Toxicity–structure activity evaluation of limonoids from<i>Swietenia</i>species on<i>Artemia salina</i>
Bibliographic record
Abstract
CONTEXT: Many plant extracts and compounds are being investigated for their cytotoxicity and hence their medicinal or therapeutic properties. Reports of toxicity studies with limonoid analogs have been sparse and have involved mainly crude extracts. In this study, individual natural limonoids have been isolated and their toxicity manipulated via semisynthesis. OBJECTIVE: The lethality of limonoid analogs from Swietenia macrophylla King and Swietenia aubrevilleana Stehlé & Cusin (Meliaceae) against Artemia salina Leach was determined. MATERIALS AND METHODS: Four known natural limonoids were isolated from the dry ground seeds of S. macrophylla and S. aubrevilleana, modified using acylation and hydrolysis reactions and tested in A. salina lethality assays at 1-400 ppm. A 50% lethal concentration (LC(50)) was determined by probit analysis. RESULTS: Higher levels of toxicity were achieved in most of the prepared analogs compared with the parent natural limonoids. The compound showing the highest toxicity with LC(50) 3.9 ppm was 3-O-benzoyl-3-detigloylisoswietenine (20). Other analogs with high toxicity were 6-O-benzoylswietenolide (7), 6-O-benzoylswietenine (17), and 3,6-O,O-dipropionylswietenolide (9), which showed LC(50) values of 4.3, 7.5, and 28.5 ppm, respectively. DISCUSSION AND CONCLUSIONS: Toxicity can be improved via semisynthesis. The compounds exhibiting high toxicity (low LC(50)) may be good candidates for cytotoxicity studies.
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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) | 0.001 | 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".