Deciphering the pathogenesis of NSAID enteropathy using proton pump inhibitors and a hydrogen sulfide-releasing NSAID
Bibliographic record
Abstract
The small intestine is a significant site of ulceration and bleeding induced by nonsteroidal anti-inflammatory drugs (NSAIDs). The pathogenesis is poorly understood. The present study explored the roles of bile, bacteria, and enterohepatic circulation to NSAID enteropathy, using both a conventional NSAID (naproxen) and a gastrointestinal-safe naproxen derivative (ATB-346), as well as proton pump inhibitors (PPIs). Rats were treated orally with naproxen or equimolar doses of ATB-346 over a 5-day period, with or without PPI administration, and intestinal damage was quantified. The cytotoxicity of bile from the rats was evaluated in vitro. Biliary excretion of naproxen and ATB-346 was determined. The impact of the NSAIDs and of PPIs on the composition of the intestinal microbiota was examined by deep sequencing of 16s rRNA. Naproxen caused significant intestinal damage and inflammation, whereas ATB-346 did not. Naproxen, but not ATB-346, dose dependently increased the cytotoxicity of bile, and it was further increased by PPI coadministration. Whereas biliary excretion of naproxen was significant in naproxen-treated rats, it was greatly reduced in rats treated with ATB-346. The enteric microbiota of naproxen-treated rats was distinct from that in vehicle- or ATB-346-treated rats, and PPI administration caused significant intestinal dysbiosis. The increase in cytotoxicity of bile induced by naproxen and PPIs may contribute significantly to intestinal ulceration and bleeding. Some of these effects may occur secondary to significant changes in the jejunal microbiota induced by both naproxen and PPIs.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".