Development and characterization of a high fat diet-streptozotocin induced type 2 diabetes model in nude athymic rats
Bibliographic record
Abstract
Abstract People with diabetes mellitus (DM) are at an increased risk for myocardial infarction (MI) than age matched people without DM. However, assays for pre-clinical therapy are performed in animal models of ischemia that lack the co-morbid conditions present in patients with MI, such as DM. This contributes to the failure to translate pre-clinical trials results to the clinic. Thus, to increase the clinical relevance of xenograft studies in pre-clinical models, it is important to have a DM model in animals that are immunodeficient. Here, we developed a type 2 diabetes mellitus (T2D) model in nude athymic rats using high-fat diet and streptozotocin (HFD-STZ). Nude athymic rats were randomized into a control group (normal chow) or a HFD (45% fat, 20% protein and 35% carbohydrate)-STZ group. STZ (35 mg/kg i.v.) or vehicle was administered 8 weeks after HFD feeding started. Assessments were done longitudinally and at week 9 (endpoint). The HFD-STZ group showed mild hyperglycemia pre STZ administration (7.7 ± 0.3 mM vs 5.8 ± 0.2 mM in control) by week 8. In addition, plasma insulin levels were increased and the HOMA index was 2.5-times higher in the HFD-STZ. The HFD-STZ group showed increased fasting (147%) triglycerides. After STZ-administration, blood glucose levels increased substantially (23.6 ± 1.4 mM vs 5.5 ± 0.3 mM in control). The HFD-STZ treated animals also showed increased left ventricular wall thickness, cardiac hypertrophy and fibrosis, reduced cardiac function compared to normal chow control. In line with the HFD-STZ model in immunocompetent rats, the HFD-STZ treatment of athymic rats recapitulates key features of T2D, including aspects of established clinical diabetic cardiomyopathy and should be suitable for xenograft studies in the context of T2D.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".