Effect of Different Doses of Sea Buckthorn (Hippophae rhamnoides L.) Seed Extract (Oil) on Histology of Islets of Langerhan of Pancreas in Alloxan Induced Diabetic Male Wistar Rats
Bibliographic record
Abstract
Background: Sea buckthorn seed oil extract contains saturated and unsaturated fatty acids. Alloxan monohydrate is oxygenated pyrimidine derivative, it produces diabetes in experimental animals. The objectives of this study was to determine the histological changes of sea buckthorn extract (seed oil) on Alloxan induced diabetic β-cells of pancreatic tissue in male Wistar rats. Method: 32 male Wistar rats were divided into 4 groups: A, B, C and D. Alloxan monohydrate in dose of 130mg/kg body weight was given as a single dose to experimental groups B, C and D. From each group half of the group the rats were sacrificed at day 4 of study and the rest on 25th day after administration of Sea buckthorn seed oil extract. Results: In comparison with the control group on the 4th day of the sacrifice all the rats in the experimental groups showed a decrease in diameter of islets of Langerhans, number of beta cells and an increase in number of beta cells pyknosis and vacuolization. On the 25th sacrificial day, only high dose Sea buckthorn (4ml) treated group D showed an increased in diameter of islets of Langerhans very close to that of controls while low dose (2ml) treated group C showed late recovery as compared to control group. Conclusion: The study assesses Sea buckthorn’s (seed oil) effects on serum glucose levels by using glucometer and histological parameters (diameter of islet, beta cell pyknosis, number of inflammatory cells) of islets of Langerhans of pancreas. It focuses on animal models, but human clinical trials are required to validate its efficacy and safety.
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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) | 0.002 | 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".