Search Results (1,022)

Background: Keloid scarring is a fibroproliferative disorder driven by a complex interplay of genetic, epigenetic, and environmental factors, resulting in significant cosmetic and functional impairment. Despite its high prevalence in African, Asian, and Hispanic populations, the molecular mechanisms underlying ancestry-dependent susceptibility remain incompletely understood. Methods: This narrative review synthesizes current genomic, epigenetic, and multi-omic evidence related to keloid scarring. Relevant literature was identified through a targeted, structured, non-systematic search of PubMed, Scopus, Web of Science, SciELO, and Google Scholar up to August 2025, focusing on genetic susceptibility loci, epigenetic regulation, and ancestry-related differences. PRISMA-ScR guidelines were used as a reporting framework to enhance transparency, without implying a formal systematic review methodology. Results: This synthesis identifies recurrent susceptibility loci at 1q41, 3q22.3, and 15q21.3 across multiple populations. Variants in NEDD4 and regulatory regions near BMP2 emerge as key modulators of profibrotic signaling pathways, including TGF-β/SMAD and NF-κB. Additionally, epigenetic reprogramming and long non-coding RNA networks, such as CACNA1G-AS1, appear to sustain fibroblast hyperactivation. A persistent limitation is the marked underrepresentation of Latin American populations in current genomic studies. Conclusions: Integrating ancestry-specific genomic variation with epigenetic markers is essential for advancing precision diagnostic and therapeutic strategies in keloid scarring. Future research should prioritize diverse, multicenter cohorts and integrative multi-omics approaches to improve risk stratification and enable targeted interventions for this disfiguring condition. Full article

Results of a methodological case study of low-level clouds in the atmosphere using a 1064 nm fiber lidar are presented. The lidar experiment was carried out in Daejeon, Republic of Korea, in January–March 2025. The study’s primary objective was to ascertain the vertical extinction coefficient profiles pertaining to tenuous, low-altitude cloud formations via implementation of a refined Sequential Lidar Signal Processing Algorithm (SLSPA). The SLSPA incorporates statistical estimation theory to assess signal and measurement error. Cloud extinction coefficient profiles are estimated within the SLSPA utilizing the modified Klett–Fernald inversion algorithm. The SLSPA adaptation is required (a) to evaluate the accuracy of Q-switch laser-based lidar sounding signal deconvolution, (b) to mitigate the impact of the lidar form factor on measurement results, (c) to account for aerosol extinction coefficient variability within the cloud in the modified inversion algorithm (MIA), and (d) to evaluate multiple scattering effect correction in the MIA. Theoretical and experimental aspects of the modified SLSPA are considered sequentially in the present work. The experimental results presented here are based on datasets sampled from the entire array of experimental data obtained during the measurement period. Full article

Understanding the complex drivers of water yield is essential for ensuring basin water resource security, yet existing linear approaches often overlook the critical nonlinear effects arising from factor interactions. Previous studies combining the InVEST model with attribution methods have typically treated climate and land use as independent factors, failing to quantify their interactive effects beyond additive assumptions. This study addresses this gap by introducing a coupled framework that explicitly isolates and quantifies nonlinear climate–land interactions through scenario-based residual decomposition and spatial interaction detection. Focusing on the Minjiang River Basin, this study first applies a locally calibrated InVEST model to analyze the spatiotemporal patterns of water yield from 2000 to 2023. Through scenario analysis and the Geographical Detector method, we decoupled the contributions of climatic factors, land use, and their interactions. The results show significant spatiotemporal heterogeneity in water yield, averaging 1053.59 mm, with a spatial pattern aligned closely with precipitation. Climatic factors dominated the changes (average contribution 93.43%), while the direct contribution of land use was minimal (−1.56%). Importantly, a significant nonlinear interaction effect was identified (average 8.13%), with the interplay between precipitation and forest land proportion showing the strongest explanatory power for spatial differentiation (q-statistic up to 96.4%). These findings highlight the necessity of an integrated climate-land regulatory strategy that enhances climate resilience and optimizes key land uses to promote sustainable water management, providing a methodological framework for analyzing complex hydrological drivers. Full article

Introduction: Glioblastoma (GBM) is the most common and aggressive type of glioma. Although current treatment strategies are well-established, their effectiveness remains limited. Recent studies have highlighted the potential of short-chain fatty acids (SCFAs)—such as acetate, butyrate, propionate, and valeric acid—as therapeutic agents against various solid tumors. Methodology: We evaluated the cell viability of A172, a GBM cell line, upon treatment with SCFAs using MTT assay. We then investigated the underlying molecular mechanisms of cell death induced by sodium butyrate and valeric acid, using their respective IC50 concentrations via Real-Time qPCR. Results: The IC50 values indicated that A172 cells were more sensitive to sodium butyrate and valeric acid (IC50 = 9.22 mM and 19.04 mM, respectively) than to sodium propionate and sodium acetate (IC50 = 41.21 mM and 121.2 mM, respectively) after 72 h of treatment. In cells treated with sodium butyrate, we observed an increased expression of BAK and decreased expression of P53 and CASP1. Treatment with valeric acid led to upregulation of BCL-2, BAK, and RIPK3, along with downregulation of P53. Conclusions: Our preliminary findings suggest that SCFAs, particularly sodium butyrate and valeric acid, can induce cell death in GBM cells through distinct molecular pathways. While further studies are necessary to elucidate the exact mechanisms, these results support the potential of SCFAs as therapeutic candidates for glioblastoma. Full article

Background/Objectives: Standardizing measurement of circulating cell-free DNA (cfDNA) in healthy individuals is critical for its application as a reference in biomarker research, yet methodological variability remains poorly documented. Methods: We systematically reviewed 35 studies (n = 1250 healthy subjects) assessing how pre-analytical handling, extraction kits, and quantification methods influence plasma cfDNA levels. We identified quantification approaches (qPCR vs. fluorometry) and use of custom extraction kits as the strongest drivers of variability. Results: In qPCR studies, including ≥ 40 subjects reduced variability, underscoring the importance of adequate sample size. Commercial kits produced more consistent yields than in-house protocols; in our dataset, many studies used Qiagen’s QIAamp Circulating Nucleic Acid Kit, which has historically served as a widely used reference platform. Blood collection in EDTA tubes had minimal impact when commercial kits were used. Conclusions: Based on these findings, we recommend EDTA tubes, a standardized commercial extraction kit, and qPCR quantification to minimize cfDNA measurement variability in healthy cohorts. Finally, we provide expected cfDNA ranges for healthy individuals based on methodological flow, which can guide future benchmarking efforts and biomarker studies, improving comparability and early-detection research. Full article

Despite the growing demand for functional gluten-free (GF) foods, the application of Quercus pubescens acorn flour remains largely underexplored. This study addresses this gap by optimizing GF cookies using response surface methodology (RSM) and prepared with Q. pubescens acorn flour and xanthan gum to balance technological quality, sensory acceptability, and functional value. A three-level full factorial design (FFD) evaluated the effects of acorn flour proportion (0, 50 and 100%), and xanthan gum level (1, 2 and 3%) on physicochemical properties (moisture, water activity, color, texture, and dimensions), sensory attributes using a 9-point hedonic scale, proximate composition, and bioactive and antioxidant properties (total polyphenols, tannins, DPPH, ABTS, FRAP). Linear and quadratic polynomial models adequately described the experimental data (R² = 0.86–0.99; non-significant lack of fit). Increasing acorn flour content significantly intensified cookie darkening, reduced snapping force and bending stiffness, reduced spread factor, and affected sensory perception, while xanthan gum improved structural integrity and dimensional stability. Multi-response optimization identified an optimal formulation containing 41.05% acorn flour and 1.46% xanthan gum, achieving balanced color development (darkness index ≈ 62), bending stiffness (~38 N/mm), and high overall sensory acceptability (~7.8). The optimized GF cookies exhibited a favorable nutritional profile and antioxidant properties, characterized by elevated total polyphenol content and antioxidant capacity, confirming the functional potential of acorn flour. The optimized cookies (containing 41.05% acorn flour) exhibited a six-fold increase in total phenolic content (from 1.63 to 10.08 mg GAE/g) and 8–10 times higher antioxidant capacity (DPPH, ABTS, and FRAP assays) compared to the control, confirming the substantial functional potential of Q. pubescens in gluten-free systems. Full article

This study aims to systematically synthesize and critically evaluate the characteristics of electric arc furnace slag (EAFS) and ladle furnace slag (LFS) when applied as an alternative paving material. A systematic literature review was conducted following the PRISMA methodology, with research published between 2000 and 2024. Three major databases were searched, considering only Q1–Q2 and English articles. After independent, blinded screening by two reviewers, a total of 177 papers met the selection criteria. The results were qualitatively synthesized through bibliometric analysis, slag characteristics, and application type. Results show that asphalt concrete (AC) is the most common application of EAFS, representing 61% of studies, with many studies exploring 100% substitution of natural aggregates. Overall, EAFS and LFS demonstrate favorable mechanical properties, including high toughness, hardness, and adequate soundness, largely attributed to their iron-rich composition, supporting their use in base layers, AC, and Portland cement concrete (PCC). However, significant chemical and mineralogical variability influences swelling potential and reactivity, highlighting the need for case-specific characterization. While swelling concerns limit its use as an unbound base material, these issues are reduced when EAFS and LFS are used as a soil binder or encapsulated within AC or PCC matrices. Environmental assessments show that most EAFS and LFS samples meet the regulatory thresholds for their respective local leaching limits, though behavior varies with steel type (low-alloy vs. stainless), particle size and pH. Significant gaps remain in long-term performance and testing standards. This review proposes guidelines for selecting appropriate tests according to the intended pavement application, aiming to facilitate the safe and effective use of EAFS and LFS in road infrastructure. Full article

The aim of this study is to investigate and demonstrate the role of correction functions and optimisation-based refinement algorithms in enhancing the performance of machine learning models, particularly in predictive anomaly detection tasks applied in industrial environments. The performance of machine learning models is highly dependent on the quality of data preprocessing, model architecture, and post-processing methodology. In many practical applications—particularly in time-series forecasting and anomaly detection—the conventional training pipeline alone is insufficient, because model uncertainty, structural bias and the handling of rare events require specialised post hoc calibration and refinement mechanisms. This study provides a systematic overview of the role of correction functions (e.g., Principal Component Analysis (PCA), Squared Prediction Error (SPE)/Q-statistics, Hotelling’s T², Bayesian calibration) and adaptive improvement algorithms (e.g., Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), Simulated Annealing (SA), Gaussian Mixture Model (GMM) and ensemble-based techniques) in enhancing the performance of machine learning pipelines. The models were trained on a real industrial dataset compiled from power network analytics and harmonic-injection-based loading conditions. Model validation and equipment-level testing were performed using a large-scale harmonic measurement dataset collected over a five-year period. The reliability of the approach was confirmed by comparing predicted state transitions with actual fault occurrences, demonstrating its practical applicability and suitability for integration into predictive maintenance frameworks. The analysis demonstrates that correction functions introduce deterministic transformations in the data or error space, whereas improvement algorithms apply adaptive optimisation to fine-tune model parameters or decision boundaries. The combined use of these approaches significantly reduces overfitting, improves predictive accuracy and lowers false alarm rates. This work introduces the concept of an Organically Adaptive Predictive (OAP) ML model. The proposed model presents organic adaptivity, continuously adjusting its predictive behaviour in response to dynamic variations in network loading and harmonic spectrum composition. The introduced terminology characterises the organically emergent nature of the adaptive learning mechanism. Full article

To address the high-dimensional design optimization of an adaptive cycle fan (ACF), this paper proposes a new multi-objective optimization (MOO) method based on explainable artificial intelligence (XAI)-driven feature selection. The proposed method integrates a neural network surrogate model, Shapley additive explanation (SHAP) analysis, and a genetic algorithm. By considering Pareto front quality, surrogate model accuracy, and optimization preference, a composite evaluation metric, Q, is defined to guide a bidirectional feature selection process based on SHAP analysis, thereby establishing a dynamic, closed-loop process of simultaneous feature selection and MOO. The results indicate that the proposed method significantly enhances global search capability, accurately identifying 66 optimal features from 119 initial features. A further comparison with results without forward selection confirms the necessity of dynamically adjusting the feature space during optimization. Under the same condition, the optimal design increases the core pressure ratio from 2.71 to 2.81 and core efficiency from 80.80% to 82.92%. The flow mechanism analysis reveals that the performance gains mainly result from the reconstruction of shock structures and the suppression of shock–boundary layer interactions and secondary flows. The XAI-enhanced surrogate-assisted evolutionary algorithm (SAEA) proposed in this paper provides a promising methodology for high-dimensional MOO of aeroengines and other complex systems. Full article

Quantiles are among the most fundamental constructs in probability theory and statistics, intrinsically linked to order structures, stochastic dominance, and the principles of robust statistical inference. Although the univariate theory of quantiles is by now classical and well developed, their generalization to multivariate settings remains mathematically subtle and methodologically demanding. In particular, extending the notion of “location within a distribution” beyond one dimension raises delicate questions of geometry, ordering, and equivariance. Within this landscape, the spatial—or geometric—formulation of multivariate quantiles has emerged as a rigorous and conceptually unifying framework capable of reconciling these issues. In this work we advance this paradigm by introducing a kernel-based estimation procedure for nonparametric conditional geometric quantiles of a multivariate response $Y\in {\mathbb{R}}^q$ ($q\ge 2$) given a functional covariate X that takes values in an infinite-dimensional space. The data are assumed to form a strictly stationary and ergodic process, while the responses may be subject to a missing-at-random mechanism, a feature of substantial practical relevance. Our analysis establishes strong consistency of the proposed estimator, characterizes its optimal convergence rate, and derives its asymptotic distribution. These limit theorems, in turn, provide the theoretical foundation for constructing asymptotically valid confidence regions and for performing inference in multivariate quantile regression with functional covariates. The theoretical developments rest on natural complexity conditions for the involved functional classes together with mild smoothness and regularity assumptions. This balance between generality and mathematical precision ensures that the resulting methodology is not only robust in a rigorous probabilistic sense but also widely applicable to contemporary problems in high-dimensional and functional data analysis. The proposed methodology is numerically investigated through simulations and is implemented in a real data application. Full article

The Folin–Ciocalteu method remains the standard approach for quantifying total phenolics in plant extracts; however, matrix-specific optimization is essential for obtaining accurate results for chemically complex botanical materials. The Peruvian Amazon harbors extensive botanical biodiversity, including numerous medicinal species with uncharacterized phenolic profiles. This study developed and validated a Folin–Ciocalteu method specifically optimized for twelve ethnomedicinal plants representing eleven families from the Peruvian Amazon, following ICH Q2(R2) guidelines. Method optimization established optimal analytical conditions: 765 nm wavelength, 60 min reaction time, 14.05% sodium carbonate, and gallic acid as the reference standard. Comprehensive validation demonstrated excellent linearity (R² = 0.995–1.000), specificity confirmed through parallel standard addition curves (slope differences < 3%), precision with relative standard deviations below 2.63% for both repeatability and intermediate precision, and accuracy with recovery of 89.43 ± 2.76% meeting AOAC guidelines for complex matrices (80–120%). Robustness testing via response surface methodology confirmed method stability across variations in sodium carbonate concentration (7.50–14.05%), Folin–Ciocalteu reagent dilution (50–100%), and reaction time (30–90 min). Limits of detection and quantification were 4.43 and 13.44 μg/mL, respectively. Application to the twelve species revealed 10-fold variation in total phenolic content (24.6 ± 2.1 to 256.8 ± 4.3 mg gallic acid equivalents per gram dry extract), with Aspidosperma schultesii leaves exhibiting the highest concentration. This validated methodology provides a reliable analytical framework for the quality control and standardization of Amazonian medicinal plants, supporting bioprospecting efforts and therapeutic development. Full article

Multi-criteria decision-making (MCDM) involves evaluating alternatives under uncertain, vague, and conflicting criteria. While fuzzy set theories, such as intuitionistic, pythagorean, fermatean, and q-rung orthopair fuzzy sets have advanced uncertainty modeling, they remain limited to capturing extreme judgments where membership reaches a value of one alongside significant non-membership. The recently introduced q-fractional fuzzy set (q-FrFS) addresses these shortcomings via a flexible constraint, making it suitable for extreme contexts. However, existing q-FrFS methodologies lack robust aggregation mechanisms capable of balancing trade-offs and modulating compensation during information fusion. To overcome this, this study proposes a novel class of Frank-based aggregation operators tailored specifically to q-FrFS environments. Leveraging the parameterized structure of Frank t-norms and t-conorms, we develop two operators: q-FrFFWA (Frank weighted averaging) and q-FrFFWG (Frank weighted geometric) alongside their essential algebraic properties. These operators enhance the representation and fusion of complex and uncertain data. Furthermore, we present a comprehensive MCDM framework utilizing the proposed operators and demonstrate its applicability by selecting optimal vehicle routing software for last-mile delivery. Sensitivity and comparative analyses affirm the stability and credibility of the proposed methodology. This research contributes to the evolving landscape of fuzzy decision-making by integrating the expressive power of q-FrFS with the adaptive flexibility of Frank aggregation, offering a potent tool for modeling and analyzing multidimensional uncertainties in complex decision environments. Full article

Psychological problems such as depression, anxiety, stress, loneliness, and reduced quality of life are prevalent in older adults, yet the effectiveness of psychological interventions remains heterogeneous. This systematic review with meta-analysis evaluated the impact of psychological and psychoeducational interventions on emotional symptoms and quality-of-life outcomes in adults aged 60 years and older. Following PRISMA 2020 guidelines, a comprehensive search was conducted in PubMed, Scopus, CINAHL, and Web of Science. Randomized controlled trials published in the last five years were included if they assessed interventions such as mindfulness, cognitive behavioral therapy, reminiscence therapy, or behavioral activation. Twenty-eight trials were included in the qualitative synthesis and twenty-two in the meta-analysis. Standardized mean differences (Hedges’ g) were pooled under fixed- and random-effects models. Heterogeneity, subgroup analyses, and publication bias were examined using Q, I², Begg–Mazumdar, Egger, and Trim-and-Fill methods. The global meta-analysis showed a moderate and significant favorable effect of psychological interventions on emotional symptoms under the random-effects model (SMD = −0.623, 95% CI −0.888 to −0.359; p < 0.001), where negative values indicate reductions in symptom severity. Subgroup analyses revealed a moderate effect on depressive symptoms, which remained significant after adjustment for publication bias, and a large effect on perceived stress (SMD = 0.581; p < 0.001); for stress outcomes, positive SMDs indicate reductions in stress (i.e., improvement) after aligning scale directionality. Anxiety showed a significant effect only under the fixed-effects model, while loneliness showed a small but significant effect (SMD = −0.110; p = 0.018). Mindfulness-specific outcomes and quality of life did not show significant pooled effects. No substantial publication bias was detected. Psychological interventions significantly improve emotional well-being in older adults, particularly by reducing depression and stress. Effects on anxiety, loneliness, mindfulness, and quality of life are more variable, emphasizing the need for methodological consistency and longer follow-up in future studies. Full article

This study investigates the predictability of Environmental, Social, and Governance (ESG) performance using financial fundamentals within the context of Taiwan, a prominent small open economy integrated into global value chains. As global markets transition toward mandatory sustainability reporting, identifying the financial ante-cedents of ESG outcomes is critical for risk management and regulatory oversight. Uti-lizing a decade of firm-level data (2014–2023) from the Taiwan Economic Journal (TEJ), we employ supervised machine learning (ML) architectures-including Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost)-to classify firms into ESG performance tiers based on indicators such as profitability, valuation, and scale. Our empirical results provide robust support for the Slack Resources Hypothesis, identifying Return on Assets (ROA) and Firm Size (SIZE) as the most consistent predictors of ESG excellence across the semiconductor, cement, and steel sectors. Conversely, mar-ket-based indicators (Tobin’s Q) dominate predictive models for the financial industry. Methodologically, XGBoost delivers superior predictive calibration for the financial sector, while Decision Trees offer highly interpretable threshold-based logic for risk screening. Our study contributes a transparent “early-warning” framework, enabling investors and regulators to identify sustainability risks through auditable financial benchmarks. The findings suggest that while financial latitude is a structural prerequisite for ESG engagement, it is not its sole determinant, pointing toward a “virtuous circle” of financial health and managerial quality. Full article

The increasing concern over heavy metal contamination in export crops has intensified research on the application of computer vision systems (CVS) and advanced sensing technologies within multi-level agricultural monitoring frameworks spanning soil contamination assessment, crop spectral diagnostics, and in situ elemental sensing. This study conducts a systematic literature review following Kitchenham’s methodology, from which 68 studies were finally included after screening and eligibility assessment. The review focuses on the use of hyperspectral imaging (HSI) and XRF-IoT sensors (X-ray fluorescence units enhanced with IoT connectivity) for detecting heavy metals in export crops, considering publications from the last seven years indexed in Web of Science Core Collection, Scopus, IEEE Xplore, EBSCOhost, and Springer Nature Link. The findings indicate that research is concentrated in highly digitalized countries, which limits its global applicability; moreover, a substantial proportion of studies is published in Q1 journals, although the methodologies are not always fully objective. Likewise, the most developed research lines are oriented toward image-based diagnostics and crop analysis. These results reveal a gap between technological advances in computer vision and their integration into agricultural decision-making aimed at improving the quality of export crops. It is recommended to foster research with greater geographical diversity, grounded in solid theoretical frameworks and an ethical perspective. Full article