Search Results (10,830)

Importance: Understanding trends in the use of hormonal therapy (HT) for prostate cancer (PCa) is crucial to optimize treatment strategies, particularly for older men with locally advanced and metastatic disease. Objective: To evaluate changes in the patterns of adjuvant and primary HT use over time in older U.S. men diagnosed with locally advanced and metastatic prostate cancer. Design, Setting, and Participants: This cohort study utilized SEER-Medicare data, which covers approximately 48% of the U.S. population and links cancer registry data with Medicare claims, including 149,515 men aged ≥66 years diagnosed with PCa between 2010 and 2019. We analyzed trends in the use of adjuvant HT for higher-risk and primary HT for lower-risk PCa. Multivariable logistic regression models were used to adjust for clinical and demographic factors. Main Outcomes and Measures: The primary outcome was the proportion of men receiving any form of HT within 6 months of PCa diagnosis. HT included injectable Gonadotropin-releasing hormone (GnRH) agonists and antagonists, orchiectomy, and anti-androgens agents. Results: The rate of adjuvant HT in higher-risk PCa patients increased significantly from 53.6% in 2010 to 68.1% in 2019 (p < 0.0001), with a steady rise in the last four years. In contrast, the rate of men with lower-risk disease receiving primary HT declined from 25% in 2010 to 16.9% in 2013, then peaked at 28.2% in 2015, and stabilized between 25% and 27.3% from 2017 to 2019. The overall HT usage increased from 33.5% in 2010 to 45.2% in 2019, showing a consistent increase over the years. These patterns persisted after adjusting for clinical and demographic factors. Conclusions and Relevance: The increasing use of adjuvant HT in higher-risk PCa patients aligns with evolving treatment guidelines, while the stable rate of primary HT in lower-risk patients represents persistent inappropriate use and highlights the need for further efforts to optimize treatment choices. While previous studies focused on men with intermediate-risk PCa receiving radiation therapy, our study broadens the scope to include men who did not undergo radiation therapy, providing a more inclusive view of HT trends. Future research should focus on refining strategies to reduce inappropriate primary HT use and improve adjuvant HT administration. Full article

Current medical informatics systems face critical challenges, including limited scalability across distributed institutions, insufficient real-time AI-driven decision support, and lack of standardized interoperability for heterogeneous medical data exchange. To address these challenges, this paper proposes a novel distributed web system architecture for medical informatics, integrating artificial intelligence techniques and cloud-based services. The system ensures interoperability via HL7 FHIR standards and preserves data privacy and fault tolerance across interconnected medical institutions. A hybrid AI pipeline combining principal component analysis (PCA), K-Means clustering, and convolutional neural networks (CNNs) is applied to diffusion tensor imaging (DTI) data for early detection of neurological anomalies. The architecture leverages containerized microservices orchestrated with Docker Swarm, enabling adaptive resource management and high availability. Experimental validation confirms reduced latency, improved system reliability, and enhanced compliance with medical data exchange protocols. Results demonstrate superior performance with an average latency of 94 ms, a diagnostic accuracy of 91.3%, and enhanced clinical workflow efficiency compared to traditional monolithic architectures. The proposed solution successfully addresses scalability limitations while maintaining data security and regulatory compliance across multi-institutional deployments. This work contributes to the advancement of intelligent, interoperable, and scalable e-health infrastructures aligned with the evolution of digital healthcare ecosystems. Full article

The proposed study explores the consistency of the geometrical character of surfaces under scaling, rotation and translation. In addition to its mathematical significance, it also exhibits advantages over image processing and economic applications. In this paper, the authors used partition-based principal component analysis similar to two-dimensional Sub-Image Principal Component Analysis (SIMPCA), along with a suitably modified atypical wavelet transform in the classification of 2D images. The proposed framework is further extended to three-dimensional objects using machine learning classifiers. To strengthen fairness, we benchmarked against both Random Forest (RF) and Support Vector Machine (SVM) classifiers using nested cross-validation, showing consistent gains when TIFV is included. In addition, we carried out a robustness analysis by introducing Gaussian noise to the intensity channel, confirming that TIFV degrades much more gracefully compared to traditional descriptors. Experimental results demonstrate that the method achieves improved performance compared to traditional hand-crafted descriptors such as measured values and histogram of oriented gradients. In addition, it is found to be useful that this proposed algorithm is capable of establishing consistency locally, which is never possible without partition. However, a reasonable amount of computational complexity is reduced. We note that comparisons with deep learning baselines are beyond the scope of this study, and our contribution is positioned within the domain of interpretable, affine-invariant descriptors that enhance classical machine learning pipelines. Full article

Accelerated artificial aging of zinc oxide (ZnO)-based acrylic artists’ paint, filled with calcium carbonate (CaCO₃) as an extender, was carried out for a total of 1963 h (~8 × 10⁷ lux·h), with assessments at specific intervals. The total color difference ΔE* was <2 (CIELab-76 system) over 1725 h of aging, while the human eye notices color change at ΔE* > 2. Oxidative degradation of organic components in the paint to form volatile products was revealed by attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, micro-Raman spectroscopy, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). It appears that deep oxidation of organic intermediates and volatilization of organic matter may be responsible for the relatively small value of ΔE* color difference during aging of the samples. To elucidate the degradation pathways, principal component analysis (PCA) was applied to the spectral data, revealing: (1) the catalytic role of ZnO in accelerating photodegradation, (2) the Kolbe photoreaction, (3) the decomposition of the binder to form volatile degradation products, and (4) the relative photoinactivity of CaCO₃ compared with ZnO, showing slower degradation in areas with a higher CaCO₃ content compared with those dominated by ZnO. These results provide fundamental insights into formulation-specific degradation processes, offering practical guidance for the development of more durable artist paints and conservation strategies for acrylic artworks. Full article

Drug repurposing refers to the systematic identification of new therapeutic uses for existing drugs. Unlike traditional de novo drug discovery, which is expensive and time-consuming, repurposing leverages compounds with already established safety, pharmacokinetic, and pharmacodynamic profiles. In this study, we propose a drug repositioning model based on low-dimensional transcriptomic representations to investigate the relationship between known anticancer drugs and non-anticancer compounds. We analyzed LINCS L1000 data (1170 drugs; 824 anticancer, 346 non-anticancer). Data were projected with UMAP, PCA, and t-SNE. For each anticancer drug and for each method, we retrieved the k = 5 nearest non-anticancer neighbors and ranked candidates by recurrence frequency across all anticancer queries. We identified Ergometrine, Mupirocin, and (S)-blebbistatin among the most frequent non-anticancer drugs with a close association with drugs known to be anticancer. In addition, we performed a local neighborhood enrichment around the three candidates. Regarding Ergometrine (DB01253), in UMAP, 44/50 neighbors were anticancer (88.0% vs. global baseline 70.5%; hypergeometric BH-adjusted p = 0.0039). Considering (S)-blebbistatin (DB01944) in UMAP, 41/50 neighbors were anticancer (82.0% vs. 70.5%; BH-adjusted p = 0.0435). Mupirocin (DB00410) in UMAP had 44/50 neighbors as anticancer (88.0% vs. 70.5%; BH-adjusted p = 0.0039). Future research should explore the three drugs with in vivo models, investigating their possible synergies. Full article

The result of accelerated development and technological progress is manifested through numerous changes in the labor market, primarily concerning the competencies of future employees. Many of those competencies have symmetrical character. The determinants that may influence the development of specific competencies are variable and dynamic, yet they share the characteristic of transcending temporal and spatial boundaries. In this paper we propose the use of a combination of Principal Component Analysis (PCA) and Fuzzy Analytic Hierarchy Process (FAHP) to rank 21st-century competencies that are developed independently of the formal educational process. Ability to organize and plan, appreciation of diversity and multiculturalism, and ability to solve problems appeared to be the highest-ranked competencies. The development of key competencies is symmetrical to the skills for the labor market. Also, the development of key competencies is symmetrical to the right selection of the quality of media content. The paper proves that the development of key competencies is symmetrical to the level of education of both parents. One of the key findings is that participants with higher levels of media literacy express more readiness for the contemporary labor market. Moreover, the family, particularly parents, exerts a highly significant positive influence on the development of 21st-century competencies. Parents with higher levels of education, in particular, provide a stimulating environment for learning, foster critical thinking, and encourage the exploration of diverse domains of knowledge. Full article

Danube Delta (DD), an ecologically vulnerable site, together with fish populations, which are significant food resources, are largely exposed to heavy metal contamination. This study was developed in the Babina–Cernovca sector of DD in September 2023. Zinc (Zn), and iron (Fe) were identified in water, while copper (Cu), iron (Fe), and manganese (Mn) were in sediments (mud). Proximate composition of the muscle tissues of eight fish species identified in the area was assessed. The muscle was also tested to identify heavy metals contamination. The contamination degree was assessed using bioaccumulation and bioconcentrations factors. The relation between nutritional parameters and metals was tested using bivariate and multivariate analyses. Samples were analyzed by specific laboratory tests, and data were processed using ANOVA, Spearman correlation, Principal Component Analysis (PCA), and hierarchical clustering. S. erythrophthalmus, C. gibelio, and A. alburnus have the highest metal bioaccumulation capacity, exhibiting species-specific accumulation patterns. PCA and clustering analysis reflect the influence of species and environmental factors on heavy metal accumulation in fish tissue. The study integrates the heavy metals content with nutritional parameters in fish muscular tissue, using bivariate and multivariate analysis for assessing fish vulnerability to heavy metals exposure in the Danube River. Full article

Pachysandra terminalis (P. terminalis), a plant belonging to the Buxaceae family, is known as a great source of aminosteroid alkaloids. In a previous communication, we reported on the isolation of a variety of aminosteroids from P. terminalis, which presented interesting activity against the protozoan pathogens, Trypanosoma brucei rhodesiense and Plasmodium falciparum. In the present study, variations in the alkaloid profile of P. terminalis related to seasonal changes as well as differences between plant organs (leaves and twigs) and between plant populations were investigated to prioritize candidates for targeted isolation in further studies. For this purpose, sample material of P. terminalis was collected from the two nearby populations in monthly intervals over one year. The ethanolic (75%) extracts were analyzed using UHPLC/+ESI-QqTOF-MS/MS, and the resulting data converted to variables encoding the intensity of MS signals in particular m/z and retention time (t_R) intervals over the chromatographic runs. The very large and complex data matrix of these <t_R:m/z> variables was evaluated using multivariate data analysis, especially principal component analysis (PCA) and volcano plot analysis of t-test data. The results of these analyses, for the first time, allowed a holistic analysis of variation in the alkaloid profiles in P. terminalis organs over the vegetation period. The evaluation of the PCA scores and loadings plots of principal components 1 through 3, as well as of volcano plots, highlighted 25 different compounds, mostly identified as aminosteroid alkaloids, that were most relevant for the differences between leaves and twigs and between the two populations and mainly determined the changes in their chemical profiles over the vegetation period. Full article

Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection. Full article

This study develops an integrated framework for groundwater pollution source identification by coupling Principal Component Analysis (PCA), Positive Matrix Factorization (PMF), and the Mantel test, with the Qujiang River Basin as a case study. The framework enables a full-process assessment, encompassing qualitative identification, quantitative apportionment, and spatial validation of pollution drivers. Results indicate that groundwater chemistry is primarily influenced by three categories of sources: natural rock weathering, agricultural and domestic activities, and industrial wastewater discharge. Anthropogenic sources account for 73.7% of the total contribution, with mixed agricultural and domestic inputs dominating (38.5%), followed by industrial effluents (35.2%), while natural weathering contributes 26.3%. Mantel test analysis further shows that agricultural and domestic pollution correlates strongly with intensive farmland distribution in the midstream area, natural sources correspond to carbonate outcrops and higher elevations in the upstream, and industrial contributions cluster in downstream industrial zones. By integrating PCA, PMF, and Mantel analysis, this study offers a robust and transferable framework that improves both the accuracy and spatial interpretability of groundwater pollution source identification. The proposed approach provides scientific support for regionalized groundwater pollution prevention and control under complex hydrogeological settings. Full article

Brassinosteroids (BRs) are essential regulators of plant development and stress responses, but the distinct contributions of BR biosynthesis and signaling to hormonal crosstalk remain poorly defined. Here, we investigated the effects of the BR biosynthesis inhibitor brassinazole (BRZ) and the BR-insensitive mutant bri1-6 on endogenous phytohormone profiles in Arabidopsis thaliana. Using multivariate analysis and targeted hormone quantification, we show that BRZ treatment and BRI1 disruption alter hormone balance through partially overlapping but mechanistically distinct pathways. Principal component analysis (PCA) and hierarchical clustering revealed that BRZ and the bri1-6 mutation do not phenocopy each other and that BRZ still alters hormone profiles even in the bri1-6 mutant, suggesting potential BRI1-independent effects. Both BRZ treatment and the bri1-6 mutation tend to influence cytokinins and auxin conjugates divergently. On the contrary, their effects on stress-related hormones converge: BRZ decreases salicylic acid (SA), jasmonic acid (JA), and abscisic acid (ABA) in the WT leaves; similarly, bri1-6 mutants show reduced SA, JA, and ABA. These results indicate that BR biosynthesis and BRI1-mediated perception may contribute independently to hormonal reprogramming, with BRZ eliciting additional effects, possibly via metabolic feedback, compensatory signaling, or off-target action. Hormone correlation analyses revealed conserved co-regulation clusters that reflect underlying regulatory modules. Altogether, our findings provide evidence for a partial uncoupling of BR levels and BR signaling and illustrate how BR pathways intersect with broader hormone networks to coordinate growth and stress responses. Full article

As a key target area in China’s new round of strategic mineral exploration initiatives, Tibet possesses favorable metallogenic conditions shaped by its unique geological evolution and tectonic setting. In this paper, the Saga region of Tibet is the research object, and Level-2A Sentinel-2 imagery is utilized. By applying mixed pixel decomposition, interfering endmembers were identified, and spectral unmixing and reconstruction were performed, effectively avoiding the drawback of traditional methods that tend to remove mineral alteration signals and masking interference. Combined with band ratio analysis and principal component analysis (PCA), various types of remote sensing alteration anomalies in the region were extracted. Furthermore, the fractal box-counting method was employed to quantify the fractal dimensions of the different alteration anomalies, thereby delineating their spatial distribution and fractal structural characteristics. Based on these results, two prospective mineralization zones were identified. The results indicate the following: (1) In areas of Tibet with low vegetation cover, applying spectral mixture analysis (SMA) effectively removes substantial background interference, thereby enabling the extraction of subtle remote sensing alteration anomalies. (2) The fractal dimensions of various remote sensing alteration anomalies were calculated using the fractal box-counting method over a spatial scale range of 0.765 to 6.123 km. These values quantitatively characterize the spatial fractal properties of the anomalies, and the differences in fractal dimensions among alteration types reflect the spatiotemporal heterogeneity of the mineralization system. (3) The high-potential mineralization zones identified in the composite contour map of fractal dimensions of alteration anomalies show strong spatial agreement with known mineralization sites. Additionally, two new prospective mineralization zones were delineated in their periphery, providing theoretical support and exploration targets for future prospecting in the study area. Full article

We report the study of seven commercially available rosehip oils (Rosa canina L.) using GC-MS, colorimetry (CIELab), UV-VIS, FTIR, and 3D EEM fluorescence spectroscopy, including using a smartphone spectrometer. GC-MS revealed two groups of oil samples with different chemical constituents: ω-6-dominant with 45–51% α-linolenic acid (samples S1, S2, and S5–S7) and ω-3-dominant with 47–49% α-linolenic, 7.3–19.1% oleic, 1.9–2.8% palmitic, 1.0–1.8% stearic, and 0.1–0.72% arachidic acid (S3, S4). In S1 PUFA content was found to be ~75% with ω-6/ω-3 ≈ 2:1. Favorable lipid indices of AI 0.0197–0.0302, TI 0.0208–0.0304, and h/H 33.0–50.6 were observed. The highest h/H (50.55) was observed in S5 and the lowest TI (0.0208) in S3. FTIR showed characteristic lines at ~3021, 2929/2853, 1749, and ~1370 cm⁻¹, and PCA yielded 60–80% variation and separated S1 from the rest of the samples, while the clusters grouped S5 and S6. The smartphone spectrometer also reproduced the individual differences in sample volumes ≤ 1 µL under 355–395 nm UV excitation. The non-destructive optical markers reflect the fatty acid profile and allow fast low-cost identification and quality control. An integrated control method including routine optical screening, periodic CG-MS verification, and chemometric models to trace oxidation and counterfeiting is suggested. Full article

This study aims to explore the utility of artificial intelligence (AI) in classifying dementia severity based on gait analysis data and to examine how machine learning (ML) can address the limitations of conventional statistical approaches. The study included 34 individuals with mild cognitive impairment (MCI), 25 with mild dementia, 26 with moderate dementia, and 54 healthy controls. A support vector machine (SVM) classifier was employed to categorize dementia severity using gait parameters. As complexity and high dimensionality of gait data increase, traditional statistical methods may struggle to capture subtle patterns and interactions among variables. In contrast, ML techniques, including dimensionality reduction methods such as principal component analysis (PCA) and gradient-based feature selection, can effectively identify key gait features relevant to dementia severity classification. This study shows that ML can complement traditional statistical analyses by efficiently handling high-dimensional data and uncovering meaningful patterns that may be overlooked by conventional methods. Our findings highlight the promise of AI-based tools in advancing our understanding of gait characteristics in dementia and supporting the development of more accurate diagnostic models for complex or large datasets. Full article

Automated quality control is critical in modern manufacturing, especially for metallic cast components, where fast and accurate surface defect detection is required. This study evaluates classical Machine Learning (ML) algorithms using extracted statistical parameters and deep learning (DL) architectures including ResNet50, Capsule Networks, and a 3D Convolutional Neural Network (CNN3D) using 3D image inputs. Using the Dataset Original, ML models with the selected parameters achieved high performance: RF reached 99.4 ± 0.2% precision and 99.4 ± 0.2% sensitivity, GB 96.0 ± 0.2% precision and 96.0 ± 0.2% sensitivity. ResNet50 trained with extracted parameters reached 98.0 ± 1.5% accuracy and 98.2 ± 1.7% F1-score. Capsule-based architectures achieved the best results, with ConvCapsuleLayer reaching 98.7 ± 0.2% accuracy and 100.0 ± 0.0% precision for the normal class, and 98.9 ± 0.2% F1-score for the affected class. CNN3D applied on 3D image inputs reached 88.61 ± 1.01% accuracy and 90.14 ± 0.95% F1-score. Using the Dataset Expanded with ML and PCA-selected features, Random Forest achieved 99.4 ± 0.2% precision and 99.4 ± 0.2% sensitivity, K-Nearest Neighbors 99.2 ± 0.0% precision and 99.2 ± 0.0% sensitivity, and SVM 99.2 ± 0.0% precision and 99.2 ± 0.0% sensitivity, demonstrating consistent high performance. All models were evaluated using repeated train-test splits to calculate averages of standard metrics (accuracy, precision, recall, F1-score), and processing times were measured, showing very low per-image execution times (as low as $3.69\times {10}^{-4}$ s/image), supporting potential real-time industrial application. These results indicate that combining statistical descriptors with ML and DL architectures provides a robust and scalable solution for automated, non-destructive surface defect detection, with high accuracy and reliability across both the original and expanded datasets. Full article