Search Results (56,297)

Objectives: To identify factors correlated with post-surgery residue in cases of breast cancer incidentally found via vacuum-assisted excision (VAE). Methods: A total of 6083 patients were enrolled in a retrospective study. Ultrasound evaluation and ultrasound-guided VAE were performed on these patients. According to the pathology of VAE, 53 patients with incidentally found breast cancer were included in the final analysis. Either breast-conserving surgery or mastectomy was performed. The maximal diameter, depth, location, BIRADS category, and Adler’s grade of all lesions before VAE was reviewed and recorded. VAE and post-surgery pathologies were used as gold standards. Either Pearson’s chi-square test or Fisher’s exact test was used for comparison of categorical variables. Results: The mean age of the enrolled patients was 49 years (IQR: 43–55 years). The mean maximal diameter of the lesions was 11.3 mm (IQR: 7–15 mm). There were twenty-eight ductal carcinomas in situ, twelve invasive ductal carcinomas, five lobular carcinomas in situ, two invasive lobular carcinomas, four intraductal papillary carcinomas, and two mucinous carcinomas. Post-surgery pathology showed 15 cases with residual cancer and 38 cases with no residual cancer. The maximal diameter, depth, and pathology derived via VAE were statistically correlated with post-surgery residue (p < 0.05). Conclusions: Small incidentally found noninvasive carcinomas located comparatively deep in the breast could be totally excised by ultrasound-guided vacuum-assisted excision. Both large and superficially invasive carcinomas were more likely to be associated with residue. Full article

Studies have shown that the iron concentration in the peritoneal fluid of women is associated with the severity of endometriosis. Therefore, investigation of iron metabolism-related genes (IM-RGs) in endometriosis holds significant implications for both prevention and therapeutic strategies in affected patients. Differentially expressed IM-RGs (DEIM-RGs) were identified by intersecting IM-RGs with differentially expressed genes derived from GSE86534. Mendelian randomization analysis was employed to determine DEIM-RGs causally associated with endometriosis, with subsequent verification through sensitivity analyses and the Steiger test. Biomarkers associated with IM-RGs in endometriosis were validated using expression data from GSE86534 and GSE105764. Functional annotation, regulatory network construction, and immunological profiling were conducted for these biomarkers. Single-cell RNA sequencing (scRNA-seq) (GSE213216) was utilized to identify distinctively expressed cellular subsets between endometriosis and controls. Experimental validation of biomarker expression was performed via reverse transcription–quantitative polymerase chain reaction (RT-qPCR). BMP6 and SLC48A1, biomarkers indicative of cellular BMP response, were influenced by a medicus variant mutation that inactivated PINK1 in complex I, concurrently enriched by both biomarkers. The lncRNA NEAT1 regulated BMP6 through hsa-mir-22-3p and hsa-mir-124-3p, while SLC48A1 was modulated by hsa-mir-423-5p, hsa-mir-19a-3p, and hsa-mir-19b-3p. Immune profiling revealed a negative correlation between BMP6 and monocytes, whereas SLC48A1 displayed a positive correlation with activated natural killer cells. scRNA-seq analysis identified macrophages and stromal stem cells as pivotal cellular components in endometriosis, exhibiting altered self-communication networks. RT-qPCR confirmed elevated expression of BMP6 and SLC48A1 in endometriosis samples relative to controls. Both BMP6 and SLC48A1 were consistently overexpressed in endometriosis, reinforcing their potential as biomarkers. Moreover, macrophages and stromal stem cells were delineated as key contributors. These findings provide novel insights into therapeutic and preventive approaches for patients with endometriosis. Full article

Pipelines and power cables are critical infrastructures in coastal areas for transporting energy resources from offshore renewable installations to onshore grids. It is important to investigate the hydrodynamic forces on pipelines and cables and their surrounding flow fields, which are highly related to their on-bottom stability. The time-varying hydrodynamic forces coefficients and unsteady surrounding flows of a near-seabed pipeline subjected to a wave-induced oscillatory boundary layer flow are studied through numerical simulations. The Keulegan–Carpenter numbers of the oscillatory flow are up to 400, which are defined based on the maximum undisturbed near-bed orbital velocity, the pipeline diameter and the period of the oscillatory flow. The investigated Reynolds number is set to 1 × 10⁴, defined based on U_w and D. The influences of different seabed roughness ratios k_s/D (where k_s is the Nikuradse equivalent sand roughness) up to 0.1 on the hydrodynamic forces and the flow fields are considered. Both a wall-mounted pipeline with no gap ratio to the bottom wall and a pipeline with different gap ratios to the wall are investigated. The correlations between the hydrodynamic forces and the surrounding flow patterns at different time steps during one wave cylinder are analyzed by using the force partitioning method and are discussed in detail. It is found that there are influences of the increasing k_s/D on the force coefficients at large KC, while for the small KC, the inertial effect from the oscillatory flow dominates the force coefficients with small influences from different k_s/D. The FPM analysis shows that the elongated shear layers from the top of the cylinder contribute to the peak values of the drag force coefficients. Full article

Long bone fractures are breaks or cracks in a long bone of the body typically caused by trauma like a fall, sport injury, accidents etc. This study investigates the effectiveness of experimental methods for fast and safe healing of long bone fractures in humans, highlighting both their advantages and disadvantages, respectively finding the most effective and safe methods for evaluating the types of fixators that can be used in the consolidation of fractured long bones. As for the preliminary data, numerical methods and applied mathematics were used to address this problem. After collecting of preliminary data there were performed a series of experimental analysis as follows: Electrical Strain Gauges (ESGs); the Moiré Fringes method; Photo-Elasticity, with the particular technique thereof, the so-called Photo-Stress method; Holographic Interferometry (HI); Speckle Pattern Interferometry (ESPI) and Shearography; and Video Image Correlation (VIC), which is also called Digital Image Correlation (DIC). By analyzing different methods, the following two methods resulted to be widely applicable, namely, ESG and DIC/VIC. The findings highlight the net advantages regarding the objective choice of these types of fixators, thereby contributing to a possible extension of these approaches for the benefit of medical surgical practice Full article

This study presents a comparative investigation of combustion and emission characteristics in a two-stroke MAN 5S35ME-B9.5 marine diesel engine equipped with a Controllable Pitch Propeller and an Exhaust Gas Recirculation system. Experimental data were obtained from both factory shop tests conducted under the IMO NOx Technical Code 2008 E2 cycle and sea trials performed onboard the T/S Baek-Kyung. Engine performance was evaluated under Tier II-FB, ecoEGR, and Tier III modes, focusing on specific fuel oil consumption, peak cylinder pressure, exhaust gas temperature, and regulated emissions. Results indicate that Tier III achieved the greatest NOx abatement, reducing emissions by up to 76.4% (1464 to 346 ppm), but with penalties of 16.8% higher SFOC and 45.2% higher CO₂ concentration. EcoEGR provided a more favorable compromise, reducing NOx by 52.3% while limiting SFOC increases to ≤15.4% and CO₂ increases to ≤30.9%. Strong correlations were observed between NOx, Pmax, and exhaust gas temperature, reaffirming fundamental trade-offs, while O₂ and CO correlations showed greater variability under sea operation. Despite operational scatter, sea trial results reproduced the key patterns observed in shop tests, confirming robustness across conditions. Overall, this correlation-based analysis provides quantified evidence of performance–emission trade-offs and offers a practical foundation for optimizing CPP-equipped two-stroke engines under varying EGR strategies. Full article

Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized cities, and relatively underdeveloped regions. However, the mechanism underlying the interaction between landscape patterns and carbon emissions across such gradients remains inadequately understood. This study utilizes nighttime light, land use and carbon emissions datasets, employing XGBoost, CatBoost, LightGBM and a stacking ensemble model to analyze the impacts and driving factors of land use changes on carbon emissions in the Yangtze River Basin from 2002 to 2022. The results showed: (1) The stacking ensemble learning model demonstrated the best predictive performance, with a coefficient of determination (R²) of 0.80, a residual prediction deviation (RPD) of 2.22, and a root mean square error (RMSE) of 4.46. Compared with the next-best models, these performance metrics represent improvements of 19.40% in R² and 28.32% in RPD, and a 22.16% reduction in RMSE. (2) Based on SHAP feature importance and Pearson correlation analysis, the primary drivers influencing CO₂ net emissions in the Yangtze River Basin are GDP per capita (GDPpc), population density (POD), Tertiary industry share (TI), land use degree comprehensive index (LUI), dynamic degree of water-body land use (K_water), Largest patch index (LPI), and number of patches (NP). These findings indicate that changes in regional landscape patterns exert a significant effect on carbon emissions in strategic economic regions, and that stacked ensemble models can effectively simulate and interpret this relationship with high predictive accuracy, thereby providing decision support for regional low-carbon development planning. Full article

Ammonia–coal co-combustion has emerged as a promising strategy for reducing carbon emissions from coal utilization, although its underlying reaction mechanisms remain insufficiently understood. The Chemkin simulation of zero-dimensional homogeneous reaction model and entrained flow reaction model was employed here, and the ROP (rate of production) and sensitivity analysis was performed for analyzing in-depth reaction mechanisms. The nitrogen conversion pathways were revealed, and the mechanisms were simplified. Based on simplified mechanisms, molecular-level reaction pathways and thermochemical conversion networks of nitrogen-containing precursors were established. The results indicate that NO emissions peak at a 30% co-firing ratio, while N₂O formation increases steadily. The NH radical facilitates NO reduction to N₂O, with NH + NO → N₂O + H identified as the dominant pathway. Enhancing NNH formation and suppressing NCO intermediates are key to improving nitrogen conversion to N₂. This paper quantifies the correlation between NO_x precursors such as HCN and NH₃ and intermediates such as NCO and NNH during ammonia–coal co-firing and emphasizes the important role of N₂O. These insights offer a molecular-level foundation for designing advanced ammonia–coal co-combustion systems aimed at minimizing NO_x emissions. Full article

Hepatocellular carcinoma (HCC) ranks among the most prevalent malignancies globally. Although treatment strategies have improved, the prognosis for patients with advanced HCC remains unfavorable. Tumor-associated macrophages (TAMs) play a dual role, exhibiting both anti-tumor and pro-tumor functions. In this study, we analyzed single-cell RNA sequencing data from 10 HCC tumor cores and 8 adjacent non-tumor liver tissues available in the dataset GSE149614. Using dimensionality reduction and clustering approaches, we identified six major cell types and nine distinct TAM subtypes. We employed Monocle2 for cell trajectory analysis, hdWGCNA for co-expression network analysis, and CellChat to investigate functional communication between TAMs and other components of the tumor microenvironment. Furthermore, we estimated TAM abundance in TCGA-LIHC samples using CIBERSORT and observed that the relative proportions of specific TAM subtypes were significantly correlated with patient survival. To identify TAM-related genes influencing patient outcomes, we developed a high-dimensional, gene-based transformer survival model. This model achieved superior concordance index (C-index) values across multiple datasets, including TCGA-LIHC, OEP000321, and GSE14520, outperforming other methods. Our results emphasize the heterogeneity of tumor-associated macrophages in hepatocellular carcinoma and highlight the practicality of our deep learning framework in survival analysis. Full article

Background: Evidence on how everyday walking and sleep relate to mood in health profession students from Central–Eastern Europe remains limited. Methods: We conducted a cross-sectional study among 277 Romanian medical students. Data were collected using validated instruments for physical activity (IPAQ-SF), sleep quality (PSQI), and depressive/anxiety symptoms (HADS). Associations were examined using bivariate and multivariable regression models, including sex-stratified analyses. Results: In bivariate analysis, total physical activity was inversely correlated with depressive symptoms (ρ = −0.19, p < 0.001). However, in the multivariable model, this effect was not statistically significant after controlling for other factors. Poor sleep quality emerged as the dominant independent predictor of both depression (β = 0.37, p < 0.001) and anxiety (β = 0.40, p < 0.001). Walking time and frequency were specifically protective against depressive symptoms. Sex-stratified analyses revealed distinct patterns: female students benefited more from walking, whereas male students showed stronger associations between overall physical activity and lower depressive symptoms. Conclusions: Within the constraints of a cross-sectional design, this study provides novel evidence from Eastern Europe that sleep quality and physical activity are central to student mental health. Psychological benefits of walking appear sex-specific, and the null mediation finding suggests benefits operate via direct or unmodelled pathways. Sleep is a critical independent target for tailored, lifestyle-based strategies. Full article

Soil samples from the urban area of Novi Sad were analyzed to determine the total concentrations of heavy metals including Cr, Pb, Cu, Zn, As, Mn, Ni, Co, Cd and Fe. In addition, leaching tests according to CEN 12457-2—Milli-Q deionized leaching procedure and ISO/TS 21268-2—CaCl₂ solution leaching procedure were conducted to assess the mobility of these metals. Multivariate statistical methods, including Pearson’s correlation, Principal Component Analysis (PCA) and Cluster Analysis, were applied to identify pollution sources and grouping patterns among elements. The results revealed a distinct clustering of Pb and Zn, separate from other metals, indicating their predominant origin from anthropogenic activities. Contamination Factor (CF), Pollution Load Index (PLI), and Geoaccumulation Index (Igeo) were calculated to evaluate the degree of pollution. Combining total concentration, mobility, and multivariate analyses offers a more comprehensive insight into the extent and origin of pollution in the urban area of Novi Sad. The results obtained are valuable for evaluating the soil conditions in the Western Balkans, which have been recognized as a necessity by the EU. Full article

Under the framework of the “dual carbon” goals, promoting the coordinated development of carbon emission efficiency, carbon sink capacity, and high-quality growth has become a critical issue for regional sustainability. Using panel data from 2006 to 2021, this study systematically investigates the three-dimensional coupling coordination among carbon emission efficiency, carbon sink capacity, and high-quality development in the Greater Chang-Zhu-Tan urban agglomeration. The spatiotemporal evolution, spatial correlation characteristics, and influencing factors of the coupling coordination were also explored. The results indicate that the coupling coordination system exhibits an evolutionary trend of overall stability with localized differentiation. The overall coupling degree remains in the “running-in” stage, while the coordination level is still in a marginally coordinated state. Spatially, the pattern has shifted from “northern leadership” to “multi-polar support,” with Yueyang achieving intermediate coordination, four cities including Changde reaching primary coordination, and three cities including Loudi remaining imbalanced. Spatial correlation has weakened from significant to insignificant, with Xiangtan showing a “low–low” cluster and Hengyang displaying a “high–low” cluster. The evolution of hot and cold spots has moved from marked differentiation to a more balanced distribution, as reflected by the disappearance of cold spots. The empirical analysis confirms a three-dimensional coupling mechanism: ecologically rich regions attain high coordination through carbon sink synergies; economically advanced areas achieve decoupling through innovation-driven development; while traditional industrial cities, despite facing the “green paradox,” demonstrate potential for leapfrog progress through transformation. Among the influencing factors, industrial structure upgrading emerged as the primary driver of spatial differentiation, though with a negative impact. Government support also exhibited a negative effect, whereas the interaction between environmental regulation and both government support and economic development was found to be significant. Full article

COVID-19 remains a global health challenge, with severe cases often leading to complications and fatalities. The objective of this study was to assess supervised machine learning algorithms for predicting severe COVID-19 based on demographic, clinical, biochemical, and genetic variables, with the aim of identifying the most informative prognostic markers. For Machine Learning (ML) analysis, we utilized a dataset comprising 226 observations with 68 clinical, biochemical, and genetic features collected from 226 patients with confirmed COVID-19 (54—moderate, 142—severe and 30 with mild disease). The target variable was disease severity (mild, moderate, severe). The feature set included demographic variables (age, sex), genetic markers (single-nucleotide polymorphisms (SNPs) in FGB (rs1800790), NOS3 (rs2070744), and TMPRSS2 (rs12329760)), biochemical indicators (IL-6, endothelin-1, D-dimer, fibrinogen, among others), and clinical parameters (blood pressure, body mass index, comorbidities). The target variable was disease severity. To identify the most effective predictive models for COVID-19 severity, we systematically evaluated multiple supervised learning algorithms, including logistic regression, k-nearest neighbors, decision trees, random forest, gradient boosting, bagging, naïve Bayes, and support vector machines. Model performance was assessed using accuracy and the area under the receiver operating characteristic curve (AUC-ROC). Among the predictors, IL-6, presence of depression/pneumonia, LDL cholesterol, AST, platelet count, lymphocyte count, and ALT showed the strongest correlations with severity. The highest predictive accuracy, with negligible error rates, was achieved by ensemble-based models such as ExtraTreesClassifier, HistGradientBoostingClassifier, BaggingClassifier, and GradientBoostingClassifier. Notably, decision tree models demonstrated high classification precision at terminal nodes, many of which yielded a 100% probability for a specific severity class. Full article

Background: Interleukin-6 (IL-6) is a multifunctional cytokine implicated in various inflammatory and immune-mediated processes. Its involvement in systemic lupus erythematosus (SLE) has been increasingly investigated, particularly related to disease activity and tissue damage. This study aimed to quantify serum IL-6 levels in patients with SLE and assess their associations with clinical manifestations and laboratory parameters. Methods: A total of 88 patients diagnosed with SLE and 87 matched healthy controls were included. Serum IL-6 concentrations were measured by ELISA. Clinical data, SLEDAI scores, organ involvement, inflammatory markers, and autoantibody profiles were recorded. The statistical analysis involved non-parametric testing, correlation analysis, and linear regression. Results: IL-6 concentrations were higher in SLE patients than in controls (7.46 ± 6.73 vs. 5.30 ± 10.89 pg/mL). Significantly increased IL-6 levels were observed in patients with active disease (SLEDAI ≥ 6; p = 0.025) and renal (p = 0.001) involvement. Positive correlations were identified between IL-6 and ESR, creatinine, ANA, and specific autoantibodies (anti-dsDNA, SSA, and SSB). IL-6 also correlated with IL-10 (p = 0.010) but showed no significant association with IL-17A, TNF-α, CRP, or complement levels. Conclusions: Elevated IL-6 levels are associated with greater disease activity and specific organ involvement in SLE. These findings highlight IL-6 as a measurable indicator of immunological and clinical disease expression, supporting its relevance in disease monitoring. Full article

Fatigue fracture is the predominant failure mode in welded joints, where complex stress distributions and stress gradient effects at critical joint regions present major challenges for fatigue design. In civil engineering, the diversity of welded joint configurations, large structural spans, and complex loading conditions make it essential to investigate the influence of geometrical size effects on fatigue performance to ensure structural safety. This study focuses on welded T-joints and examines how variations in web plate thickness, weld toe size, and welding angle affect their fatigue behavior through experimental testing. The results show that fatigue life curves fitted using the Mises stress amplitude exhibit higher accuracy than those based on the normal stress amplitude used in current design codes. Pearson correlation analysis indicates that the influences of the geometrical parameters on fatigue life are mutually independent. Furthermore, analysis of the coefficient of variation reveals that welding angle has the greatest effect on fatigue life, whereas weld toe size exerts the least influence. Full article

Gliomas are aggressive brain tumours with diverse histological and molecular features, complicating accurate diagnosis and treatment. Dysregulated lipid metabolism contributes to glioma progression, and analysing lipid profiles in plasma and tissue may enhance diagnostic and prognostic accuracy. This study investigated lipid dysregulation to identify key lipid signatures that distinguish glioma from other brain diseases and examined the associations between lipid biomarkers in glioma tissue and plasma. Biospecimens from 11 controls and 72 glioma patients of varying grades underwent lipidomic profiling using liquid chromatography-mass spectrometry. Univariate and multivariate analyses identified differentially abundant lipids, and correlation analysis evaluated the associations between tissue and plasma biomarkers. Lipidomic analysis revealed distinct lipid profiles in the tissues and plasma of glioma patients compared to those of controls. Prominent lipid metabolites in glioma tissues included LPC 21:3 (AUC = 0.925), DG 43:11 (AUC = 0.906), and PC 33:1 (AUC = 0.892), which served as effective biomarkers. Conversely, in plasma, lipid metabolites such as phosphatidylethanolamine (PE 21:3, AUC = 0.862), ceramide-1-phosphate (CerP 26:1, AUC = 0.861), and sphingomyelin (SM 24:3, AUC = 0.858) were identified as the most promising lipid biomarkers. Significant positive and negative correlations were observed between the tissue and plasma lipid biomarkers of glioma patients. Lipidomic profiling revealed aberrant lipid classes and pathways in glioma tissues and plasma, enhancing understanding of glioma heterogeneity and potential clinical applications. Full article