Investigating Effects of Atorvastatin and Lactulose on ameliorating Crohn’s Disease in 2,4,6-Trinitrobenzene Sulphonic Acid (TNBS)-Induced Colitis: A Research Protocol
Bibliographic record
Abstract
Introduction: Crohn’s disease (CD) is an inflammatory bowel disease (IBD) triggered by excessive activation of the gastrointestinal (GI) immune system, causing intestinal inflammation. Current therapeutic strategies use probiotics and fecal matter transplantation (FMT) to increase microbiome diversity and induce CD remission, but their efficacy is unclear. Conversely, prebiotics such as lactulose were found to increase anti-inflammatory bacteria thus reducing inflammatory markers in colitis mouse models. Additionally, atorvastatin has been found to modulate GI immune system activity by reducing chemokine expression, providing a potential treatment for CD. In this research protocol, a combination therapy using lactulose and atorvastatin is proposed as a treatment strategy for CD. Methods: 2.5% 2,4,6-trinitrobenzene sulphonic acid (TNBS) was injected intrarectally at a dose of 100 mg/kg to induce colitis in the mouse models. In this randomized controlled trial, the mice will be equally assigned to 5 groups: untreated TNBS mice (n = 10); TNBS mice treated with lactulose (0.005 ml / g of body weight, n = 10); TNBS mice treated with atorvastatin (5 mg/kg, n = 10); TNBS mice treated with the atorvastatin and lactulose combination therapy (n = 10); and the untreated healthy mouse (n = 10) to serve as the negative control. Histopathological analysis was conducted on colon tissue along with ELISA identification of inflammatory markers and mRNA analysis. Anticipated Results: It is expected that the TNBS group treated with the combination therapy will have the lowest levels of inflammatory cytokines. It is also expected that the treated TNBS mice will have the closest Crohn’s Disease Activity Index (CDAI) score to the healthy untreated condition. Discussion: Findings indicate that atorvastatin and lactulose combined supplementation could provide a more optimized treatment strategy for CD as opposed to atorvastatin or lactulose alone. Conclusion: The protocol anticipates the combined treatment to significantly improve colonic health in TNBS-induced colitis mice, resulting in decreased colonic ulcerations, diarrhea, and bowel wall thickness. Future studies should explore other inflammatory markers like CRPs and include different animal models to gain insights into treatment dosage and efficacy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".