Cardioprotective and antihypertensive effects of <i>Enicostemma littorale</i> Blume extract in fructose-fed rats
Bibliographic record
Abstract
We investigated the protective effects of Enicostemma littorale Blume (EL) extract on hypertension and insulin resistance along with its associated cardiovascular complications in high fructose (HF) fed rats. For this, rats were divided among 4 groups: (i) control, fed laboratory chow; (ii) fed with a high level of fructose; (iii) fed with a high level of fructose plus E. littorale extract; and (iv) fed with a high level of fructose plus rosiglitazone (Rg). EL and Rg treatments were given simultaneously with HF diet. The results show that untreated HF-fed rats showed altered oral glucose tolerance, increased fasting insulin, and increased fasting glucose. These rats also exhibited hypertriglyceridemia, moderate hypertension, platelet hyperaggregability, decreased prothrombin time, activated partial thromboplastin time, altered vascular reactivity, and increased serum levels of enzymes (creatine kinase, type muscle-brain (CK-MB), aspartate aminotransferase (SGOT), lactate dehydrogenase (LDH), and alanine aminotransferase (SGPT). This is the first demonstration of platelet hyperaggregation and prothrombotic alteration in HF-fed rats. HF-fed rats treated with EL showed improved insulin resistance, along with reduced hypertriglyceridemia, hypertension, platelet aggregability, blood coagulation, serum enzymes (CK-MB, SGOT, LDH and SGPT), and vascular reactivity. These effects of EL in HF-induced hypertensive rats might be associated with the suppression of hyperinsulinemia and hypertriglyceridemia, along with its antiatherogenic and antithrombogenic potential. These data indicate that the aqueous extract of EL has great therapeutic potential for the prevention and (or) management of insulin resistance and the associated hypertension.
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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".