Preparation of Andrographis paniculata Extract Solid Dispersion by Hot-melt Extrusion Technology
Bibliographic record
Abstract
OBJECTIVE:To prepare Andrographis paniculata extract solid dispersion by using hot-melt extrusion(HME)technology,and to evaluate it in vitro. METHODS:The type of hydrophilic carrier and the proprotion of A. paniculata extract to carrier were screened by single factor test using andrographolide and dehydroandrographolide as index. The preparation technology of A.paniculata extract solid dispersion was optimized. The dissolution rate in vitro was studied,and DSC,SEM,X-ray diffraction were used to analyze the solid dispersion. RESULTS:The optimal technology of A. paniculata extract solid dispersion by HME were as follows:chosing polyethylene caprolactam-polyvinyl acetate-soluplus as the carrier;the ratio of A. paniculata extract to soluplus was 1 ∶ 2(m/m);the temperate-rasing program of HME should be 130→135→140→130 ℃;the rotation rate of screw arbor was 27r/min,feeding speed was 15 g/min. The results of physicochemical characterization experiment indicated that A. paniculata extract were in amorphous state in solid dispersion by HME. CONCLUSIONS:HME can disperse A. paniculata extract in amorphous state,and improve its solubility.
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 | 0.001 | 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) | 0.001 | 0.001 |
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".