Development of oral formulation of <i>Lepidium</i> seeds significantly decreases the high blood glucose levels in diabetic rats: <i>in vitro</i> formulation and <i>in vivo</i> antidiabetic performance
Bibliographic record
Abstract
Background Lepidium sativum, Garden Cress (GC), seeds have a lot of natural molecules with a pronounced activity against different disorders. It was reported that GC seeds have the ability to lower the blood glucose level.Aim The aim of this work was to formulate GC seeds into oral tablets containing a fixed dose of the grounded seeds. Furthermore, the anti-diabetic performance of the prepared tablets was studied in the streptozotocin rats’ model in comparison with positive control metformin.Methods Micrometrics of GC grounded seeds with different excipients were investigated. Then, GC tablets were prepared via direct compression technique. GC tablets were characterized for their uniformity of dosage unit, friability, hardness, disintegration time, and in vitro release. The antidiabetic effect was studied in rats for a period of 28 days. Glycosylated hemoglobin, liver performance, and lipid levels include total cholesterol (TC), triglycerides (TGs), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were also estimated. In addition, histopathological study of liver and pancreas was also performed.Results Prosolv®EasyTab produced tablets with higher hardness, lower disintegration time, and fast release. GC tablets significantly lower the elevated blood glucose level. In addition, they have antihyperlipidemic activity, hepatocellular protective role and restore the histology of the liver and pancreas.Conclusion GC tablets could be a promising alternative formulation to control the high blood glucose level in diabetic rats rather than chemically derivatized drugs.
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 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 | 0.001 |
| 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".