Impact of a high energy diet on obesity-associated immune biomarkers in Sprague Dawley rats (MUC2P.929)
Bibliographic record
Abstract
Abstract A trial was designed to examine effects of a high energy diet on obesity-associated biomarkers in Sprague-Dawley (SD) rats. Male rats (n = 176) were fed an AIN93G high-energy diet containing 16% dietary fat for 2 weeks, and segregated into obese-prone and obese-resistant phenotypes. Necropsies of animals displaying the highest (n = 10) and those displaying the lowest weight gain (n = 10) were conducted to examine potential differences in the concentration of immune biomarkers. Adipose, mucosal (ileum, ileal Peyer’s patches, cecum, proximal and distal colon), mesenteric lymph nodes (mln) and systemic (serum, liver, spleen) tissues were comparatively analyzed for cytokine or adipokine profiles. Obese rats had higher levels of leptin (P = 0.0001) and IL-22 (P = 0.059) in the distal colon and higher levels of IL-10 (P = 0.032) and TNF-α (P = 0.016) in the proximal colon compared to their obese-resistant counterparts. In obese-resistant rats, higher levels of CINC-2αβ (P = 0.048) occurred in mesenteric adipose tissue along with higher levels of soluble ICAM-1 (P = 0.018) in the mln. These findings suggest a high energy diet imparted differential effects on mucosal cytokine and adipokine production in colonic and mesenteric tissue between obese-prone and obese-resistant SD rats. Elevated levels of pro-inflammatory and regulatory cytokines are evident as little as 2 weeks after the initiation of a high-energy diet.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".