Endothelin‐mediated remodeling in aortas of diabetic rats
Bibliographic record
Abstract
BACKGROUND: Smooth muscle cells proliferation and extracellular matrix (ECM) protein deposition are key features of diabetic macroangiopathy. In the present study, we have studied the role of endothelin(A) (ET(A)) receptor, the predominant receptor on smooth muscle cells, in diabetes-induced vascular hypertrophy and remodeling. METHODS: Streptozotocin-induced diabetic rats were administrated a selective ET(A) receptor antagonist, TBC3214, for 26 weeks. Following treatment, aortas were harvested and subjected to gene expression and morphometric analyses. We quantified fibronectin (FN) and plasminogen activator inhibitor-1 (PAI-1) expression as indicators of increased ECM protein synthesis. ET-1, ET-3, transforming growth factor-beta1 (TGF-beta1) and angiotensinogen mRNA levels were measured to elucidate genes involved in FN expression. We have investigated an embryonic splice variant of FN, oncofetal FN, and nonmuscle myosin heavy chain (SMemb) as vascular remodeling indicators. RESULTS: Our results show that diabetes leads to upregulation of FN, PAI-1, ET-1, ET-3, TGF-beta1 and angiotensinogen mRNA levels in association with increased medial thickness. Immunohistochemical analyses revealed concurrent protein level changes. Diabetes also upregulated oncofetal FN and SMemb mRNA levels. Treatment with TBC3214 attenuated the mRNA levels of several genes and prevented increased medial thickness. CONCLUSIONS: These results indicate that diabetes-induced vascular hypertrophy and remodeling is associated with reexpression of embryonic forms of FN and myosin heavy chain. Such changes are ET-dependent and may be mediated via TGF-beta1 and angiotensin.
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 | 0.000 | 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) | 0.001 | 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".