Facile synthesis of bromelain copper nanoparticles to improve the primordial therapeutic potential of copper against acute myocardial infarction in diabetic rats
Bibliographic record
Abstract
Our current investigation comprises the synthesis and pharmacological impact of bromelain copper nanoparticles (BrCuNP) against diabetes mellitus (DM) and associated ischemia/reperfusion (I/R) – induced myocardial infarction. Bromelain is a proteolytic enzyme obtained from Ananas comosus L. Merr., which has blood platelet aggregation inhibiting and arterial thrombolytic potential. Moreover, copper is well-known to facilitate glucose metabolism and strengthen cardiac muscle and antioxidant activity; although, chronic or long-term exposure to high doses of copper may lead to copperiedus. To restrict these potential hazards, we synthesized herbal nano-formulation which convincingly indicated the improved primordial therapeutic potential of copper by reformulating the treatment carrier with bromelain, resulting in facile synthesis of BrCuNP. DM was induced by administration of double cycle repetitive dose of low dose streptozotocin (20 mg/kg, i.p.) in high-fat diet- fed animals. DM and associated myocardial I/R injury were estimated by increased serum levels of total cholesterol, low-density lipoprotein, very low-density lipoprotein, lactate dehydrogenase, creatine kinase myocardial band, cardiac troponin, thiobarbituric acid reactive substances, tumor necrosis factor α, interleukin 6, and reduced serum level of high-density lipoprotein and nitrite/nitrate concentration. However, treatment with BrCuNP ameliorates various serum biomarkers by approving cardioprotective potential against DM- and I/R-associated injury. Furthermore, upturn of histopathological changes were observed in cardiac tissue of BrCuNP-treated rats in comparison to disease models.
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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) | 0.000 | 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 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".