Nanoformulation of valsartan-loaded tablet attenuates L-NAME-induced hypertension: role of Nrf2/PPARγ/AT1 signaling pathway
Bibliographic record
Abstract
Hypertension is the most common entity globally, marked by high prevalence and heterogeneous pathophysiology. Oxidative stress is a crucial area of investigation among potential etiologies. We examined the hypothesis that blocking the angiotensin type 1 (AT1) receptor with valsartan (VST) in self-nanoemulsifying delivery systems (SNEDS) and loads in liquisolid tablets (LST-1) or valsartan and hydrochlorothiazide (VST/HCTZ) in SNEDS and loads in liquisolid tablets (LST-2) in comparison with non-SNEDS liquisolid tablets (DCT-3 and DCT-4) would lead to an improvement in hypertension management. The present study aims to explore the molecular mechanisms underlying their effect in N(G)-nitro-L-arginine methyl ester (L-NAME)-induced hypertensive rats. Male Sprague-Dawley rats were given L-NAME (40 mg/kg/day) orally for three weeks to inhibit the endogenous synthesis of nitric oxide (NO). Concurrent treatment with VST or VST/HCTZ liquisolid tablets (20 mg/kg/day for three weeks) resulted in lowering blood pressure (BP), reversing the L-NAME-induced serum NO suppression, enhancing lipid profile, and improving oxidative status. The antioxidant defense of paraoxonase was significantly increased in the LST-1- and LST-2-treated rats compared to the L-NAME-treated rats by 135% and 90%, respectively. Furthermore, SNEDS-loaded VST or SNEDS-loaded VST/HCTZ liquisolid tablets significantly lowered the elevated level of AT1 (P < 0.05), showed a marked Nrf2 expression (P < 0.01) and overexpressed PPARγ (P < 0.05), and suppressed iNOS expression (P < 0.0001). These results highlight the remarkable benefits of the novel formula, "SNEDS-loaded VST and SNEDS-loaded VST/HCTZ," as an alternative therapy in treating hypertension and its complications.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".