Editorial: New players on the monoaminergic field: relevance to the mental disorders
Bibliographic record
Abstract
It is well established that monoamines, such as serotonin (5-HT), noradrenaline, and dopamine, are fundamental in emotions and mood regulation. They also play a key role in the pathophysiology and treatment of mood disorders, such as unipolar and bipolar depression, as well as anxiety disorders, such as generalized anxiety, obsessive-compulsive, panic, and post-traumatic stress disorders [1]. We may underline three factors that are of critical importance for the proper understanding of the role of monoamines in mood disorders. The first one is the autoregulatory mechanism within the monoaminergic circuits [2]. The second one is the multiple crossinteraction between different monoamines [3]. The third factor is the crosstalk between monoamines and other bioactive molecules influencing mood regulation and mental functions in general. Examples of particularly important bioactive molecules are amino acid neurotransmitters, trace amines (biological amines biochemically related to the "classical" monoamines, but present in the brain in trace concentrations, such as tyramine, octopamine, and tryptamine), corticosteroids, adenosine, endogenous opioids, and cannabinoids (Fig 1).While the auto-and cross-regulatory mechanisms of the central monoaminergic systems have been extensively studied in the last decades, the crosstalk between monoamines and specific other biomolecules involved in mood regulation received lesser attention. This collection of articles was prepared in order to diminish this gap in knowledge and to stimulate future research in this direction.The task was completed thanks to the international team of researchers, coming from different parts of the world (Asia, America, Europe) and employment sectors (universities, public hospitals and research centers, and industrial enterprises). These researchers contributed their outstanding articles addressing the abovementioned points.Sha Zhao and colleagues from the Qingdao University, Qingdao, China, provided a review article on endocannabinoid-monoamine interactions, learning, and memory. Elad Lerer and co-authors from the Hadassah Medical Center, Jerusalem, Israel, contributed original research on the neurochemical mechanisms underlying putative beneficial effect of psychedelics in mental disorders. Daniela Jezova with Eliyahu Dremencov and their co-authors from the Biomedical Research Center and Centre of Biosciences, Slovak Academy of Sciences, Bratislava, Slovakia, shared with us an original research article on functional interactions between monoamines, glutamate, and endogenous opioids, focusing on prenatal effect of the ligands of opioid receptors on monoaminergic and glutamatergic transmission. Stephen Daniels and co-authors from the Institute of Mental Health Research, University of Ottawa, Ottawa, Canada, prepared an original research paper on glutamate-monoamine interactions and their role in the rapid antidepressant effect of ketamine. Hurmat Malik and co-contributors from the COMSATS University Islamabad, Islamabad, Pakistan, shared with us the results of their research on the interactions between monoamines, adenosine, microelements and steroids, with a practical focus on the putative anxiolytic and antidepressant-like effects of a natural element, diosgenin. Karolina Kolaczynska and Legend to the Figure Figure 1: Functional interactions between monoamines and other signaling biomolecules. VTA DA, ventral tegmental area dopamine neuron, LC NA, locus coeruleus noradrenaline neuron, TMN HA, tuberomammillary nucleus histamine neuron, DRN 5-HT, dorsal raphe nucleus serotonin (5-HT) neuron, ARN BEN, arcuate nucleus β-endorphin neuron, PFC GLU, prefrontal cortex glutamate neuron, A2A, adenosine-2A receptor, D2, dopamine-2 receptor, α1/2-alpha-1/2 adrenoceptor, H1/3histamine-1/3 receptor, 5-HT1A, serotonin-1A receptor, DOR-δ-opioid receptor, GR, glucocorticoid receptor, NMDA, N-methyl-D-aspartate glutamate receptor, CB1, cannabinoid-1 receptor, TAAR1, trace amine associated receptor 1, CS, corticosteroids, EC, endocannabinoids, +, stimulatory effect, -, inhibitory effect. According to [3; 4; 5; 6; 7; 8; 9].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".