Search Results (4,274)

Urinary microRNAs (miRNAs) are promising noninvasive biomarkers for cancer detection, but their clinical utility is reduced by inconsistent normalization strategies, reducing reproducibility and comparability across studies. In this study, we assessed the stability of miR-20a as an endogenous normalizer for urinary miRNA profiling in clear cell renal cell carcinoma (ccRCC) while standardizing the pre-analytical phase using a urine stabilizing solution. Ninety-nine urine samples were analyzed: 47 from healthy individuals, 30 from ccRCC patients pre-surgery, and 22 post-operative patients. Six candidate miRNAs—miR-20a, miR-15b, miR-16, miR-15a, miR-210-3p, and miR-let-7b—were quantified via RT-qPCR. Stability analysis with RefFinder, integrating multiple algorithms (geNorm, normFinder, BestKeeper, and ΔCt methods), identified miR-20a as the most stable among the six candidates. Raw Ct values of miR-20a were normally distributed (Shapiro–Wilk test, p > 0.05), with no significant intergroup differences (one-way ANOVA, F(2.96) = 2.324, p = 0.103) and minimal intragroup variability (CV% 4.98–6.38). MiR-20a expression remained stable across different tumor staging, grading, and urine storage durations. These findings confirm miR-20a as a robust endogenous normalizer for urinary miRNA analyses and support the feasibility of developing reproducible urinary liquid biopsy workflows for ccRCC, even in settings where immediate sample processing is not feasible. Full article

To address the reduced accuracy of stereo matching networks near object boundaries and disparity discontinuities, a double cost–volume stereo matching network with entropy-difference-guided fusion is proposed. The proposed network was built based on RAFT-Stereo. It employs a pretrained backbone to extract multi-scale features and uses deformable attention for cross-scale feature fusion. A shallow image-guided branch was used to generate pixel-wise constraint information to limit the magnitude of sampling offsets and alleviate cross-structure sampling. Based on the extracted features, a group-wise correlation cost–volume and a normalized correlation cost–volume were constructed. Both cost–volumes were regularized by 3D Hourglass networks, and a structure-consistent intra-scale aggregation module was introduced during the regularization of the group-wise correlation cost–volume. The two aggregated results were then fused by the entropy-difference-guided fusion module to obtain the final cost–volume. The experimental results show the effectiveness of the proposed network in the Scene Flow, KITTI, and ETH3D datasets, achieving an endpoint error of 0.45 px and a >3 px error rate of 2.41% on the Scene Flow dataset. Full article

This study compared the kinematic and neuromuscular characteristics of the tennis forehand drive between high-performance (HP) and intermediate (INT) players. Eighteen right-handed male players (HP: n = 9; INT: n = 9) performed cross-court forehands while three-dimensional motion capture and surface electromyography (EMG) were recorded from the dominant upper limb and trunk. Kinematic and EMG data were time-normalized to the forward swing. One-dimensional statistical parametric mapping two-sample t-tests were used to compare joint angles, angular and linear velocities, and EMG amplitude waveforms between groups. Bonferroni-corrected significance levels were set at α = 0.0017 for kinematic variables and α = 0.0063 for EMG data. HP players exhibited greater racket linear velocity during the final part of the forward swing, accompanied by higher shoulder, elbow and wrist linear velocities, whereas hip linear velocity did not differ between groups. Joint angles were broadly similar, with SPM revealing only slightly greater early knee flexion in HP players. In contrast, HP players showed higher hip and knee angular velocities and greater wrist angular velocities in both flexion/extension and radial/ulnar deviation towards impact. EMG patterns were generally comparable, but HP players displayed higher biceps brachii activation in two significant clusters during the mid-to-late forward swing and greater triceps brachii activation in the late forward swing. No significant differences were observed for deltoid, pectoralis major, latissimus dorsi, flexor carpi radialis or extensor carpi radialis. These findings indicate that superior forehand performance in HP players is associated primarily with refined segmental coordination, greater lower-limb and distal segment velocities, and locally increased elbow muscle activation, rather than with widespread increases in upper-limb or trunk muscle activity. Full article

Egyptian papyri are commonly documented using high-resolution two-dimensional imaging, which enhances legibility but does not adequately capture the micrometric surface morphology required for material and conservation studies. To address this limitation, we developed and validated an integrated, fully non-contact imaging workflow combining Ultra-Close-Range Multiband Photogrammetry with Reflectance Transformation Imaging (RTI) and normal map integration. The protocol was tested on six papyrus fragments from the Museo Egizio di Torino (XXI Dynasty–Byzantine period) exhibiting different conservation conditions. Multiband photogrammetry in the visible and visible-induced infrared luminescence bands achieved a Ground Sample Distance of 17 µm/px and a point cloud density of approximately 170 points/mm², enabling detailed analysis of fiber morphology, surface deformation, and the spatial distribution of Egyptian blue. RTI-based normal map integration provided complementary high-frequency surface information with reduced acquisition and processing times. To overcome RTI low-frequency distortions, a revised normal integration strategy was implemented using surface planarization and frequency-domain fusion with photogrammetric data based on Power Spectral Density analysis. The resulting hybrid models combine metric reliability with enhanced surface detail, providing a scalable and non-invasive approach for papyrological documentation and conservation research. Full article

Background and Objectives: This study investigated whether capillary and intravenous sampling affect acylcarnitine and amino acid profile results analyzed by tandem mass spectrometry. Methods: The study included 120 patients either diagnosed with an inherited metabolic disease or undergoing evaluation for a suspected metabolic disorder at the Department of Pediatric Nutrition and Metabolism, Selçuk University Faculty of Medicine. Paired capillary and intravenous blood samples were collected simultaneously, applied to filter paper, and analyzed by LC-MS/MS to determine acylcarnitine and amino acid profiles. Results: Significant differences were observed between capillary and intravenous samples for several acylcarnitines, including C0, C2, C8, C8.1, C10, C10.1, C14.1, C16, and C18.1 (p < 0.05). In the amino acid profile, arginine, aspartic acid, citrulline, glutamic acid, glycine, leucine + isoleucine, methionine, tyrosine, and the methionine/phenylalanine ratio differed significantly between sampling methods (p < 0.05). Despite these differences, Cohen’s kappa analysis showed high agreement between capillary and venous samples for most parameters (78.3–100%) when categorized as low, normal, or high based on reference ranges. Additionally, no significant discrepancies were found in key diagnostic parameters among patients with specific inherited metabolic diseases. Conclusions: Although certain acylcarnitine and amino acid levels differed between capillary and intravenous samples, overall diagnostic agreement was high. However, since the study group did not include any patients with fatty acid oxidation disorders, a separate confirmatory study is needed for this condition. Larger multicenter studies involving more patients and a wider range of metabolic disorders are needed to better understand the clinical impact of sampling method on dried blood spot analyses. Full article

This two-step design used the unilateral bench press to examine the effect of breast cancer surgery on upper-limb muscle activation under low and moderate fatigue conditions. First, we studied the proper method to normalize the activation values obtained during dynamic contractions. For that, the muscle activation was relativized to the maximal value obtained during (i) an isometric contraction (ISO_Norm), and the concentric phase of the (ii) repetition maximum load (1RM_Norm), and (iii) the first three repetitions of an 80% 1RM set (Max80%_Norm). The normalization method with the lowest inter-subject variability was further used to compare the muscle activation of the affected and non-affected sides of twelve women who underwent unilateral breast surgery (eight mastectomies and four lumpectomies). Both sides were tested using dynamic sets at 60 and 80% of their 1RM until reaching 40% velocity loss (VL). Repetitions completed at each %1RM were then divided into two groups: low fatigue (first half of repetitions) and moderate fatigue (second half of repetitions). On results, the ISO_Norm and the Max80%_Norm showed the highest (mean CV = 32.9%) and lowest (mean CV = 12.9%) inter-subject variability, respectively. The affected side showed higher activation for the deltoid and triceps (Δ = 6.9 to 15.9%) but lower for the pectoralis (Δ = −5.7 to −13.2%) against 60% 1RM. These differences were lower and without a consistent trend against 80% 1RM. Between-side comparisons were not significant for either 60% 1RM (p > 0.270) or 80% 1RM (p > 0.500). Although these results should be interpreted with caution due to the small and heterogeneous sample, our analyses did not reveal meaningful differences in upper-limb muscle activation following breast cancer surgery. Full article

Background/Objectives: Cardiovascular regulation complexity (CRC) is an underexplored health marker in the context of type 2 diabetes mellitus (T2DM). Additionally, associating CRC with physical and cardiorespiratory fitness variables could provide greater insight into how physical conditioning impacts cardiovascular health in the context of T2DM. This study aims to investigate whether the relationship between physical and cardiorespiratory fitness and CRC differs according to the presence or absence of T2DM. Methods: Sixty-eight men were equally divided into the T2DM group (T2DMG; 57 ± 6 years old and 28.4 ± 3.1 kg/m²) and the control group (CG; 52 ± 5 years old and 25.1 ± 2.8 kg/m²). Participants underwent a resting cardiovascular data collection and a cardiopulmonary exercise test on a cycle ergometer. For each group, the relative peak power (W/kg_PEAK) and peak oxygen consumption (VO_2PEAK) were correlated with the CRC indices, namely, Shannon entropy, the complexity index, the normalized complexity index, and the sample entropy from heart period (HP) and systolic arterial pressure (SAP) series. A partial correlation was performed for each group, controlling for age, physical activity level, and metabolic cart. Results: Only the CG showed positive and significant correlations between relative VO_2PEAK and W/kg_PEAK and CRC indices derived from the HP series (0.354 ≤ r ≤ 0.548 and 0.001 ≤ p ≤ 0.047). Correlations with the SAP series were not significant, regardless of the groups. Conclusions: In this sample, there was no positive relationship between physical and cardiorespiratory fitness variables and CRC indices among individuals with T2DM. Further large sample studies are needed to elucidate the factors involved in T2DM that impact CRC. Full article

Microsatellite instability (MSI) testing is frequently used to screen patients for the early detection of Lynch syndrome, the most common hereditary colorectal cancer syndrome. MSI testing compares microsatellite repeat lengths in tumor DNA with those in matched normal tissue from the same patient. Therefore, precise sample identification is critical for obtaining reliable test results. The Penta-C and Penta-D pentanucleotide markers are widely used for sample identification in MSI testing. We investigated instability, defined as allelic mismatches or shifts, discordant fragment sizes, or the appearance of alleles in tumor DNA that were absent in the corresponding normal DNA, in the Penta-C and Penta-D loci across 2609 paired colorectal tumor and matched normal tissue or blood DNA samples. The allele sizes of both markers did not match in 0.3% of microsatellite-stable (MSS) and 12.3% of microsatellite instability-high (MSI-H) patients (p < 0.001, difference in proportions, 12.0% (95% CI, 8.9–15.1%)). Non-matching allele sizes in 12.3% of the MSI-H tumors suggest that other repeat markers may also be unstable and not suitable for sample identification in these tumors. Full article

This study develops and evaluates simultaneous confidence interval procedures for all pairwise differences of coefficients of variation under delta-inverse Gaussian distributions. The objective is to provide reliable comparative inference for relative variability in zero-inflated and highly skewed data, where standard normal-based methods may be unreliable. Five approaches were studied and compared in terms of coverage probabilities and average widths: generalized confidence interval, adjusted generalized confidence interval, fiducial confidence interval, method of variance estimates recovery, and normal approximation. A Monte Carlo simulation study was conducted under varying shape parameters, zero-inflation probabilities, sample sizes, and numbers of populations ($k$ = 3, 6, and 10). Although most methods produced CPs near the nominal 0.95 level, meaningful differences emerged when both coverage accuracy and interval efficiency were considered. The AGCI method consistently delivered stable coverage across parameter settings and remained robust as dimensionality increased. The MOVER approach achieved competitive coverage while frequently yielding narrower intervals. In contrast, GCI occasionally showed mild undercoverage, and FCI tended to produce overly wide intervals. An empirical application to zero-inflated mortality data supports the simulation findings. Overall, AGCI and MOVER provide reliable and practical tools for simultaneous inference on differences in CVs across delta-IG populations. Full article

This study investigates the impact of geopolitical risk measures (aggregate geopolitical risk, geopolitical acts, and geopolitical threats) on 40 global stock market indexes from developed and emerging markets for a sample of 20 years. By employing simultaneous quantile regression and a Two-Stage Quantile-on-Quantile Regression (QQR) framework, we analyze the risk transmission mechanisms across the conditional distribution of stock returns. The empirical results reveal a notable regime-dependent reversal: a negative influence is exerted by geopolitical risk during a bullish market regime, while a counterintuitive positive association is present for the bearish market conditions. This effect is more pronounced for emerging and commodity-rich markets, which may provide a potential hedge during supply-side shocks. Moreover, the QQR analysis focused on the United States of America stock market provides an examination of the different potential transmission mechanisms of geopolitical variants. The results suggest that geopolitical threats (GPRT) represent a persistent factor that negatively affects the market for normal and bullish market regimes, while geopolitical acts (GPRA) represent a tail-risk catalyst that exacerbates losses during severe market crashes. The results remain robust to an alternative specification of returns and indicate the necessity of distinguishing between geopolitical acts and threats from a risk management standpoint, as well as correctly identifying the market regime. Full article

This study aimed to explore the efficacy of oral supplementation with 11 probiotic strains, combined in the strain mix Probatech™ (Centro Sperimentale del Latte S.r.l Strada Provinciale per Merlino, 326839 Zelo Buon Persico (LO), Italy) and delivered through the food supplement PROBAFLOR and how it plays a positive role in maintaining normal intestinal function, providing benefits in healthy adult subjects. A 16-week, randomized, double-blind, placebo-controlled clinical study was conducted starting with 60 participants. Participants were randomly assigned to either the probiotic or placebo group. Participants were asked to provide one faecal sample at the beginning of the study, another one after 12 weeks of supplementation and the final one after 16 weeks. Amplicon 16S rRNA gene sequencing and GC-MS Short-Chain Fatty Acid (SCFA) profiling were performed on the faecal samples. Participants filled out questionnaires to assess their gastrointestinal health and psychological well-being. The overall mean GIQLI scores increased in both groups over time. The increases were significant within both groups but not between groups. Following the administration of PROBAFLOR, Shannon and Simpson diversity indices showed a significant increase at day 90 (week 12) (p < 0.05), demonstrating that the intervention effectively enhanced gut microbiota diversity. A shift in the intestinal microbiota towards SCFA-producing families and genera was observed. Moreover, the change in total and single SCFAs was significantly different between probiotic and placebo groups at the end of the supplementation period. Once-daily consumption of the PROBAFLOR probiotics formula regulated gut microbiota balance by modulating SCFA production. It may be beneficial for gut health, improving defecation habits and satisfaction, normalizing stool frequency, and promoting bacterial metabolism. Full article

Effective climate policy is central to sustainability transitions and to monitoring progress toward sustainable development, yet national climate policy ratings often differ in scope, indicator design, time coverage, and scoring logic, producing inconsistent country assessments. This creates a need for transparent tools that can compare, interpret, and contextualize existing indices rather than rely on any single metric. This paper develops an exploratory protocol for sustainability-oriented cross-index assessment of national climate policy effectiveness. We combine a structured comparative analysis and a SWOT-informed diagnostic synthesis of four representative approaches—the Climate Change Performance Index (CCPI), Climate Action Tracker (CAT), the Climate Laws, Institutions, and Measures Index (CLIMI), and the Climate Policy Measure Index (CPMI)—with a pilot inter-index concordance test using rank-based correlation analysis for a small country sample and a common reference year (2012). The pilot is intended as an illustrative methodological example rather than a generalizable statistical test. The results indicate strong alignment among broad, composite approaches (CCPI, CAT, CLIMI), while an instrument-focused metric (CPMI, centered on carbon pricing and fiscal signals) shows weaker consistency with outcome- and governance-oriented ratings. Building on these insights, we compile an integrated indicator set that links outcomes (GHG levels and trends), structural drivers (energy mix, efficiency), policy instruments (pricing, regulation, subsidies), governance capacity (legal and institutional strength), and enabling conditions (finance, public engagement, international cooperation). We also specify the operational steps of the proposed workflow, including index selection, temporal harmonization, ordinal encoding, concordance analysis, discrepancy diagnosis, indicator mapping, and provisional normalization, weighting, aggregation, and validation rules for future composite implementation. The protocol should therefore be understood as a sustainability-oriented decision support workflow for interpreting agreements and disagreements across existing indices and for supporting more balanced evaluation of low-carbon transitions; a fully aggregated composite index with large-sample validation remains a task for future research. Full article

Methane emissions from end-use installations in residential natural gas systems remain poorly quantified, despite their importance to both safety and climate policies worldwide. While distribution networks and appliances have received research attention, interior piping between the meter and appliances represents a critical knowledge gap. To address this gap, a systematic survey of 473 residential systems in Saarlouis, Germany, was conducted using standardized pressure decay tests (DVGW G 600). Measurements were performed during the installation of gas regulators necessitated by a grid pressure increase from 23 mbar to 55 mbar above ambient. This provided a unique opportunity to assess whole-system leakage under controlled conditions without installation modifications. Leak rates were standardized to reference pressure and converted to methane emissions using measured gas composition, using a linear pressure scaling as a provisional approximation valid for the small pressure differences in the applied test conditions. A total of 411 (86.9%) installations showed no detectable leak rate (LDL: 0.2 $\mathrm{L}{\mathrm{h}}^{-1}$). However, seven systems (1.5%) exceeded 1 $\mathrm{L}{\mathrm{h}}^{-1}$, and one surpassed the unacceptable threshold of 5 $\mathrm{L}{\mathrm{h}}^{-1}$. Mean emissions across all systems were 0.067 [0.041, 0.098] $\mathrm{g}{\mathrm{h}}^{-1}$, with smaller installations showing higher volume-normalized rates. Critically, fewer than 1.48% of systems contributed more than 46% of total emissions, demonstrating a strongly skewed, heavy-tailed distribution. Scaled nationally using Monte Carlo methods accounting for sampling uncertainty and skewed distributions, residential interior piping contributes 12.30 [8.11, 18.55] $\mathrm{G}\mathrm{g}{\mathrm{year}}^{-1}$ to Germany’s methane emissions. These results emphasize the need to include residential leak rates in emission inventories and highlight the efficiency potential of targeted mitigation strategies focused on high-emitting installations under evolving EU methane regulations. Full article

In high-speed spindle motors operating above 10,000 rpm, vibration analysis is essential for detecting mechanical anomalies. However, data scarcity and imbalance, especially for rare fault conditions, limit the performance of deep learning-based anomaly detection models. In this study, we define sample scarcity as the limited availability of real labeled vibration sequences for model training, i.e., only 5000 normal and 5000 faulty samples collected from three spindle motors (10,000 real samples in total). We propose a Transformer-based Variational Autoencoder (T-VAE) to generate realistic triaxial acceleration sequences for spindle motor health monitoring. The model integrates positional encoding and multi-head self-attention to capture long-range temporal dependencies in multivariate time-series data, and applies a KL annealing strategy to improve training stability. Using 5000 normal and 5000 faulty vibration samples collected from three spindle motors, the model generates 100,000 synthetic samples per class, which are used to augment training for a downstream CNN–LSTM classifier. Without augmentation, the classifier achieved 95.73% pass detection on normal samples and 81.40% fail detection on faulty samples. After augmentation with Transformer-VAE, performance increased to 98.07% pass detection for normal data and 97.99% fail detection for faulty data. For prediction, we evaluate on an independent dataset of 25,000 normal and 25,000 faulty sequences obtained from eleven different spindle motors not used in training (cross-spindle). The results demonstrate that the T-VAE effectively alleviates the data scarcity problem and significantly improves anomaly detection accuracy for high-speed spindle motor vibration signals. This approach can be directly applied to predictive maintenance systems in real-world manufacturing environments. Full article