Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function

Depression is a multifactorial disease with unknown etiology affecting globally. It’s the second most significant reason for infirmity in 2020, affecting about 50 million people worldwide, with 80% living in developing nations. Recently, a surge in depression research has been witnessed, resulting in a multitude of emerging techniques developed for prediction, evaluation, detection, classification, localization, and treatment. The main purpose of this study is to determine the volume of depression research conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, and associations with other mental disorders like anxiety and stress using traditional and medical intelligence (medical with AI). In addition, it also designs a comprehensive survey on detection, treatment planning, and genetic predisposition, along with future recommendations. This work is designed through different methods, including a systematic mapping process, literature review, and network visualization. In addition, we also used VOSviewer software and some authentic databases such as Google Scholar, Scopus, PubMed, and Web of Science for data collection, analysis, and designing comprehensive picture of the study. We analyzed 60 articles related to medical intelligence, including 47 from machine learning with 513,767 subjects (mean ± SD = 10,931.212 ± 35,624.372) and 13 from deep learning with 37,917 subjects (mean ± SD = 3159.75 ± 6285.57). Additionally, we also found that stressors impact the brain's cognitive and autonomic functioning, resulting in increased production of catecholamine, decreased cholinergic and glucocorticoid activity, with increased cortisol. These factors lead to chronic inflammation and hinder the brain's normal functioning, leading to depression, anxiety, and cardiovascular disorders. In the brain, reactive oxygen species (ROS) production is increased by IL-6 stimulation and mitochondrial cytochrome c oxidase is inhibited by nitric oxide, a potent inhibitor. Proteins, lipids, oxidative phosphorylation enzymes, and mtDNA are further disposed to oxidative impairment in the mitochondria. Consequently, mitochondrial dysfunction exacerbates oxidative stress, impairs mitochondrial DNA (mtDNA) or deletions of mtDNA, increases intracellular Ca2+ levels, changes in fission/fusion and mitochondrial morphology, and lastly leads to neuronal death. This study highlights the multidisciplinary approaches to depression with different aspects using traditional and medical intelligence. It will open a new way for depression research through new emerging technologies.

Mots clés

• brain
• cardia
• molecule
• detection
• inflammation
• mitochondrial dysfunction
• artificial intelligence
• therapy
• prediction
• oxidative stress
• medicine
• unani medicine
• herbal medicine