Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Other Anti- Inflammatory Agents in the Treatment of Neurodegenerative Disease
Bibliographic record
Abstract
Inflammation is characteristic of a broad spectrum of neurodegenerative diseases. These include Alzheimer's (AD), Parkinson's (PD), and Huntington's diseases, amyotrophic lateral sclerosis, all of the tauopathies, multiple sclerosis and many other less common conditions. Morphologically, the level of inflammation is determined by the concentration and degree of activation of microglial cells. Biochemically, it is judged by the presence of a spectrum of inflammatory mediators. Epidemiological evidence indicates that anti-inflammatory agents such as non-steroidal anti-inflammatory drugs (NSAIDs) have a sparing effect on AD and PD indicating that inflammation exacerbates the pathology in these diseases. NSAIDs are protective in transgenic animal models of AD, providing further evidence of the negative consequences of inflammation. Here we describe an in vitro model, which was used to study the protective effects of NSAIDs in AD. This model is based on neuronal cell killing by stimulated microglia or microglia-like cells. In this model NSAIDs show protective effects at a therapeutically relevant level, which is in the low micromolar range. There are reports suggesting that NSAIDs act independently of cyclooxygenase (COX) inhibition, but only at higher doses. Classical NSAIDs are still the most logical choice for agents that will slow the progression or delay the onset of AD and other neurodegenerative diseases despite failures of naproxen, celecoxib and rofecoxib in AD clinical trials. Several other classes of anti-inflammatory drugs have been identified as potentially beneficial in this and similar assay systems. Therefore combination therapy with other anti-inflammatory agents that work through different mechanisms of action might prove to be a superior therapeutic strategy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".