides a promising AI-driven solution in Sarcopenia diagnosis, especially in low-resource healthcare settings. Future work will focus on improving the dataset, validating the model across diverse populations, and incorporating explainable AI to improve clinical adoption. Full article

Impulsive purchasing behavior among university students has gained increased attention in the context of digital consumption settings; however, much of the existing research is product-specific and quantitative, leaving the subjective nuances of this phenomenon underexplored. This study investigates how college students perceive and explain their impulsive purchase behavior across various product categories and platforms, using qualitative data from focus groups (n = 72). By revealing the prevalence of key patterns—interest-aligned, emotional relief, hedonistic lifestyle, social influence, inquisitive reviewer, presentation appeal, and controlled purchase—this research uncovers the underlying identity-affirming practices, internal emotional negotiations, and external sociotechnical cues that shape such behavior. Ultimately, it reframes impulsive buying as a socially embedded, identity-driven act rather than an act of irrationality. These findings advance our understanding of consumer psychology by emphasizing the lived experiences and self-construction processes of young consumers navigating media-saturated, algorithmically curated purchasing environments. Full article

This study examines the feasibility of applying eye tracking as a rigorous method for assessing user experience in web design. A controlled experiment was conducted with 102 participants, who interacted with both guideline-compliant websites and systematically degraded variants violating specific principles of Material Design 2. Eleven websites were presented in A/B conditions with modifications targeting three design dimensions: contrast, link clarity, and iconography. Eye-tracking indicators—time to first fixation, fixation duration, fixation count, and time to first click—are examined in conjunction with subjective ratings and expert assessments. Mixed-effects models are employed to ensure robust statistical inference. The results demonstrate that reduced contrast and unclear links consistently impair user performance and increase search effort, whereas the influence of icons is more context-dependent. The study contributes by quantifying the usability costs of guideline deviations and by validating a triangulated evaluation framework that combines objective, subjective, and expert data. From a practical perspective, the findings support the integration of eye tracking into A/B testing and guideline validation, providing design teams with empirical evidence to inform and prioritize improvements in user interfaces. Full article

Halophytes such as Salicornia europaea rely on biochemical and structural mechanisms to survive in saline environments. This study aimed to evaluate oxidative stress and structural defense responses in four inland populations—Poland (Inowrocław, Ciechocinek), Germany (Salzgraben-Salzdahlum, Salz), and Soltauquelle (Soltq)—subjected to 0, 200, 400, and 1000 mM NaCl, using non-destructive, image-based approaches. Lipid peroxidation was assessed via malondialdehyde (MDA) detected with Schiff’s reagent, and hydrogen peroxide (H₂O₂) accumulation was visualized with 3,3′-diaminobenzidine (DAB). Roots and shoots were analyzed through colour image analysis and quantified using a computer vision system (CVS). MDA accumulation revealed population-specific differences, with Salz tending to exhibit lower peroxidation, characterized by lower L* ≈ 42–43 and higher b* ≈ 37–18 in shoots at 200–400 mM, which may reflect a potentially more effective salt-management strategy. Although H₂O₂ responses deviated from a direct salinity-dependent trend, particularly in the tolerant Salz and Soltq populations, both approaches effectively tracked population-specific adaptation, with German populations displaying detectable basal H₂O₂ levels, consistent with its multifunctional signalling role in salt management and growth regulation. Structural defences were further explored through histochemical mapping and image analysis of pectin and lignin distribution, which revealed population-specific patterns consistent with cell wall remodelling under stress. Non-destructive, image-based methods proved effective for detecting oxidative and structural responses in halophytes. Such a non-destructive, cost-efficient, and reproducible approach can accelerate the identification of salt-tolerant ecotypes for saline agriculture and reinforce S. europaea as a model species for elucidating salt-tolerance mechanisms. Full article

Background/Objectives: The diagnosis of Mycobacterium tuberculosis complex (MTBC) and nontuberculous mycobacterial (NTM) infections is accomplished by three main diagnostics methods: smear microscopy, culture, and molecular testing. Diagnostic algorithms used by laboratories can significantly impact clinical and infection control management. Current Canadian Tuberculosis Standards recommend the use of nucleic acid amplification testing (NAAT) for smear-positive patients and smear-negative patients upon request. An alternative algorithm is to utilize NAAT in the Panel approach on all samples, pulmonary and extrapulmonary, to potentially reduce time to diagnosis and treatment. This alternative approach was implemented in November 2019 at the Newfoundland and Labrador Public Health and Microbiology Laboratory (NL PHML) using a laboratory-developed multiplex real-time PCR (LDT m-qPCR) assay targeting Mycobacterium spp. (Myco spp.) and MTBC, performed in parallel with smear and culture. Methods: To investigate the impact of this alternate testing approach, we conducted an observational retrospective analysis of laboratory diagnostic and treatment data, recognizing that temporal changes in epidemiology, clinical practice, and laboratory workflow may also have influenced outcomes. To complete this, study data from three years before and four years after implementation were gathered. Results: The sensitivity/specificity of the smear, m-LDT qPCR-MTBC, m-LDT qPCR-Myco spp., and culture assays in this study were 18.1%/100%, 96.7%/99.8%, 47.6%/99.0%, and 96.8%/100%, respectively. The gold standard utilized for these calculations was clinical diagnosis for active MTBC disease and culture for NTM infections, recognizing that the use of clinical diagnosis may introduce subjectivity. The Panel approach reduced the time to diagnosis of tuberculosis MTBC by 29 days (p < 0.0001) for NL PHML, and when modelled for a laboratory with rapid culture identification, diagnosis was reduced by 14 days (p = 0.003). Among non-empirically treated tuberculosis patients, the time to treatment was decreased by 25.5 days (p < 0.001). For NTM infections, rapid diagnostics only affected one patient’s treatment. This finding agrees with clinical management guidelines, which do not routinely utilize rapid diagnostics for the diagnosis of disease or treatment decisions. The cost implications of additional NAAT testing were calculated to be an increase of CAD 23.62 per sample. Conclusions: Our findings support the adoption of a molecular assay for MTBC as an initial diagnostic tool to decrease time to diagnosis and time to treatment, depending on local epidemiology and irrespective of smear status. Utilizing a molecular assay for genus level identification of NTM had minimal impact on clinical management suggesting its limited diagnostic utility in a broad population setting. Full article

Background and objectives: Clinical laboratory analyses are essential for diagnosing and treating allergic diseases mediated by immunoglobulin E (IgE). Identifying the sources of sensitivity, subject to regional variations, enables the implementation of effective management strategies, such as prevention and immunotherapy. Through a cross-sectional study, this study aimed to determine the sensitivity profile to aeroallergens (latent class) and their correlation with age, sex, and season in the population sampled. Methods: The purpose was to map the concentrations of specific IgE serum levels linked to the most prevalent allergens, considering variations related to age, specific IgE levels, and seasons of the year through a cross-sectional study. Results: The 995 reports of specific IgE tests analyzed were clustered into six aeroallergen categories and were predominantly composed of female individuals (57.1%). The most prevalent age group was younger than 18 (56.5%), and most exams were performed in the spring (27.7%). The aeroallergen category ‘grass’ significantly correlated with sex and age, indicating that men have a 65% greater probability of having high levels of specific IgE as a response to this allergen, and age is related to higher IgE levels. Latent class analysis identified an optimal three-class solution for specific IgE sensitization patterns: Class 1: Moderate Sensitization; Class 2: Low Sensitization; and Class 3: High Sensitization. Conclusion: The present study highlights the importance of knowing the local aeroallergen profile for early diagnosis and prevention of allergic diseases, emphasizing the relevance of the allergen category related to the age and sex of the individuals. Full article

Background/Objectives: Thyroid-stimulating hormone (TSH), free thyroxine (FT4), and total triiodothyronine (TT3) are biomarkers for evaluating thyroid function. Although hormone levels are affected by many biological and environmental factors, most laboratories use manufacturer-provided reference intervals (RIs) without considering these factors. Thus, in this study we assessed RIs for TSH, FT4, and TT3 in a Korean population, using a direct selection technique. Methods: Serum samples from patients without a history of thyroid disease, medication, family history, or antibody-positive test results were collected after a review of medical records. TSH, FT4, and TT3 levels were measured using the Cobas e801 analyzer (Roche Diagnostics GmbH, Mannheim, Germany) with dedicated reagents. RIs were then established using a non-parametric method, using values at the 2.5th and 97.5th percentiles as reference limits, which were then verified in a validation cohort. Results: A total of 618 subjects were enrolled in this study. Because the distribution of reference values for the four subgroups divided by sex and age (65 years) showed insignificant differences, combined RIs were determined, with the established RIs being 0.38–5.46 mIU/L for TSH, 12.28–22.40 pmol/L for FT4, and 0.94–2.32 nmol/L for TT3. When compared to manufacturer-claimed RIs, the Korean RI for TSH showed higher upper limits, while that for TT3 showed lower upper limits. Additionally, when newly established RIs were applied to the validation cohort, the rate of test-positive results decreased significantly. Conclusions: Significant differences in RIs for TSH and TT3 in the Korean population, compared to manufacturer-claimed values, highlight the need for population-specific RIs. Thus, interpreting the results for the Korean population requires caution, and Korean population-based RIs are necessary. Full article

Lower limb functional degeneration has become prevalent, notably reducing the core strength that drives motor control. Squats are frequently used in lower limb training, improving overall muscle strength. However, performing continuously with improper techniques can lead to dynamic knee instability. It worsens with little to no motivation to perform these power training motions. Hence, it is crucial to have a gaming-based exercise tracking system to adaptively enhance the user experience without causing injury or falls. In this work, 28 healthy subjects performed exergame-based squat training, and dynamic kinematic features were recorded. The five features acquired from a depth camera-based inertial measurement unit (IMU) (1—Knee shakiness, 2—Knee distance, and 3—Squat depth) and an Anima forceplate sensor (4—Sway velocity and 5—Sway area) were assessed using a Spearman correlation coefficient-based feature selection method. An input vector that defines knee instability is used to train and test the Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models for binary classification. The results showed that knee instability events can be successfully classified and achieved a high accuracy of 96% in both models with sets 1, 2, 3, 4, and 5 and 1, 2, and 3. The feature selection results indicate that the LSTM network with the proposed combination of input features from multimodal sensors can successfully perform real-time tracking of knee instability. Furthermore, the findings demonstrate that this multimodal approach yields improved classifier performance with enhanced accuracy compared to using features from a single modality during lower limb therapy. Full article

Eukaryotic cells respond to oxidative stress (OS), a physiological condition characterized by the accumulation of reactive oxygen species (ROS), through various protective mechanisms. The antioxidant defense system (ADS) is activated either by post-translational modifications of pre-existing proteins or through the induction of gene expression. These mechanisms protect cellular biomolecules against ROS damage. Although extensive research has been conducted in different species, there is limited information regarding the specific response of Yarrowia lipolytica to OS. This study aims to elucidate the molecular mechanisms by which Y. lipolytica responds to OS by analyzing the expression of genes encoding enzymes involved in antioxidant response, such as superoxide dismutase (Sod), catalase (Cat), and glutathione peroxidase (Gpx). The Y. lipolytica genome contains three CAT genes, one SOD gene, one copper chaperone for Sod (CCS) gene, and one GPX gene. The expression profiles of these genes were assessed in Y. lipolytica cells exposed to H₂O₂ [5 mM] over time. All genes reached their maximal expression within the first 15 min of exposure. Comparative analysis between young and aged Y. lipolytica cells subjected to OS revealed that young cells exhibited higher expression levels for all genes, with CAT3 and SOD showing the highest expression values. These findings suggest that the enzymes encoded by these genes play a crucial role in the antioxidant response of this species. To our knowledge, this is the first study demonstrating that the ADS in Y. lipolytica is regulated at the transcriptional level. Full article