Inhibition of endoplasmic reticulum stress combined with activation of angiotensin-converting enzyme 2: novel approach for the prevention of endothelial dysfunction in type 1 diabetic rats
Bibliographic record
Abstract
Persistent hyperglycemia in type 1 diabetes triggers numerous signaling pathways, which may prove deleterious to the endothelium. As hyperglycemia damages the endothelial layer via multiple signaling pathways, including enhanced oxidative stress, downregulation of angiotensin-converting enzyme 2 signaling, and exacerbation of endoplasmic reticulum (ER) stress, it becomes difficult to prevent injury using monotherapy. Thus, the present study was conceived to evaluate the combined effect of ER stress inhibition along with angiotensin-converting enzyme 2 activation, two major contributors to hyperglycemia-induced endothelial dysfunction, in preventing endothelial dysfunction associated with type 1 diabetes. Streptozotocin-induced diabetic animals were treated with either diminazene aceturate (5 mg·kg−1 per day, p.o.) or tauroursodeoxycholic acid, sodium salt (200 mg·kg−1 per day i.p.), or both for 4 weeks. Endothelial dysfunction was evaluated using vasoreactivity assay, where acetylcholine-induced relaxation was assessed in phenylephrine pre-contracted rings. Combination therapy significantly improved vascular relaxation when compared with diabetic control as well as monotherapy. Restoration of nitrite levels along with prevention of collagen led to improved vasodilatation. Moreover, there was an overall reduction in aortic oxidative stress. We conclude that by simultaneously inhibiting ER stress and activating angiotensin-converting enzyme 2 deleterious effects of hyperglycemia on endothelium were significantly alleviated. This could serve as a novel strategy for the prevention of endothelial dysfunction.
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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 | 0.000 | 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 | 0.001 |
| 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".