Activity of extracts and terpenoids from <i>Tontelea micrantha</i> (Mart. ex Schult.) A.C.Sm. (Celastraceae) against pathogenic bacteria
Bibliographic record
Abstract
The pharmacological properties of plant extracts and phytochemicals, such as flavonoids and terpenoids, remain of great interest. In this work, the effect of extracts, friedelan-3,21-dione, and 3β-O-D-glucosyl-sitosterol isolated from Tontelea micrantha roots was evaluated against Staphylococcus aureus, Bacillus subtilis, Klebsiella pneumoniae, Klebsiella oxytoca and Escherichia coli. The antibacterial activity was evaluated by the minimum inhibitory and bactericidal concentrations (MIC and MBC, respectively), and the synergistic effect was assessed by the Checkerboard assay. Furthermore, the cytotoxicity of the plant-derived compounds against Vero cells was measured by the 3-(4 5-dimethylthiazol-2-yl)-2 5-diphenyltetrazolium bromide (MTT) method. The biological effects of the isolated compounds were predicted using the PASS online software. The chloroform and hexane extracts of T. micrantha roots showed promising antibacterial effect, with MIC in the range of 4.8–78.0 µg/mL. Further analyses showed that these compounds do not affect the integrity of the membrane. The combination with streptomycin strongly reduced the MIC of this antibiotic and extracts. The extracts were highly toxic to Vero cells, and no cytotoxicity was detected for the two terpenoids isolated from them (i.e. friedelan-3,21-dione and 3β-O-D-glucosyl-sitosterol; CC50 > 1000 μg/mL). Therefore, extracts obtained from T. micrantha roots significantly inhibited bacterial growth and are considered promising agents against pathogenic bacteria. The cytotoxicity results were very relevant and can be tested in bioassays.
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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) | 0.001 | 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 | 0.000 |
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".