Impact of <i>in vivo</i> glycation of LDL on platelet aggregation and monocyte chemotaxis in diabetic <i>Psammomys obesus</i>
Bibliographic record
Abstract
Psammomys obesus (sand rat) is an appropriate model to highlight the development of hyperinsulinemia, insulin resistance, obesity, and diabetes. This animal species, with genetically predetermined diabetes, acquires non-insulin dependent diabetes mellitus when exposed to energy-rich diets. In the present study, we explored the possibility that glycation of LDL may occur in diabetes-prone P. obesus and affect platelet and macrophage functions. The glycation of LDL, isolated from diabetic animals, was significantly (P < 0.05) higher (40%) than that of control animals. The incubation of platelets with glycated LDL enhanced the reactivity of platelets by 32-44% depending on the aggregating agents (thrombin, collagen, ADP). Furthermore, LDL derived from diabetic rats were chemotactic for normal monocytes and stimulated the incorporation of [14C]oleate into cellular cholesteryl esters. The enhancement of platelet aggregation and cholesterol esterification in monocytes may contribute toward the accelerated development of atherosclerotic cardiovascular disease in diabetic P. obesus animals. This study also illustrates the relevance of studying atherosclerosis in the P. obesus animal model, as it shows an increased tendency to develop diet-induced diabetes, which is associated with cardiovascular disorders.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".