Preparation of magnetic activated carbon fibers@Fe3O4 by electrostatic self-assembly method and adsorption properties for methylene blue.

Nano-Fe3O4 was loaded onto coconut-based activated carbon fibres (CACF) using an electrostatic self-assembly method. The effects of the mass ratio of CACF to nano-Fe3O4, loading time, pH and temperature on the loading effect were investigated and ideal loading conditions were determined. To study the adsorption performance of MACF@Fe3O4 for methylene blue, the effects of the initial concentration, pH and time on the adsorption were investigated and the working conditions of adsorption were established. MACF@Fe3O4 was systematically characterized. Adsorption kinetics were investigated under ideal conditions. The ideal loading conditions for MACF@Fe3O4 were as follows: mass ratio of 1:1, 20 min, pH 9.36, 22.5°C. The saturation magnetization of MACF@Fe3O4 was 48.2263 emu·g-1, which could be quickly separated under an external magnetic field. When the dosage was 0.010 g, the adsorption rate reached 97.29% and the maximum adsorption capacity was 12.1616 mg·g-1. The adsorption process conformed to pseudo-first-order kinetics during the first 15 min and pseudo-second-order kinetics during 20-120 min. The equations were ln( Q e - Q t )=2.2394-0.0689t and t Q t =0.0774 + 0.5295t , respectively. The isothermal adsorption model showed that MACF@Fe3O4 was more in line with the Langmuir model, indicating that the adsorption process was mainly monolayer adsorption. The thermodynamic analysis results showed that the adsorption process of MB by MACF@Fe3O4 was an endothermic process. In this study, MACF@Fe3O4 with high adsorption capacity and easy separation from coconut palm fibres has good application prospects in the field of adsorption, which can promote the high-value utilization of coconut palms.

GT-CAM: Game Theory based Class Activation Map for GCN.

Graph Convolutional Networks (GCN) have shown outstanding performance in skeleton-based behavior recognition. However, their opacity hampers further development. Researches on the explainability of deep learning have provided solutions to this issue, with Class Activation Map (CAM) algorithms being a class of explainable methods. However, existing CAM algorithms applies to GCN often independently compute the contribution of individual nodes, overlooking the interactions between nodes in the skeleton. Therefore, we propose a game theory based class activation map for GCN (GT-CAM). Firstly, GT-CAM integrates Shapley values with gradient weights to calculate node importance, producing an activation map that highlights the critical role of nodes in decision-making. It also reveals the cooperative dynamics between nodes or local subgraphs for a more comprehensive explanation. Secondly, to reduce the computational burden of Shapley values, we propose a method for calculating Shapley values of node coalitions. Lastly, to evaluate the rationality of coalition partitioning, we propose a rationality evaluation method based on bipartite game interaction and cooperative game theory. Additionally, we introduce an efficient calculation method for the coalition rationality coefficient based on the Monte Carlo method. Experimental results demonstrate that GT-CAM outperforms other competitive interpretation methods in visualization and quantitative analysis.

Knockdown of Galectin-9 alleviates rheumatoid arthritis through suppressing TNF-α-induced activation of fibroblast-like synoviocytes.

The role of Galectin-9 (Gal-9) in the pathogenesis of rheumatoid arthritis (RA) remains unclear. This study aimed to investigate the mechanism of action and therapeutic potential of Gal-9 in RA. We detected Gal-9 expression in clinical samples, explored the mechanism of function of Gal-9 by knockdown and overexpression in fibroblast-like synoviocytes (FLSs), and further verified it in collagen-induced arthritis (CIA) model. We found that the levels of Gal-9 were considerably elevated in RA synovium than in osteoarthritis (OA) patients. A substantial decrease of Gal-9 was demonstrated after tumor necrosis factor (TNF-α) inhibitor treatment in the plasma of patients with RA. Additionally, transcriptome sequencing revealed that Gal-9 was involved in the regulation of the TNF-α pathway. Gal-9 was considerably upregulated after TNF-α stimulation in FLSs, and knockdown of Gal-9 substantially inhibited TNF-α activated proliferation, migration and inflammatory response. According to cell transcriptome sequencing results, we further confirmed that Gal-9 could achieve these effects by interacting with MAFB and affecting PI3K/AKT/mTOR pathway. Finally, we knocked down Gal-9 on the CIA model and found that it could alleviate the progression of arthritis. In conclusion, our study revealed that the knockdown of Gal-9 could inhibited TNF-α induced activation in RA through MAFB, PI3K/AKT/mTOR.

COVID-19 infection characteristics, risk factors and its potential impacts on Takayasu arteritis: a web-based survey in a large cohort.

Objectives: To investigate the characteristics of COVID-19 and its impact on patients with Takayasu's arteritis (TAK). Methods: A web-based survey was administered to a TAK cohort and their co-residents in China during January 2023. Infection symptoms, post-acute sequelae of COVID-19 (PASC), potential impacts of COVID-19 on patients' disease condition, treatment and immune-related parameters were analyzed. In addition, risk factors for COVID-19 and disease relapse after infection were explored. Results: The infection rate was significantly lower in patients with TAK than in co-residents (79.13% vs 90.67%, p=0.025). TAK patients were more prone to gastrointestinal symptoms (17.78% vs 5.88%, p=0.024), sleep problems (25.15% vs 10.29%, p=0.011), and symptoms involving more than 2 organs (58.90% vs 35.29%, p=0.001) after infection. Although only 2.45% of TAK patients were hospitalized and none progressed to life-threatening conditions, they were more likely to suffer from PASC (26.38% vs 13.24%, p=0.029), especially active patients. Active disease after the pandemic was significantly lower in infected patients than uninfected patients (21/163, 12.88% vs. 11/43, 25.58%, p=0.041). The presence of multiple system symptoms was a risk factor for active TAK after infection [OR: 3.62 (95% CI 1.06-12.31), p=0.040]. Moreover, csDMARDs treatment was a risk factor for COVID-19 infection [OR: 3.68 (95% CI 1.56-8.66), p=0.002]. Conclusion: Although TAK patients with COVID-19 have more acute and post-acute symptoms, there is no adverse outcome and the risk of disease relapse does not increase. Patients treated with csDMARDs may be at higher risk of infection and deserve more clinical attention.