Chronic and neuropathic pain are a widespread burden. Incomplete understanding of underlying pathomechanisms is one crucial factor for insufficient treatment. Recently, impairment of the blood nerve barrier (BNB) has emerged as one key aspect of pain initiation and maintenance. In this narrative review, we discuss several mechanisms and putative targets for novel treatment strategies. Cells such as pericytes, local mediators like netrin-1 and specialized proresolving mediators (SPMs), will be covered as well as circulating factors including the hormones cortisol and oestrogen and microRNAs. They are crucial in either the BNB or similar barriers and associated with pain. While clinical studies are still scarce, these findings might provide valuable insight into mechanisms and nurture development of therapeutic approaches.
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http://dx.doi.org/10.1016/j.pharmthera.2023.108484 | DOI Listing |
Sci Rep
December 2024
School of Marxism, China University of Political Science and Law (CUPL), Beijing, 100091, China.
To improve students' understanding of physical education teaching concepts and help teachers analyze students' cognitive patterns, the study proposes an association learning-based method for understanding physical education teaching concepts using deep learning algorithms, which extracts image features related to teaching concepts using convolutional neural networks. Moreover, a neurocognitive diagnostic model based on hypergraph convolution is constructed to mine the data of students' long-term learning sequences and identify students' cognitive outcomes. The findings revealed that the highest accuracy of the association graph convolutional neural network was 0.
View Article and Find Full Text PDFACS Appl Mater Interfaces
December 2024
TCS Research, Sahyadri Park 2, Rajiv Gandhi Infotech Park, Hinjewadi Phase 3, Pune 411057, India.
Realization of a sustainable hydrogen economy in the future requires the development of efficient and cost-effective catalysts for its production at scale. MXenes (MX) are a class of 2D materials with 'n' layers of carbon or nitrogen (X) interleaved by 'n+1' layers of transition metal (M) and have emerged as promising materials for various applications including catalysts for hydrogen evolution reaction (HER). Their properties are intimately related to both their composition and their atomic structure.
View Article and Find Full Text PDFJ Nanobiotechnology
December 2024
Key Laboratory of Special Environmental Medicine of Xinjiang, General Hospital of Xinjiang Military Command, No. 359, Youhao North Road, Urumqi, Xinjiang, China.
Objective: This study aims to elucidate the mechanisms by which nanovesicles (NVs) transport curcumin(CUR) across the blood-brain barrier to treat hypothalamic neural damage induced by heat stroke by regulating the expression of poly(c)-binding protein 2 (PCBP2).
Methods: Initially, NVs were prepared from macrophages using a continuous extrusion method. Subsequently, CUR was loaded into NVs using sonication, yielding engineered cell membrane Nanovesicles loaded with curcumin (NVs-CUR), which were characterized and subjected to in vitro and in vivo tracking analysis.
Sci Rep
December 2024
School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, 161006, China.
A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG's and improve the robustness of the system under varying load and generation conditions.
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