About this Research Topic
Depression is a severe mental illness characterized by persistent sadness and loss of interest. It is recognized as the leading cause of disability, affecting approximately 322 million individuals worldwide. With the outbreak of the Coronavirus Disease 2019, the global mental health problem was further deteriorated.
Currently, the underlying pathogenesis of depression remains unclear. Growing evidence suggests depression cannot be fully explained by any single biological or environmental pathway. Furthermore, the overall effectiveness of antidepressant treatment for depression has yet to be satisfactory. Even the first-line treatment option, selective serotonin reuptake inhibitors, has a limited remission rate of only one-third. More importantly, the diagnosis of depression heavily relies on subjective identification based on clinical symptoms. Therefore, there is an urgent need to explore objective biomarkers for depression.
With the widely application of high-throughput omics technologies, tremendous breakthroughs and advancements were made in medical science. These technologies not only can help identify potential therapeutic targets, make individualized treatment plans, and predict treatment efficacy, but also facilitate the discovery of new drug targets and the development of innovative treatment strategies for depression. Therefore, we sincerely welcome submissions that identify depression biomarkers based on the vehicle of high-throughput omics technologies.
Specific topics that we strongly recommend addressing may include (but are not limited to) the following:
- Mental health disease related to depression, such as depression, post-stroke depression, post-traumatic depression, postpartum depression, children and adolescent depression, geriatric depression, and treatment-resistant depression;
- Objects related to clinical depression patients or animal models of depression;
- High-throughput omics techniques, such as genomics, transcriptomics, proteomics, metabolomics, 16s rna sequencing, and imagomics;
- Drug intervention, psychological intervention or other intervention in depression;
- The diagnosis, treatment or prediction in depression.
Currently, the underlying pathogenesis of depression remains unclear. Growing evidence suggests depression cannot be fully explained by any single biological or environmental pathway. Furthermore, the overall effectiveness of antidepressant treatment for depression has yet to be satisfactory. Even the first-line treatment option, selective serotonin reuptake inhibitors, has a limited remission rate of only one-third. More importantly, the diagnosis of depression heavily relies on subjective identification based on clinical symptoms. Therefore, there is an urgent need to explore objective biomarkers for depression.
With the widely application of high-throughput omics technologies, tremendous breakthroughs and advancements were made in medical science. These technologies not only can help identify potential therapeutic targets, make individualized treatment plans, and predict treatment efficacy, but also facilitate the discovery of new drug targets and the development of innovative treatment strategies for depression. Therefore, we sincerely welcome submissions that identify depression biomarkers based on the vehicle of high-throughput omics technologies.
Specific topics that we strongly recommend addressing may include (but are not limited to) the following:
- Mental health disease related to depression, such as depression, post-stroke depression, post-traumatic depression, postpartum depression, children and adolescent depression, geriatric depression, and treatment-resistant depression;
- Objects related to clinical depression patients or animal models of depression;
- High-throughput omics techniques, such as genomics, transcriptomics, proteomics, metabolomics, 16s rna sequencing, and imagomics;
- Drug intervention, psychological intervention or other intervention in depression;
- The diagnosis, treatment or prediction in depression.
Keywords: Depression, Biomarkers, Diagnosis, Treatment, Mental Health, Prediction
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