Streptozotocin-induced increase in cholesterol ester transfer protein (CETP) and its reversal by insulin in transgenic mice expressing human CETP
Bibliographic record
Abstract
High plasma triacylglycerol and low high-density lipoprotein levels are risk factors for cardiovascular disease in diabetes. Plasma high-density lipoprotein levels are regulated by cholesterol ester transfer protein (CETP). The regulation of CETP under diabetic conditions is not clear, and this is due to a lack of appropriate models. We used transgenic mice expressing human CETP to study the regulation of this protein under type-1 diabetic conditions and further investigated whether insulin reverses the effect of diabetes. Mice expressing human CETP under the control of its natural flanking region and age-matched littermates not expressing this protein were made diabetic by injecting streptozotocin, and the reversal of diabetes was assessed by injecting insulin. The plasma total cholesterol, low-density lipoprotein-cholesterol, and triacylglycerol concentrations were elevated, whereas high-density lipoprotein-cholesterol concentrations were reduced after the onset of diabetes. Insulin injection partially recovered this effect. The plasma cholesterol ester transfer activity, CETP mass, and hepatic CETP mRNA abundance were significantly higher in diabetic mice that were partially restored by insulin administration. There was a strong correlation between high-density lipoprotein-cholesterol concentrations and cholesterol ester transfer activity. These results suggest that an increase in CETP under diabetic conditions might be a major factor responsible for increased incidence of diabetes-induced atherosclerosis.
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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) | 0.000 | 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) | 0.002 | 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".