JOURNAL/nrgr/04.03/01300535-202509000-00029/figure1/v/2024-12-31T000210Z/r/image-tiff

Protein arginine methyltransferase-6 participates in a range of biological functions, particularly RNA processing, transcription, chromatin remodeling, and endosomal trafficking. However, it remains unclear whether protein arginine methyltransferase-6 modifies neuropathic pain and, if so, what the mechanisms of this effect. In this study, protein arginine methyltransferase-6 expression levels and its effect on neuropathic pain were investigated in the spared nerve injury model, chronic constriction injury model and bone cancer pain model, using immunohistochemistry, western blotting, immunoprecipitation, and label-free proteomic analysis. The results showed that protein arginine methyltransferase-6 mostly co-localized with β-tubulin III in the dorsal root ganglion, and that its expression decreased following spared nerve injury, chronic constriction injury and bone cancer pain. In addition, PRMT6 knockout (Prmt6^-/-) mice exhibited pain hypersensitivity. Furthermore, the development of spared nerve injury-induced hypersensitivity to mechanical pain was attenuated by blocking the decrease in protein arginine methyltransferase-6 expression. Moreover, when protein arginine methyltransferase-6 expression was downregulated in the dorsal root ganglion in mice without spared nerve injury, increased levels of phosphorylated extracellular signal-regulated kinases were observed in the ipsilateral dorsal horn, and the response to mechanical stimuli was enhanced. Mechanistically, protein arginine methyltransferase-6 appeared to contribute to spared nerve injury-induced neuropathic pain by regulating the expression of heterogeneous nuclear ribonucleoprotein-F. Additionally, protein arginine methyltransferase-6-mediated modulation of heterogeneous nuclear ribonucleoprotein-F expression required amino acids 319 to 388, but not classical H3R2 methylation. These findings indicated that protein arginine methyltransferase-6 is a potential therapeutic target for the treatment of peripheral neuropathic pain.

• Book : 20(9)
• Pub. Date : 2025
• Page : pp.2682-2696
• Keyword :

• Book : 156(pb)
• Pub. Date : 2025
• Page : pp.104836
• Keyword :

• Book : 10(1)
• Pub. Date : 2025
• Page : pp.101661
• Keyword :

• Book : 44()
• Pub. Date : 2025
• Page : pp.38-46
• Keyword :

• Book : 57()
• Pub. Date : 2025
• Page : pp.103139
• Keyword :

• Book : 145()
• Pub. Date : 2025
• Page : pp.105683
• Keyword :

Due to challenging field operations and resource constraints, seismic data acquisition often requires coping with missing traces. Interpolation algorithms are crucial for reconstructing these missing traces to enable improved subsurface analysis and interpretation. Although deep learning has made exciting advances in seismic reconstruction, its focus has predominantly been on 2D and 3D data sets with relatively low rates of missing data. Reconstruction of 5D seismic data entails considering simultaneous sources and receivers deployed in areal arrays to solve the reconstruction problem. The latter offers greater data redundancy, which can be leveraged to enhance interpolation quality. Traditional 5D deep-learning interpolation methods rely heavily on synthetic training pairs, posing challenges when applied to real-world data. This necessitates transfer learning techniques, which can be cumbersome. To address this, we introduce a self-supervised, coordinate-based deep interpolation algorithm that mitigates the need for labeled data. Using a multilayer perceptron (MLP) network can effectively encode the continuous seismic 5D wavefield. Once trained, the MLP can infer missing trace amplitudes from their coordinates. We contribute to boosting the MLP, enabling it to generate seismic profiles rather than single-point predictions. This enhancement significantly strengthens the model’s performance and efficiency. Moreover, we apply nuclear norm regularization to the output profiles, improving the reconstruction quality. The effectiveness of our algorithm is illustrated with synthetic and field data experiments.

• Book : 90(1)
• Pub. Date : 2025
• Page : pp.V29-V42
• Keyword :

• Book : 56(2)
• Pub. Date : 2025
• Page : pp.101832
• Keyword :

• Book : 42()
• Pub. Date : 2025
• Page : pp.101852
• Keyword :

• Book : 42()
• Pub. Date : 2025
• Page : pp.101846
• Keyword :