Combination of high-fat/high-fructose diet and low-dose streptozotocin to model long-term type-2 diabetes complications
Bibliographic record
Abstract
Abstract The epidemic of type 2 diabetes mellitus (T2DM) is fueled by added fructose consumption. Here, we thus combined high-fat/high-fructose diet, with multiple low-dose injections of streptozotocin (HF/HF/Stz) to emulate the long-term complications of T2DM. HF/HF/Stz rats, monitored over 56 weeks, exhibited metabolic dysfunctions associated with the different stages of the T2DM disease progression in humans: an early prediabetic phase characterized by an hyperinsulinemic period with modest dysglycemia, followed by a late stage of T2DM with frank hyperglycemia, normalization of insulinemia, marked dyslipidemia, hepatic fibrosis and pancreatic β-cell failure. Histopathological analyses combined to [18F]-FDG PET imaging further demonstrated the presence of several end-organ long-term complications, including reduction in myocardial glucose utilization, renal dysfunction as well as microvascular neuropathy and retinopathy. We also provide for the first time a comprehensive µ-PET whole brain imaging of the changes in glucose metabolic activity within discrete cerebral regions in HF/HF/Stz diabetic rats. Altogether, we developed and characterized a unique non-genetic preclinical model of T2DM adapted to the current diet and lifestyle that recapitulates the major metabolic features of the disease progression, from insulin resistance to pancreatic β-cell dysfunction, and closely mimicking the target-organ damage occurring in type 2 diabetic patients at advanced stages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".