A Novel Method Aimed at Counteracting the Side Effects Caused by Prostaglandin E<sub>2</sub> Deficiency During Non-Steroidal Anti-Inflammatory Drug Therapy
Bibliographic record
Abstract
BACKGROUND: Prostaglandin E2 (PGE2) plays key physiological roles within the body's organs and the systemic environment. Non-steroidal anti-inflammatory drugs (NSAIDs) inhibit the biosynthesis of PGE2, which can lead to global PGE2 deficiency, resulting in serious side effects in the gastrointestinal, renal and other systems. In contrast, various pyridine derivatives have been found to increase endogenous PGE2 levels within multiple organs and the systemic environment. We hypothesised that the use of pyridine derivatives (nicotinic acid, nicotine, niceritrol, nicotinyl alcohol, pyridinol carbamate, pyridoxine hydrochloride and pyridostigmine bromide) can recover PGE2 levels during NSAID treatment. METHODS: Reassessment of experimental data on PGE2 levels in NSAIDs and pyridine derivatives treatment, and in controls from previously published, independent studies. RESULTS: Overall, in all our investigations P values for unpaired or pair-wise comparisons were not statistically significant. CONCLUSIONS: We demonstrated that using pyridine derivatives along with NSAIDs, such as nonselective cyclooxygenase (COX) and selective COX-2 inhibitors, does not reduce endogenous PGE2 expression to below basal levels. This finding is based on both in vitro studies using animal and human tissues and in vivo studies performed with healthy volunteers. Using pyridine derivatives to correct a PGE2 deficiency during NSAID treatment is a novel method that we propose can offer a valuable, cost-effective therapeutic approach to preventing and treating the side effects of NSAIDs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".