Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Growth and differentiation factor 15: An emerging therapeutic target for brain diseases.
Biosci Trends
January 2025
Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Growth and differentiation factor 15 (GDF15), a member of the transforming growth factor-βsuperfamily, is considered a stress response factor and has garnered increasing attention in recent years due to its roles in neurological diseases. Although many studies have suggested that GDF15 expression is elevated in patients with neurodegenerative diseases (NDDs), glioma, and ischemic stroke, the effects of increased GDF15 expression and the potential underlying mechanisms remain unclear. Notably, many experimental studies have shown the multidimensional beneficial effects of GDF15 on NDDs, and GDF15 overexpression is able to rescue NDD-associated pathological changes and phenotypes.
View Article and Find Full Text PDFCA-SQBG: Cross-attention guided Siamese quantum BiGRU for drug-drug interaction extraction.
Comput Biol Med
January 2025
College of Electronic Information, Xijing University, Xi'an, China. Electronic address:
Accurate and efficient drug-drug interaction extraction (DDIE) from the medical corpus is essential for pharmacovigilance, drug therapy and drug development. To solve the problems of unbalance dataset and lack of accurate manual annotations in DDIE, a cross-attention guided Siamese quantum BiGRU (CA-SQBG) is constructed to improve feature representation learning ability for DDIE. It mainly consists of two quantum BiGRUs (QBiGRUs) and a cross-attention, where two QBiGRUs are Siamese implemented in a variational quantum environment to learn the contextual semantic feature representation of drug pairs, cross-attention is employed to learn mutual information from the Siamese QBiGRUs, which in turn allows the two modules to extract DDI more collaboratively.
View Article and Find Full Text PDFThe Chinese version of the patients' experiences scale for older inpatients in the medical group: Instrument development and cross-sectional validation study.
Geriatr Nurs
January 2025
West China Biomedical Big Data Center, West China Hospital of Sichuan University, Chengdu, China. Electronic address:
Value-based healthcare is increasingly emphasizing attention to patients' self-reported experiences. However, due to the lack of effective tools, older patients in China lack feedback on the comprehensive care experience. Based on the psychometric assessment procedure, we developed a new geriatric inpatient experience scale (GIES).
View Article and Find Full Text PDFPublic value and digital health: The example of guiding values in the national digital health strategy of France.
Int J Med Inform
January 2025
ISCTE-Instituto Universitário de Lisboa, Lisbon, Portugal; Universidade da Beira Interior Faculdade de Ciências da Saúde, Covilha, Portugal.
Introduction: In the WHO European Region, 44 of 53 reporting Member States (MS) have a national digital health strategy (NDHS) or policy. Their formulation is heterogenous and evolving and should best reflect public common interest. This research aims to explore how a public value approach improves the relevance of digital health policies and services, increasing their capacity to better serve the diverse range of societal interests.
View Article and Find Full Text PDFWhen bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute.
Neural Netw
January 2025
School of Cyber Science and Engineering, Xi'an Jiaotong University, China. Electronic address:
Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose Eagle, a deep framework based on bipartitE grAph learninG for anomaLy dEtection.
View Article and Find Full Text PDFWant 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!