Hydro-Alcoholic Root Extracts of Ziziphus abyssinica is Effective in Diabetes Nephropathy and Diabetic Wound Healing
Bibliographic record
Abstract
Background: This study evaluated the potential of Ziziphus abysinnica root extract in managing hyperglycaemia in type 2 diabetes mellitus (T2DM), diabetic wound healing and diabetic nephropathy. Methodology: Blood glucose concentrations were measured daily for 14 days after daily administrations of either Ziziphus abysinnica (30, 100, and 300 mg/kg, p.o), metformin (300 mg/kg, p.o) or normal saline as negative control before diabetes induction using a single dose of Streptozotocin (60 mg/kg, i.p) and nicotinamide (120 mg/kg, i.p). Histopathological analysis was performed on the harvested kidneys following administration with Ziziphus abysinnica in diabetic rats. The diabetic wound healing potentials of the plant was also evaluated in streptozotocin-induced diabetic rats by treating them with 15%w/w ZAE ointment. Results: Generally, the percentage of blood glucose levels analysed following administration of drugs were found to be dose-dependent. The highest dose of ZAE (300 mg/kg) had a higher percentage reduction in blood glucose concentration when compared to metformin (300 mg/kg). The lowest dose (30 mg/kg) of ZAE administered attenuated STZ induced pathological damage and showed moderate to maximal improvement to the kidney nephrons. In contrast, the 100 mg/kg and 300 mg/kg dose ZAE demonstrated minimal pathological changes to the kidney architecture. Conclusion: Overall, our study demonstrated the antidiabetic potential of Ziziphus abysinnica, suggesting its possible therapeutic benefit in diabetic wound healing and diabetic nephropathy.
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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".