Yu Wang’s research while affiliated with Guizhou Education University and other places

Hexavalent chromium (Cr(VI)), a highly toxic and carcinogenic contaminant, presents a significant hazard to aquatic ecosystems and human health. Developing environmentally friendly, cost-effective, biodegradable, and easily recyclable adsorbents is critical for efficient Cr(VI) removal. Here, we present an innovative solution using a Mg/Al layered double hydroxide (LDH)-modified sphagnum cellulose gel (MgAl/LDH@SMCG), prepared by pre-treating sphagnum cellulose, crosslinking with polyvinyl alcohol, and doping with LDH. The resulting porous composite gel features abundant -COOH and -OH chelating groups, significantly enhancing its adsorption capacity and structural stability. The material’s structure and surface modifications were systematically characterized using SEM, TGA, FT-IR, and XPS. Batch adsorption experiments were conducted to assess the influence of adsorbent dosage, initial Cr(VI) concentration, pH, contact time, and temperature on performance. Adsorption kinetics, isotherms, and thermodynamics analyses revealed a primary mechanism of monolayer chemical adsorption, with experimental data closely fitting the Freundlich isotherm and pseudo-second-order kinetic models. The modified gel exhibits increased surface roughness and adsorption sites, resulting in markedly improved Cr(VI) removal efficiency. This study not only provides theoretical insights into Cr(VI) adsorption but also highlights the potential of LDH-functionalized cellulose gels for heavy metal wastewater treatment, offering a sustainable pathway for addressing global water contamination challenges.

The rapid adoption of Artificial Intelligence (AI) in medical imaging raises fairness and privacy concerns across demographic groups, especially in diagnosis and treatment decisions. While federated learning (FL) offers decentralized privacy preservation, current frameworks often prioritize collaboration fairness over group fairness, risking healthcare disparities. Here we present FlexFair, an innovative FL framework designed to address both fairness and privacy challenges. FlexFair incorporates a flexible regularization term to facilitate the integration of multiple fairness criteria, including equal accuracy, demographic parity, and equal opportunity. Evaluated across four clinical applications (polyp segmentation, fundus vascular segmentation, cervical cancer segmentation, and skin disease diagnosis), FlexFair outperforms state-of-the-art methods in both fairness and accuracy. Moreover, we curate a multi-center dataset for cervical cancer segmentation that includes 678 patients from four hospitals. This diverse dataset allows for a more comprehensive analysis of model performance across different population groups, ensuring the findings are applicable to a broader range of patients.

Oil spills pose a major environmental challenge, highlighting the urgent need for effective materials capable of achieving efficient oil-water separation to mitigate their detrimental impacts. While various bio-based and synthetic adsorbents have been explored for this purpose, existing materials often suffer from low adsorption capacity, poor reusability, limited hydrophobicity, or environmental concerns. In particular, natural bio-based materials frequently exhibit inherent hydrophilicity, limiting their effectiveness in selective oil adsorption. To address this gap, we developed a novel bio-based oil adsorbent derived from sphagnum moss, modified via sequential pretreatment with hydrogen peroxide and sodium hydroxide, followed by chemical functionalization with silane. This modification enhanced hydrophobicity and structural stability, overcoming the limitations of unmodified bio-based adsorbents. Characterization using SEM, XPS, FTIR, and TGA confirmed the successful grafting of hydrophobic functional groups and the formation of a uniformly rough surface, leading to a water contact angle of 157°. Comparative analysis demonstrated that the modified sphagnum moss exhibited a significantly enhanced adsorption capacity of 22.756 g/g for motor oil, outperforming conventional bio-based adsorbents, including currently prevalent biological adsorbents (1.69–12.8 g/g) and biochar (8.1–18.2 g/g). Furthermore, the adsorption kinetics conformed to a pseudo-second-order model, indicating chemisorption as the dominant mechanism. This suggests strong interactions between oil molecules and the functionalized surface, contributing to enhanced efficiency and selectivity. These findings highlight the novelty, superior performance, and environmental compatibility of modified sphagnum moss as an effective and sustainable solution for oil spill remediation. Its high adsorption capacity, selective oil affinity, and reusability make it a promising alternative to existing bio-based adsorbents, providing an eco-friendly approach to oil spill management and environmental restoration.

Neuromyelitis optica spectrum disorder (NMOSD) is a disabling autoimmune disease. Neutrophil activation plays a crucial role in the neuroinflammatory damage observed during disease exacerbations. This study aims to elucidate the potential role of the repulsive guidance molecule A-bone morphogenetic protein 4 (RGMa-BMP4) signaling pathway in neutrophil activation during NMOSD attacks. We employed transcriptomic sequencing, quantitative PCR, flow cytometry, and Western blot analysis on peripheral blood samples from NMOSD patients in acute and remission phases. Additionally, an NMO rat model was established to investigate in vivo molecular mechanisms, focusing on key signaling molecules, including RGMa, BMP4, and SMADs. Transcriptomic analysis identified five genes associated with NMOSD pathogenesis or neutrophil activation, with RGMA, EGFR, and HLA-DOB showing the most significant differences. RT-qPCR confirmed elevated levels of RGMA, BMP4, and SMADs in the acute phase. Flow cytometry and Western blotting demonstrated an increased nuclear-to-cytoplasmic ratio of SMAD4 protein in neutrophils from acute-phase NMOSD patients. In the NMO rat model, we observed significant upregulation of RGMA, BMP4, and SMAD4 mRNA in brain and spinal cord tissues, along with enhanced nuclear translocation of SMAD4 protein. Furthermore, there was a marked increase in myeloperoxidase (MPO) mRNA expression, a marker of neutrophil activation, in both brain and spinal cord tissues in the model group. Our findings indicate that the RGMa-BMP4 signaling pathway likely plays a key role in neutrophil-mediated neuroinflammation during NMOSD attacks. These results suggest potential therapeutic targets within this pathway, warranting further investigation into their clinical implications.

Background Alzheimer's disease (AD) has a major negative impact on people's quality of life, life, and health. More research is needed to determine the relationship between age and the pathologic products associated with AD. Meanwhile, the construction of an early diagnostic model of AD, which is mainly characterized by pathological products, is very important for the diagnosis and treatment of AD. Method We collected clinical study data from September 2005 to August 2024 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Using correlation analysis method like cor function, we analyzed the pathology products (t-Tau, p-Tau, and Aβ proteins), age, gender, and Minimum Mental State Examination (MMSE) scores in the ADNI data. Next, we investigated the relationship between pathologic products and age in the AD and non-AD groups using linear regression. Ultimately, we used these features to build a diagnostic model for AD. Results A total of 1,255 individuals were included in the study (mean [SD] age, 73.27 [7.26] years; 691male [55.1%]; 564 female [44.9%]). The results of the correlation analysis showed that the correlations between pathologic products and age were, in descending order, Tau (Corr=0.75), p-Tau (Corr=0.71), and Aβ (Corr=0.54). In the AD group, t-Tau protein showed a tendency to decrease with age, but it was not statistically significant. p-Tau protein levels similarly decreased with age and its decrease was statistically significant. In contrast to Tau protein, in the AD group, Aβ levels increased progressively with age. In the non-AD group, the trend of pathologic product levels with age was consistently opposite to that of the AD group. We finally screened the optimal AD diagnostic model (AUC=0.959) based on the results of correlation analysis and by using the Xgboost algorithm and SVM algorithm. Conclusion In a novel finding, we observed that Tau protein and Aβ had opposite trends with age in both the AD and non-AD groups. The linear regression curves of the AD and non-AD groups had completely opposite trends. Through a machine learning approach, we constructed an AD diagnostic model with excellent performance based on the selected features.

Brain tumor segmentation models have aided diagnosis in recent years. However, they face MRI complexity and variability challenges, including irregular shapes and unclear boundaries, leading to noise, misclassification, and incomplete segmentation, thereby limiting accuracy. To address these issues, we adhere to an outstanding Convolutional Neural Networks (CNNs) design paradigm and propose a novel network named A4-Unet. In A4-Unet, Deformable Large Kernel Attention (DLKA) is incorporated in the encoder, allowing for improved capture of multi-scale tumors. Swin Spatial Pyramid Pooling (SSPP) with cross-channel attention is employed in a bottleneck further to study long-distance dependencies within images and channel relationships. To enhance accuracy, a Combined Attention Module (CAM) with Discrete Cosine Transform (DCT) orthogonality for channel weighting and convolutional element-wise multiplication is introduced for spatial weighting in the decoder. Attention gates (AG) are added in the skip connection to highlight the foreground while suppressing irrelevant background information. The proposed network is evaluated on three authoritative MRI brain tumor benchmarks and a proprietary dataset, and it achieves a 94.4% Dice score on the BraTS 2020 dataset, thereby establishing multiple new state-of-the-art benchmarks. The code is available here: https://github.com/WendyWAAAAANG/A4-Unet.