Puyang Vocational and Technical College

Abstract This study investigates the factors that influence college students’ intention to use artificial intelligence generated content (AIGC) technology in design learning. An extended technology use and diffusion model is proposed and validated by integrating the artificial intelligence device use acceptance (AIDUA) model with the innovation diffusion theory (IDT). The present study collected data from 385 Chinese college students majoring in design through online surveys. The proposed model, which includes technology concerns, emotional acceptance, and behavioral transformation, was empirically tested using structural equation modeling (SEM) on data collected from students across different academic levels. The research findings suggest that, in the first stage, relative advantage and compatibility exert a significant and positive influence on both performance expectancy and effort expectancy. However, complexity negatively affects both. In the second stage (emotional acceptance), effort expectancy has a highly positive and significant influence on the adoption and diffusion. Conversely, the impact of performance expectancy on use and diffusion is not substantial. In the final stage (behavioral transformation), both social influence and individual innovation positively and significantly impact the use and diffusion of AIGC. Thus, the empirical results support the integration of AIDUA and IDT. This study provides a conceptual AIGC use and diffusion framework that other researchers can use to investigate AIGC-related topics in design learning.

Accurate differentiation between luminal A and B subtypes of breast cancer is critical for selecting therapeutic strategies. However, current approaches rely predominantly on invasive biopsy and immunohistochemical (IHC) analysis. Therefore, the development of non-invasive imaging-based methods capable of reliably classifying tumor subtypes remains an urgent task. To develop and validate a hybrid classification model combining radiomic and deep learning features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate between luminal A and B subtypes of invasive breast cancer. The study included 312 women from China, Russia and Bulgaria with confirmed luminal subtypes of breast cancer. All patients underwent standardized pre-treatment DCE-MRI, and subtypes were determined using IHC. Tumors were semi-automatically segmented, and radiomic features were extracted using PyRadiomics. Additionally, deep features were extracted from DCE-MRI using a 3D ResNet-50 convolutional neural network. Three models were constructed: a radiomics-based model, a deep learning-based model, and a hybrid model that integrated both approaches using a stacking ensemble method. Model performance was evaluated using AUC, sensitivity, specificity, and other metrics on a test dataset and an independent external validation cohort (n = 148). SHAP and Grad-CAM techniques were applied for model interpretability. The hybrid model significantly outperformed the individual approaches, achieving an AUC of 0.921, sensitivity of 88.6%, and specificity of 89.7% on the test dataset. Performance remained robust in the external validation cohort (AUC = 0.903). Statistical tests (DeLong and bootstrapping) confirmed the significance of these differences. The most important contributors were radiomic features related to shape and texture (e.g., entropy, sphericity) and high-level deep features. Visualizations highlighted clinically relevant model attention areas. The proposed hybrid approach represents a clinically applicable, non-invasive method for classifying breast cancer subtypes, potentially complementing or partially replacing biopsy in selected cases. It enhances diagnostic accuracy while maintaining interpretability. Future work will focus on prospective validation and integration with genomic and clinical data within the framework of precision oncology.

Pancreatic cancer remains one of the most lethal malignancies, characterized by rapid progression, late-stage diagnosis, and resistance to current therapies. A major contributor to its poor prognosis is the highly immunosuppressive tumor microenvironment (TME), where regulatory T cells (Tregs) suppressing effector T cells (Teffs). The ratio of Teffs to Tregs (Teff/Treg ratio) has emerged as a pivotal biomarker that reflects immune balance within the TME, with significant prognostic and therapeutic relevance. A low Teff/Treg ratio correlates with poor clinical outcomes, immunotherapy resistance, and tumor progression. Elucidating the mechanisms regulating that regulate this ratio such as immune checkpoint pathways, cytokine-mediated signaling, and stromal interactions offers promising avenues to restore anti-tumor immunity. This review critically evaluates the prognostic values of the Teff/Treg ratio in pancreatic cancer, highlights emerging strategies to rebalance this axis, and outlines current challenges and research gaps. Standardizing measurement methodologies and integrating Teff/Treg analysis into pancreatic cancer-specific research are essential next steps toward precision immunotherapy.

Biomimetic membrane interface engineering constructs functionalized detection platforms by incorporating natural cell membranes, synthetic lipids, or hybrid membranes. The primary purpose of this strategy is to minimize background interference while leveraging intrinsic membrane properties for effective target interaction. Applications are diverse, ranging from the high-purity separation of circulating tumor cells (CTCs) to the efficient isolation of extracellular vesicles (EVs), as well as the subsequent detection of EV-derived contents (e.g., miRNA, protein, and mRNA) via membrane fusion mechanisms. Conceptually, this approach serves as a robust bridge between synthetic materials and biological systems. Its major advantages lie in the significant reduction of non-specific binding and the unique capability to facilitate both target capture and internal cargo analysis. However, challenges such as complex preparation processes, stability issues, and the dependence on functional modifications to address significant tumor heterogeneity remain to be resolved. This review summarizes recent progress, analyzes these critical issues, and outlines future directions.

Abstract Sulfonylurea herbicides, widely used in agriculture, persisted long in soil, has raised considerable environmental concerns. Microbial remediation is effective in decomposing organic pollutants. However, researches on microbe-plant combined remediation of contaminated soil based on rhizosphere environment remains limited. In this study, the influence of cucumber root exudates on degrading strain Chenggangzhangella sp. BI-1 was investigated. Additionally, the treatment of cucumber seeds coating with strain BI-1 was employed to study its effects on cucumber stress resistance, herbicides degradation in rhizosphere soil, and the structure of the bacterial community. The results showed that root exudates significantly enhanced BI-1 degradation efficiency towards tribenuron-methyl, chlorimuron-ethyl, and bensulfuron-methyl. Liquid chromatography-mass spectrometry analysis identified 15 amino acids, 55 organic acids, and 4 vitamins in root exudates. Among these, lysine, glutamic acid, vitamin C, succinic acid, and tartaric acid significantly promoted colonization of strain BI-1 on roots. Transcriptomic analysis revealed that 1524 genes in strain BI-1 were significantly upregulated by root exudates, while 1643 genes downregulated, associated with nutrient metabolism, chemotaxis and genetic information processing. After coating treatment under the optimal conditions of 0.8 g·10 mL-1 carboxymethyl cellulose sodium, inoculation-seeds ratio of 1:35 and coating time of 57 min, cucumber seeds were planted in soil contaminated (3.0 mg/kg), the degradation rates reached 75.3% (tribenuron-methyl), 71.3% (chlorimuron-ethyl), and 77.7% (bensulfuron-methyl) after 14 d, which were significantly higher than those in the non-coating treatment. Meanwhile, the coating treatment reduced the malondialdehyde content in cucumber leaves and increased the activities of peroxidase, superoxide dismutase and catalase. It also significantly altered the relative abundances of cucumber rhizosphere bacteria, improving the richness and diversity of bacterial community. Functional prediction via FAPROTAX indicated that the coating treatment increased the relative abundance of functions such as aerobic chemoheterotrophy, thereby facilitating herbicides degradation. This study could provide a basis for the bioremediation of contaminated soils.