The nervous, endocrine and immune systems are interconnected through neurotransmitters, endocrine hormones and cytokines, forming a neuro-endocrine-immune (NEI) network that outputs effect information to target organs in a multi-level and multi-system manner. The effects of acupuncture can be amplified through the NEI network, causing local microenvironment responses at acupoints to treat post-stroke depression (PSD). This paper systematically elucidates the mechanisms of acupuncture treatment for PSD from three aspects: nervous, endocrine and immune, and reveals the potential mechanisms and biological pathways of acupuncture treatment for PSD from an overall perspective based on the NEI network, aiming to provide a reliable theoretical basis for fundamental research on acupuncture treatment for PSD.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.13703/j.0255-2930.20240311-k0004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Human motion similarity evaluation based on deep metric learning.
Sci Rep
December 2024
College of Sports, Beihua University, Jilin, 132000, China.
In order to eliminate the impact of camera viewpoint factors and human skeleton differences on the action similarity evaluation and to address the issue of human action similarity evaluation under different viewpoints, a method based on deep metric learning is proposed in this article. The method trains an automatic encoder-decoder deep neural network model by means of a homemade synthetic dataset, which maps the 2D human skeletal key point sequence samples extracted from motion videos into three potential low-dimensional dense spaces. Action feature vectors independent of camera viewpoint and human skeleton structure are extracted in the low-dimensional dense spaces, and motion similarity metrics are performed based on these features, thereby effectively eliminating the effects of camera viewpoint and human skeleton size differences on motion similarity evaluation.
View Article and Find Full Text PDF[Not Available].
Recenti Prog Med
December 2024
Dipartimento di epidemiologia del Ssr del Lazio, Asl Roma 1.
Air pollution has no borders. Over 90% of the global population breathes air contaminated daily by pollutants such as fine particulate matter (PM 2.5 and PM 10), ozone, and nitrogen dioxide (NO2), with serious consequences for public health and the environment.
View Article and Find Full Text PDFQuantifying information stored in synaptic connections rather than in firing patterns of neural networks.
ArXiv
November 2024
Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.
A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of connections among the constituent neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing patterns of neural networks, little is known about quantifying information encoded by its synaptic connections. Here, we develop a theoretical framework using continuous Hopfield networks as an exemplar for associative neural networks, and data that follow mixtures of broadly applicable multivariate log-normal distributions.
View Article and Find Full Text PDFA comprehensive review of sensor node deployment strategies for maximized coverage and energy efficiency in wireless sensor networks.
PeerJ Comput Sci
November 2024
Department of Computing and Information Systems, Sunway University, Malaysia, Kuala Lumpur, Malaysia.
Wireless Sensor Networks (WSNs) have paved the way for a wide array of applications, forming the backbone of systems like smart cities. These systems support various functions, including healthcare, environmental monitoring, traffic management, and infrastructure monitoring. WSNs consist of multiple interconnected sensor nodes and a base station, creating a network whose performance is heavily influenced by the placement of sensor nodes.
View Article and Find Full Text PDFTGNet: tensor-based graph convolutional networks for multimodal brain network analysis.
BioData Min
December 2024
Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, 18015, PA, USA.
Article Synopsis
- The paper discusses a new approach called TGNet, a tensor-based graph convolutional network, designed to analyze multimodal brain networks more effectively than current methods.
- TGNet combines tensor decomposition with multi-layer graph convolutional networks to capture both simple and complex brain network structures.
- The framework has been tested on data from various neurological disorders and shows improved accuracy in disease classification, especially when sample sizes are small, showcasing its potential for future research.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!