Abstract
Corticotropin-releasing factor (CRF) and its related peptides play a central role in the co-ordination of autonomic, behavioural, cardiovascular, immune and endocrine responses to stressful stimuli. Their actions are mediated through activation of two G-protein-coupled receptors encoded by separate genes, termed CRF-R1 and -R2. In this review we focus on the structure, phylogeny, and expression of CRF peptide family. We present up-to-date literature on divergent actions of the CRF peptide family, including stress and behaviour, cardiovascular physiology, energy balance and re pr oduction. We also pr ese nt a br ief over view of CRF rec eptor s inc luding pha rmacologic al aspe cts a nd potential therapeutic interventions for CRF receptors.
Keywords: corticotropin releasing factor, stressful stimuli, protein-coupled receptors, crf peptide family
Call for Papers in Thematic Issues
Current Genomics in Cardiovascular Research
Cardiovascular diseases are the main cause of death in the world, in recent years we have had important advances in the interaction between cardiovascular disease and genomics. In this Research Topic, we intend for researchers to present their results with a focus on basic, translational and clinical investigations associated with ...read more
Genomic Insights into Oncology: Harnessing Machine Learning for Breakthroughs in Cancer Genomics.
This special issue aims to explore the cutting-edge intersection of genomics and oncology, with a strong emphasis on original data and experimental validation. While maintaining the focus on how machine learning and advanced data analysis techniques are revolutionizing our understanding and treatment of cancer, this issue will prioritize contributions that ...read more
Integrating Artificial Intelligence and Omics Approaches in Complex Diseases
Recent advancements in AI and omics methodologies have revolutionized the landscape of biomedical research, enabling us to extract valuable information from vast amounts of complex data. By combining AI algorithms with omics technologies such as genomics, proteomics, metabolomics, and transcriptomics, researchers can obtain a more comprehensive and multi-dimensional analysis of ...read more
Integrating Machine Learning with Genome Science: Pioneering Developments and Future Directions
Integrating machine learning (ML) with genome science is driving transformative advancements in fields such as genomics, personalized medicine, and drug discovery. Genomic data is vast and complex, making traditional analysis methods inadequate for uncovering deep insights. Machine learning, particularly deep learning models like convolutional neural networks (CNNs) and recurrent neural ...read more
Related Journals
Related Books
The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming
Handbook of Integrated Weed Management for Major Field Crops
Practical Biochemistry
Anticancer Drugs Sourced from Marine Life
Opportunities for Biotechnology Research and Entrepreneurship
Diseases of Ornamental, Aromatic and Medicinal Plants
Diabetes and Breast Cancer: An Analysis of Signaling Pathways
Fundamentals of Cellular and Molecular Biology
Industrial Applications of Soil Microbes
Genome Editing in Bacteria (Part 2)
4
