Optimization of Andrographolide Solid Lipid Nanoparticles by Central Composite Design and Response Surface Methodology
Bibliographic record
Abstract
Andrographolide solid lipid nanoparticles were prepared by high pressure homogenization.The influence of the ratio of drug to lipid materials(the mixture of glycerin monostearate and Compritol ATO 888 with the ratio of 1∶1),ratio of lecithin to lipid materials and the concentration of surfactant(Tween-80) on entrapment efficiency and drug loading were investigated by central composite design.The results fit with multiple linear and binomial equation and the optimal formulation was predicted by response surface methodology.The results showed that there was a well correlation for drug loading by multiple linear regression while for entrapment efficiency the binomial equation was superior to the multiple linear regression.The optimal formulation was as follows: the ratio of drug to lipid materials was 9%,the ratio of lecithin to lipid materials was 1.6,and the concentration of Tween-80 was 3%.The entrapment efficiency,drug loading,mean diameter and ζ potential of the product were(91.0±0.9)%,(3.49±0.03)%,(286.3±8.0)nm and(-20.6±0.2)mV,respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".