Nitric Oxide and Major Depressive Disorder: Pathophysiology and Treatment Implications
Bibliographic record
Abstract
Major depressive disorder (MDD) is a multi-factorial and heterogeneous disease. Robust evidence suggests that inflammation is involved in the pathogenesis of MDD for a subpopulation of individuals. However, it remains unclear what traits and/or states precede the onset of inflammation in this subpopulation of individuals with MDD. Several recent studies have implicated nitric oxide (NO) as a critical regulator of neuroinflammation, thus suggesting a possible role in the pathophysiology of MDD. The aim of this review is to evaluate the evidentiary base supporting the hypothesis that the increased hazard for developing MDD in certain subpopulations may be mediated, in part, by inflammogenic trait and/or state variations in NO signaling pathways. We conducted a non-systematic literature search for English language studies via PubMed and Google Scholar, from 1985 to October 2014. Replicated evidence suggests that NO has contrasting effects in the central nervous system (CNS). Low concentrations of NO are neuroprotective and mediate physiological signaling whereas higher concentrations mediate neuroinflammatory actions and are neurotoxic. Certain polymorphisms in the neuronal nitric oxide synthase gene (NOS1) are associated MDD. Furthermore, state variations (e.g. decreased levels of essential co-factor, 5,6,7,8-tetrahydrobiopterin [BH4], enhanced microglial cell activity) in the NO signaling pathway are associated with an increased risk of developing MDD. Increased concentrations of NO enhance the production of reactive nitrogen species (RNS) and reactive oxygen species (ROS), which are associated with an increase in pro-inflammatory cytokines. Taken together, evidences suggest that abnormalities in NO signaling may constitute a trait-marker related to MDD pathophysiology, which could be explored for novel therapeutic targets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".