Search Results (32,976)

Previous studies have shown that plasma amyloid-beta oligomers (AβOs), the toxic form of amyloid-beta (Aβ), are a critical issue in the development or worsening of Alzheimer’s disease (AD) and can be regarded as a blood marker for screening in dementia. We examined plasma AβOs with their related biomarkers in a case–control study to clarify these issues. A total of 16 patients diagnosed with Alzheimer’s dementia (AD) and 16 cognitively normal controls (NCs) were recruited to compare their plasma biomarkers, AβO, Aβ_1-40, and Aβ_1-42, also referring to other parameters like APOE ε4 status, Clinical Dementia Rating^®-Sum of Boxes (CDR^®-SB), and Mini Mental Status Examination (MMSE) scores. In plasma concentrations of Aβ_1-40, Aβ_1-42, and AβO, the mean concentrations were significantly different between the two groups. There is a significant increase in the concentrations of Aβ_1-40 and AβO, while Aβ_1-42 is decreased in individuals with AD compared to NC. AβO was statistically associated with the Aβ_1-40 and Aβ_1-42/Aβ_1-40 ratio. Higher plasma concentrations of AβO were significantly associated with AD compared to non-dementia controls. This suggests that AβOs can be potential plasma biomarkers to screen in AD. However, a study recruiting more individuals is necessary to examine the association, if any. Full article

A novel stochastic finite element model updating method based on multi-point sensitivities is proposed to improve the reproduction and prediction ability of finite element models for experimental data. Drawing upon the theory of small perturbations, this approach employs the sensitivity matrix in conjunction with the probability distribution of responses evaluated at multiple parameter points to determine the probability density associated with each parameter point and to estimate the statistical properties of the parameters. To achieve this objective, principal component analysis is employed to unify the dimensionality of the parameters and the responses; the least squares method was used to estimate the characteristics of the parameters. The reliability and validity of this method were confirmed through experimentation with a 3-degree-of-freedom spring-mass system and an aerospace thermal insulation structure. A comparison of this method with classical methods reveals significant advantages in terms of robustness across varying computational scales. Notably, it attains superior accuracy with smaller sample sizes while maintaining precision comparable to conventional methods with large samples. Consequently, this characteristic confers upon the method a distinct advantage in scenarios where the costs of finite element computation are prohibitively high. Full article

Background/Objectives: Kidney donation remains a critical component of addressing end-stage renal disease. This study examines differences in awareness, willingness to donate, and concerns related to kidney donation among medical and non-medical university students. By comparing these groups within the context of Croatia’s presumed-consent system for organ donation, the study provides insights into how educational backgrounds shape attitudes in a setting with high transplantation rates but limited data on young adults. Methods: A cross-sectional observational study targeted at medical and non-medical university students in Croatia. Data were collected from 640 participants via a self-administered, close-ended, structured questionnaire with 33 items divided across three sections. Responses were analyzed using IBM SPSS Statistics program (v. 30.0), to identify significant differences. Due to the cross-sectional design, causal relationships could not be inferred. Results: Overall, 190 students (28.7%) reported willingness to donate a kidney during their lifetime, which was more common among medical students (N = 59; 39.0%) than non-medical students (N = 131; 26.8%). Collectively, willingness to donate postmortem was high in both groups (N = 527; 82.3%), as was willingness in a brain-dead state (N = 448; 70.0%). Medical and non-medical students mostly cited perceived health risks as a concern and concerns related to surgical complications. Regarding information sources, 33.2% of students reported inadequate knowledge of kidney donation, with social media and internet searches cited more frequently than healthcare professionals. Conclusions: Our findings indicate that medical and non-medical students exhibit distinct gaps in knowledge, risk perception and willingness toward kidney donation. Within Croatia’s presumed-consent framework, these findings highlight the importance of targeted educational strategies to support informed decision-making among future generations. Full article

Background: Patient-reported outcome measures (PROMs) provide important insights into recovery after anterior cruciate ligament reconstruction (ACLR). Previous research suggests that males and females recover differently after ACLR, with females reporting greater pain, slower functional gains, and lower psychological readiness at later stages of rehabilitation. However, it is unknown if patient-reported outcomes differ by sex early after ACLR. To address this gap, we conducted a cross-sectional analysis comparing patient-reported outcome measures between sexes three months after ACLR. We hypothesized that females would report worse PROMs compared to males. Methods: This cross-sectional analysis used data from a prospectively maintained ACL reconstruction cohort. Fifty-six individuals (female: 23 and male: 33) with primary, unilateral ACLR completed PROMs three months after surgery. These PROMs included the Knee Injury and Osteoarthritis Outcome Score (KOOS; Symptoms, Pain, Activities of Daily Living, Sport and Recreation, Quality of Life), International Knee Documentation Committee (IKDC) subjective score, Knee Outcome Survey–Activities of Daily Living Scale (KOS-ADLS), Anterior Cruciate Ligament–Return to Sport After Injury (ACL-RSI), and the Tampa Scale of Kinesiophobia (TSK). All outcomes were expressed on a 0 to 100 percent scale, with higher scores indicating better outcomes, except for TSK, where lower scores indicated better outcomes. Normality was assessed within sex, using the Shapiro–Wilk test. Two-tailed independent-samples t-tests with Welch correction were used for approximately normal variables; otherwise, Mann–Whitney U tests were utilized (α = 0.05). Several outcomes had limited statistical power to detect MCID-sized differences, and findings for these measures should be interpreted cautiously. Results: No significant differences between sexes were found for any of the PROMs. Males trended towards having better KOOS Sport and Recreation and IKDC, but these were not statistically significant, and the effect sizes were small-to-moderate. Conclusions: No statistically significant sex-based differences were detected in PROMs at approximately 3 months after ACLR, indicating that any sex-related divergences between these measures may not occur until later in recovery. Full article

This study presents a texture-aware image synthesis framework designed to generate material-consistent façades using adversarial learning. The proposed architecture incorporates a mask-guided channel-wise attention mechanism that adaptively merges segmentation information with texture statistics to reconcile structural guiding with textural fidelity. A thorough comparative analysis was performed utilizing three internal variants—Vanilla GAN, Wasserstein GAN (WGAN), and WGAN-GP—against leading baselines, including TextureGAN and Pix2Pix. The assessment utilized a comprehensive multi-metric framework that included SSIM, FID, KID, LPIPS, and DISTS, in conjunction with a VGG-19 based perceptual loss. Experimental results indicate a notable divergence between pixel-wise accuracy and perceptual realism; although established baselines attained elevated PSNR values, the suggested Vanilla GAN and WGAN models exhibited enhanced perceptual fidelity, achieving the lowest LPIPS and DISTS scores. The WGAN-GP model, although theoretically stable, produced smoother but less complex textures due to the regularization enforced by the gradient penalty term. Ablation investigations further validated that the attention mechanism consistently enhanced structural alignment and texture sharpness across all topologies. Thus, the study suggests that Vanilla GAN and WGAN architectures, enhanced by attention-based fusion, offer an optimal balance between realism and structural fidelity for high-frequency texture creation applications. Full article

The article examines the implementation of 3D printing in civil protection and crisis management with a focus on the educational process, while 3D printing technology enables the creation of various teaching aids that streamline teaching and enrich theoretical knowledge. The empirical part of the study is based on a quantitative questionnaire survey among students of the Faculty of Safety Engineering of the University of Žilina in Žilina, with hypotheses set in advance and forming the basis for the construction of the questionnaire. The questionnaire collected data on the subjective evaluation of 3D printing through continuous, nominal, and ordinal responses and was completed by 277 students. Statistical methods of simple and group classification, as well as t-test, ANOVA, Kruskal–Wallis and Pearson’s correlation analysis were used to evaluate the data. Statistical significance was used to determine whether observed differences and relationships were unlikely to have arisen by chance. In addition, effect size measures were used in correlation and regression analyses to assess the strength and practical relevance of statistically significant relationships. The results of the study show that 3D printing significantly contributes to improving education and preparedness in civil protection, as it allows for more material-efficient and flexible production of educational aids compared to traditional custom production. Thus, it supports the development of more resilient communities and contributes to long-term sustainability. The findings confirmed that 3D printing is a suitable tool for improving public preparedness for emergencies. Full article

Background/Objectives: Osteoporosis is a major global health concern, and early risk assessment plays a crucial role in fracture prevention. Although demographic, clinical, and lifestyle factors are commonly incorporated into screening tools, their relative importance within data-driven prediction frameworks can vary substantially across datasets. Rather than aiming to identify novel predictors, this study evaluates the stability and behavior of established osteoporosis risk factors using statistical inference and machine learning-based feature selection methods across heterogeneous data sources. We further examine whether simplified and near-minimal models can achieve predictive performances comparable to that of full-feature configurations. Methods: An open-access Kaggle dataset (n = 1958) and a retrospective clinical dataset from the University of Phayao Hospital (n = 176) were analyzed. Feature relevance was assessed using logistic regression, likelihood ratio testing, MRMR, ReliefF, and unified importance scoring. Multiple predictor configurations, ranging from full-feature to minimal and near-minimal models, were evaluated using decision tree, support vector machine, k-nearest neighbor, naïve Bayes, and efficient linear classifiers. External validation was performed using hospital-based records. Results: Across all analyses, age consistently emerged as the dominant predictor, followed by corticosteroid use, while other variables showed limited incremental predictive contributions. Simplified models based on age alone or age combined with medication-related variables achieved performances comparable to full-feature models (accuracy ≈91% and AUC ≈ 0.95). In addition, near-minimal models incorporating gender alongside age and medications demonstrated a favorable balance between discrimination and computational efficiency under external validation. Although overall performance declined under distributional shift, naïve Bayes and efficient linear classifiers showed the most stable external behavior (AUC = 0.728–0.787). Conclusions: These findings indicate that stability-driven feature selection primarily reproduces well-established epidemiological risk patterns rather than identifying novel predictors. Minimal and near-minimal models—including those incorporating gender—retain acceptable performances under external validation and are methodologically efficient. Given the limited size and single-center nature of the external cohort, the results should be interpreted as preliminary methodological evidence rather than definitive support for clinical screening deployment. Further multi-center studies are required to assess generalizability and clinical relevance. Full article

Background: Lingual orthodontic systems have recently advanced with the introduction of fully customized CAD/CAM-based designs featuring self-ligating (SL) mechanisms. This study aimed to evaluate the three-dimensional accuracy of a customized SL lingual system in reproducing digitally planned tooth positions. Methods: A total of 280 teeth were analyzed following treatment with a fully customized self-ligating lingual system (Harmony^®, Aso International Inc., Tokyo, Japan). Digital models obtained before treatment (T0), from the setup (TS), and after treatment (T1) were superimposed using a best fit algorithm in GOM Inspect. Tooth movements were quantified across seven biomechanically relevant parameters including tip, torque, rotation, buccolingual, mesiodistal, vertical, and overall displacement. Predicted and achieved movements were compared using paired t tests and Bland–Altman analysis. Results: The fully customized SL lingual appliance achieved an overall dentition accuracy of 92.1%. Mean accuracy for linear tooth movements was 94.5% ± 2.1% in the maxilla and 93.8% ± 2.5% in the mandible. For angular movements, mean accuracy was 90.8% ± 3.4% in the maxilla and 89.3% ± 3.9% in the mandible. The highest precision was observed in anterior teeth for mesiodistal (96.2%) and buccolingual (95.8%) movements, whereas the lowest accuracy occurred in rotational movements of the posterior segments (87.1%). No statistically significant differences were found between predicted and achieved movements for most parameters (p > 0.05). Conclusions: The fully customized SL lingual orthodontic system demonstrated high accuracy in reproducing digitally planned tooth movements, particularly in the anterior segments. Although accuracy was slightly lower in the posterior regions, the overall outcomes remained mechanically and clinically acceptable across all evaluated dimensions. Full article

Mud loach (Misgurnus mizolepis) is a freshwater fish widely farmed in inland aquaculture owing to its nutritional value. However, failure to distinguish Chinese from Korean mud loach negatively affects the distribution economy and food safety regulation. Untargeted profiling was previously used to determine the origin of mud loaches, and N-acetylhistidine and anserine were selected as biomarker candidates. However, their quantitative verification and practical applicability for origin discrimination have not been thoroughly investigated. In this study, mud loaches of different geographical origins were analyzed using liquid chromatography-ultraviolet and liquid chromatography-tandem mass spectrometry to quantify the two metabolites, followed by statistical and receiver operating characteristic (ROC) analyses to evaluate their discriminative performance. Compared with Korean mud loaches, Chinese mud loaches showed significantly higher concentrations of both metabolites. The area under the curve values for N-acetylhistidine and anserine were 0.88 and 0.89, respectively, reflecting high sensitivity and specificity for discriminating between Korean and Chinese mud loaches. Cutoff values were established for reliably distinguishing the geographical origin of mud loaches. The established approach based on N-acetylhistidine and anserine can be used to determine the geographical origin of mud loach. Full article

To evaluate the influence of different surface conditioning protocols—sandblasting (SB), tribochemical silica coating (TBC), CO₂ laser irradiation, and plasma-enhanced chemical vapor deposition (PECVD-Si coating for 49 min) on surface roughness (Ra), surface morphology, and composite-to-zirconia shear bond strength (SBS). Eighty 3Y-TZP plates were randomly allocated into four groups (n = 20) based on surface conditioning protocol: Group 1 (SB), Group 2 (CO₂ laser), Group 3 (TBC), and Group 4 (PECVD-Si coating for 49 min). From each group, five specimens underwent Ra assessment using a contact profilometer, and five specimens were examined for surface morphology via scanning electron microscopy (SEM). The remaining ten specimens received resin composite buildup, followed by artificial aging. Subsequently, SBS testing was performed using a universal testing machine, and failure modes were evaluated under a stereomicroscope. Statistical analysis was conducted using one-way ANOVA with post hoc Tukey test and chi-square for fracture assessment(α = 0.05). Group 1 (SB) demonstrated the lowest Ra (0.844 ± 0.063 µm) and SBS (12.21 ± 4.6 MPa), whereas Group 4 (PECVD-Si coating for 49 min) exhibited the highest Ra (1.388 ± 0.098 µm) and SBS (30.48 ± 2.5 MPa). Intergroup comparison revealed no statistically significant differences between Groups 2 and 3 for both Ra and SBS values (p > 0.05). However, Groups 1 and 4 differed significantly in both parameters (p < 0.05). PECVD-based silica coating for 49 min demonstrated superior surface conditioning efficacy for 3Y-TZP, yielding significantly higher Ra and SBS values compared to sandblasting, tribochemical silica coating, and CO₂ laser irradiation. Full article

The region of South-East Europe, located in geologically and climatically diverse areas, hosts a wide range of freshwater habitats. However, comprehensive studies of macroinvertebrate communities are limited, and research on Chironomidae (Diptera) is particularly scarce. We present data on the diversity and structure of chironomid assemblages in hilly and mountainous streams across three ecoregions: the Pannonian Lowland (Ecoregion 11), the Dinaric Western Balkans (Ecoregion 5), and the Eastern Balkans (Ecoregion 7) and provide a comparative overview of their community patterns based on 130 samples. According to the CCA results and Monte Carlo permutation tests, water temperature, dissolved oxygen, conductivity, pH, and altitude were identified as statistically significant parameters influencing Chironomidae assemblages across the ecoregions, collectively explaining 72.20% of the variation. The higher diversity indices were recorded in each season in the Pannonian Lowland and the highest within-ecoregion similarity. Dissimilarity was highest between ER11 and ER7 and lowest between ER5 and ER7. These results demonstrate that the ecoregion was the strongest influence of the studied environmental variables on Chironomidae assemblages, with community patterns closely reflecting their spatial distribution across distinct ecoregional settings. Full article

Background/Objectives: Diagnostic performance of angiography-derived physiological measures has been benchmarked against two-dimensional (2D) and three-dimensional (3D) quantitative coronary angiography (QCA), which are known for their poor correlation with hemodynamic lesion severity. Relying on the statistical concept of the wisdom of the crowd, we devised a human-performance reference for FFR surrogates, called vox populi FFR (vpFFR), and examined the comparative diagnostic performance of vpFFR, as well as 2D- and 3D-QCA, using invasively measured FFR as the gold standard. Methods: Analyses were performed in a single-center, prospective registry of consecutive FFR procedures. We calculated vpFFR as a mean of five independent, blinded predictions of the invasively measured FFR. Pearson’s correlation coefficient and receiver operating characteristic (ROC) curve analyses were used for diagnostic performance comparisons. Results: In 116 patients (156 vessels), Pearson’s correlation coefficients for vpFFR, 2D-, and 3D-QCA with invasively measured FFR are 0.56, −0.26, and −0.01, respectively (p < 0.001, p = 0.001 and p = 0.918). vpFFR has a sensitivity of 56%, specificity of 84%, positive predictive value of 67%, and negative predictive value of 76%. It correctly classified hemodynamic severity of lesions in 73% of vessels compared to 65% and 51% for 2D- and 3D-QCA, respectively. vpFFR has a larger area under the ROC curve than 2D- and 3D-QCA for predicting positive FFR (0.78, 0.63, and 0.45, respectively, p < 0.001). Conclusions: vpFFR, a mean value of five predictions of invasively measured FFR, has moderate diagnostic performance, superior to 2D- and 3D-QCA using FFR as the gold standard, and can be used as a human-performance reference for existing and emerging angiography-derived physiological measures. Full article

Uncertainty quantification in science and engineering has become increasingly important due to advances in computational mechanics and numerical simulation techniques. In this work, the relationship between uncertainty in soil material parameters and the variability of failure loads and displacements of a shallow foundation is investigated. A Drucker–Prager constitutive law is implemented within a stochastic finite element framework. The random material variables considered are the critical state line slope c, the unload–reload path slope $\kappa$, and the hydraulic permeability k defined by Darcy’s law. The novelty of this work lies in the integrated stochastic u–p finite element framework. The framework combines Drucker–Prager plasticity with spatially varying material properties, and Latin Hypercube Sampling. This approach enables probabilistic prediction of failure loads, displacements, stresses, strains, and limit-state initiation points at reduced computational cost compared to conventional Monte Carlo simulations. Statistical post-processing of the output parameters is performed using the Kolmogorov–Smirnov test. The results indicate that, for the investigated configurations, the distributions of failure loads and displacements can be adequately approximated by Gaussian distributions, despite the presence of material nonlinearity. Furthermore, the influence of soil depth and load eccentricity on the limit-state response is quantified within the proposed probabilistic framework. Full article

To address the insufficient exploration ability, susceptibility to local optima, and limited convergence accuracy of the standard Student Psychology-Based Optimization (SPBO) algorithm in three-dimensional UAV trajectory planning, we propose an enhanced variant, Enhanced SPBO (ESPBO). ESPBO augments SPBO with three complementary strategies: (i) Time-Adaptive Scheduling, which uses normalized time ($\tau =t/T$) to schedule global step-size shrinking, Gaussian fine-tuning, and Lévy flight intensity, enabling strong early exploration and fine late-stage exploitation; (ii) Mentor Pool Guidance, which selects a top-K mentor set and applies time-varying guidance weights to reduce misleading attraction and improve directional stability; and (iii) Directional Jump Exploration, which couples a differential vector with Lévy flights to strengthen basin-crossing while keeping the differential step bounded for robustness. Numerical experiments on CEC2017, CEC2020 and CEC2022 benchmark functions compare ESPBO with Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Improved multi-strategy adaptive Grey Wolf Optimization (IAGWO), Dung Beetle Optimization (DBO), Snake Optimization (SO), Rime Optimization (RIME), and the original SPBO. We evaluate best path length, mean trajectory length, standard deviation, and convergence curves and assess statistical stability via Wilcoxon rank-sum tests (p = 0.05) and the Friedman test. ESPBO significantly outperforms the comparison algorithms in path-planning accuracy and convergence stability, ranking first on both test suites. Applied to 3D UAV trajectory planning in mountainous terrain with no-fly zones, ESPBO achieves an optimal path length of 199.8874 m, an average path length of 205.8179 m, and a standard deviation of 5.3440, surpassing all baselines; notably, ESPBO’s average path length is even lower than the optimal path length of other algorithms. These results demonstrate that ESPBO provides an efficient and robust solution for UAV trajectory optimization in intricate environments and extends the application of swarm intelligence algorithms in autonomous navigation. Full article

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning. Full article