Effects of Various Preextraction Treatments of Crinum asiaticum Leaf on Its Anti-Inflammatory Activity and Chemical Properties
Bibliographic record
Abstract
Crinum asiaticum Linn. has been used in Thai traditional medicine to relieve inflammatory symptoms and treat osteoarthritis. There have been reports on its potent anti-inflammatory property but nothing on the effects of different pretreatments on its chemical properties and anti-inflammatory activity. Pretreatment of herbal raw materials is an important step which affects the overall quality of Thai traditional medicine. The objectives of this study were to investigate different treatments of C. asiaticum leaves prior to ethanolic extraction and to compare the extracts for their anti-inflammatory activity and chemical properties. The treatments included hot air drying in an oven, microwave drying, traditional grilling on a charcoal stove before drying in an oven, and temperature shock in hot and cold water before hot air drying. The anti-inflammatory activity and chemical properties of the extracts were analyzed using the established methods. Results showed that 95% ethanolic extract of hot air oven-dried leaves had the highest anti-inflammatory activity and total phenolic and lycorine contents. We recommend hot air drying as a preextraction treatment for C. asiaticum leaves for its simplicity, best retention of the herbal quality, and suitability for scaling up to an industrial process.
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) | 0.002 | 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".