Neurobiology and Therapeutic Potential of Cyclooxygenase-2 (COX-2) Inhibitors for Inflammation in Neuropsychiatric Disorders
Bibliographic record
Abstract
Neuropsychiatric disorders, such as depression, bipolar disorder, schizophrenia, obsessive-compulsive disorder, and neurodevelopmental disorders such as autism spectrum disorder, are associated with significant illness burden. Accumulating evidence supports an association between these disorders and inflammation. Consequently, anti-inflammatory agents, such as the cyclooxygenase-2 inhibitors, represent a novel avenue to prevent and treat neuropsychiatric illness. In this paper, we first review the role of inflammation in psychiatric pathophysiology including inflammatory cytokines' influence on neurotransmitters, the hypothalamic-pituitary-adrenal axis, and microglial mechanisms. We then discuss how cyclooxygenase-2-inhibitors influence these pathways with potential therapeutic benefit, with a focus on celecoxib, due to its superior safety profile. A search was conducted in PubMed, Embase, and PsychINFO databases, in addition to Clinicaltrials.gov and the Stanley Medical Research Institute trial registries. The results were presented as a narrative review. Currently available outcomes for randomized controlled trials up to November 2017 are also discussed. The evidence reviewed here suggests cyclooxygenase-2 inhibitors, and in particular celecoxib, may indeed assist in treating the symptoms of neuropsychiatric disorders; however, further studies are required to assess appropriate illness stage-related indication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.003 | 0.001 |
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".