Differential adipokine response in genetically predisposed lean and obese rats during inflammation: a role in modulating experimental colitis?
Bibliographic record
Abstract
The relationship between a predisposition to obesity and the development of colitis is not well understood. Our aim was to characterize the adipokine response and the extent of colitis in diet-induced obese (DIO) rats. DIO and control, diet-resistant (DR) animals were administered either saline or trinitrobenzene sulfonic acid (TNBS) to induce colitis. Macroscopic damage scores and myeloperoxidase (MPO) activity were measured to determine the extent of inflammation. Trunk blood was collected for the analysis of plasminogen activator inhibitor-1 (PAI-1) as well as leptin, ghrelin, and adiponectin. Colonic epithelial physiology was assessed using Ussing chambers. DIO rats had a modestly increased circulating PAI-1 before TNBS treatment; however, during colitis, DR animals had more than a fourfold increase in circulating PAI-1 compared with DIO rats. Circulating leptin was higher in DIO rats compared with DR animals, in the inflamed and noninflamed states. These changes in TNBS-induced adipokine profile were accompanied by decreased macroscopic tissue damage score in DIO animals compared with DR tissues. Furthermore, TNBS-treated DR animals lost significantly more weight than DIO rats during active inflammation. Colonic epithelial physiology was comparable between groups, as was MPO activity. The factors contributing to the decreased colonic damage are almost certainly multifold, driven by both genetic and environmental factors, of which adipokines are likely to play a part given the increasing body of evidence for their role in modulating intestinal 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".