Diazepam Loaded Solid Lipid Nanoparticles: <i>In Vitro</i> and <i>in Vivo</i> Evaluations
Bibliographic record
Abstract
Purpose: To overcome side effects of repetitive administration of Diazepam (Dzp) besides gaining benefits from sustaining release (SR) of the drug, which contributes to patient compliance, we concentrated on designing and preparing Dzp Solid Lipid Nanoparticles (SLNs). Methods: Using cholesterol (CHOL), stearic acid (SA) and glycerol monostearate (GMS), SLNs were prepared by high shear homogenization technique coupled with sonication. Polysorbate 80 (Tween 80) was used as a nonionic surfactant. After modification of prepared SLNs, particle size, zeta potential, drug-loading efficiency, morphology and scanning calorimetry as well as release studies were conducted. To increase the stability of desired particles, freeze-drying by cryoprotectant was carried out. In the final stage, In-vivo study was performed by oral (PO) and intraperitoneal (IP) administration to Wistar male rats. Results: Results indicated that optimized prepared particles were in average 150 nm diameter in spherical shape with 79.06 % loading efficiency and release of more than 85% of loaded drug in 24 hours. In-vivo investigations also illustrated differences in blood distribution of Dzp after loading this drug into SLNs. Conclusion: Based on the findings, it seems that drug delivery using SLNs could be an opportunity for solving complications of Dzp therapy in future.
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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".