A new rat model of diabetic macrovascular complication
Bibliographic record
Abstract
OBJECTIVES: Age-related medial calcification (elastocalcinosis) of large arteries is accelerated in diabetes and appears mainly in distal arteries. The aim was to devise a rat model of elastocalcinosis in association with diabetes to examine the hypothesis that diabetes accelerates vascular calcification experimentally. METHODS: Male Wistar rats received a high fat diet during 2 months followed by a low dose of streptozotocin to induce diabetes (D). Elastocalcinosis was facilitated by 3 weeks of treatment with warfarin and vitamin K (WVK). We started WVK treatment 1 week (D4WVK) and 4 weeks (D7WVK) after the injection of streptozotocin and in age-matched healthy rats. Measurements of hemodynamic and metabolic parameters, aortic and femoral calcium content, and immunohistochemistry for alkaline phosphatase, osteopontin, tumor necrosis factor (TNF)-alpha, and transforming growth factor (TGF)-TGF-beta were performed. RESULTS: Three weeks of WVK treatment alone did not increase the calcium content in the aorta and femoral arteries. However, in the D7WVK group, femoral calcification, but not aortic calcium content, increased significantly as compared to the WVK group. This response was not observed in the D4WVK group. In femoral arteries, strong immunostaining for alkaline phosphatase and osteopontin was observed in the D7WVK group. TNF-alpha and TGF-beta expressions were mainly localized in the adventitia of arteries from diabetic rats. CONCLUSION: We have established a model of accelerated elastocalcinosis in diabetes related to its duration and localized in distal arteries. The modification of local protein expression is also in accordance with clinical data, suggesting that this model could be useful to investigate mechanisms related to this important clinical macrovascular complication of diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".