Pathophysiology of NSAID-Associated Intestinal Lesions in the Rat: Luminal Bacteria and Mucosal Inflammation as Targets for Prevention
Bibliographic record
Abstract
Non-steroidal anti-inflammatory drugs (NSAIDs) exert detrimental effects on the intestine, mainly through an involvement of enteric bacteria This study examined the pathophysiology of NSAID-associated intestinal lesions in a rat model of diclofenac-enteropathy and evaluated the effect of rifaximin on small bowel damage. Enteropathy was induced in 40-week-old male rats by intragastric diclofenac (4 mg/kg BID, 14 days). Rifaximin (delayed release formulation) was administered (50 mg/kg BID) 1 hour before the NSAID. At the end of treatments, parameters dealing with ileal damage, inflammation, barrier integrity, microbiota composition and TLR-NFkB-inflammasome pathway were evaluated. In addition, the modulating effect of rifaximin on NLRP3 inflammasome was tested in an in vitro cell system. Diclofenac induced intestinal damage and inflammation, triggering an increase in tissue concentrations of tumor necrosis factor and interleukin-1, higher expression of TLR-2 and TLR-4, MyD88, NFB and activation of caspase-1. In addition, the NSAID decreased ileal occludin expression and provoked a shift of bacterial phyla towards an increase in Proteobacteria and Bacteroidetes abundance. All these changes were counterbalanced by rifaximin co-administration. The drug was also capable of increasing the proportion of Lactobacilli, a genus depleted by the NSAID. In LPS-primed THP-1 cells stimulated by nigericin (a model to study the NLRP3 inflammasome), rifaximin reduced IL-1 production in a dose-dependent fashion, this effect being associated with inhibition of the up-stream caspase-1 activation. In conclusion, diclofenac induced ileal mucosal lesions, driving inflammatory pathways and microbiota changes. Rifaximin prevents diclofenac-induced enteropathy through both antibacterial and anti-inflammatory activities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".