The Effect of Pomegranate Peel Ethanol Extract to TNF-α Expression of Mice Colonic Epithelial Cells Induced Using Dextran Sodium Sulfate (DSS)
Bibliographic record
Abstract
Introduction:The conventional drugs for inflammatory bowel disease (IBD) have many side effects that impact patient's quality of life, leading to the emergence of alternative therapies such as pomegranate peel ethanol extract (PPE).This study aims to investigate the anti-inflammatory effect of PPE by observing TNF-α expression in mice induced chronic inflammation of the colon using dextran sodium sulfate (DSS).Methods: 28 Swiss Webster mice samples were taken and divided into five groups, the control group (6 mice), the negative control group (5 mice), the group that was given DSS and aspirin (6 mice), the group was given DSS and a high dose of PPE (5 mice), and the group was given DSS and a low dose of PPE (6 mice).In mice, distal colonic tissue was taken and then stained immunohistochemically against TNF-α and observed with light microscopy at 400x magnification, and TNF-α expression was assessed using the H-Score.Results: TNF-α expression was significantly lower in the group given a high dose of PPE than the negative control group (p <0.05), with mean rank scores of 3.00 and 8.00.There was no significant difference between the group given PPE with a high dose and aspirin (p> 0.05).Conclusion: TNF-α expression in colonic epithelial cells of mice given DSS decreased upon treatment of a high dose of PPE, indicating a mechanism of decreasing inflammation.PPE also has the same effect as aspirin in reducing inflammation.
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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".