Monoamine Oxidase Inhibitors: A Review of Their Anti-Inflammatory Therapeutic Potential and Mechanisms of Action
Bibliographic record
Abstract
Chronic inflammatory diseases are debilitating, affect patients’ quality of life, and are a significant financial burden on health care. Inflammation is regulated by pro-inflammatory cytokines and chemokines that are expressed by immune and non-immune cells, and their expression is highly controlled, both spatially and temporally. Their dysregulation is a hallmark of chronic inflammatory and autoimmune diseases. Significant evidence supports that monoamine oxidase (MAO) inhibitor drugs have anti-inflammatory effects. MAO inhibitors are principally prescribed for the management of a variety of central nervous system (CNS)-associated diseases such as depression, Alzheimer’s, and Parkinson’s; however, they also have anti-inflammatory effects in the CNS and a variety of non-CNS tissues. To bolster support for their development as anti-inflammatories, it is critical to elucidate their mechanism(s) of action. MAO inhibitors decrease the generation of end products such as hydrogen peroxide, aldehyde, and ammonium. They also inhibit biogenic amine degradation, and this increases cellular and pericellular catecholamines in a variety of immune and some non-immune cells. This decrease in end product metabolites and increase in catecholamines can play a significant role in the anti-inflammatory effects of MAO inhibitors. This review examines MAO inhibitor effects on inflammation in a variety of in vitro and in vivo CNS and non-CNS disease models, as well as their anti-inflammatory mechanism(s) of action.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".