Phytochemical Screening and Antibacterial Test of Leaf Extract of Canar Susu (Smilax macrocarpa Blume) Against Eschercihia coli, Pseudomonas aeruginosa, and Staphylococcus epidermidis
Bibliographic record
Abstract
Smilax is one of the genus Smilacaceae is widely used as a medicinal plant because it contains various secondary metabolites with some bioactivity, such as anti-inflammatory, antirheumatic, analgesic, antioxidant, anticancer and antibacterial. One species of Smilax that has not been studied and only grew in Indonesia is Smilax macrocarpa Blume (canar susu). Therefore, a preliminary study of phytochemicals and biological activities is required to encourage progress and novelty in science and to know its phylogenetics in Indonesia's biodiversity. The research was done by extraction method using maceration with methanol as a solvent. Simplicia characteristic, toxicity test with BSLT method, phytochemical screening according to Harborne method, and antibacterial activity test using microdilution against including Escherihia coli, Pseudomonas aeruginosa, and Staphylococcus epidermidis were performed to leaf extract of canar susu. The results obtained that methanol extract of canar susu leaves contains alkaloids, flavonoids, steroids, tannins, terpenoids, saponins, and glycosides. Water content, ash content, acid-soluble ash content, water sari content, and alcohol sari concentration 8.74%; 3.60%; 0.11%; 19.01% and 5.40% respectively. Toxicity results obtained LC50 680.07 ppm. Antibacterial activity test against E. coli has MIC 625 ppm, whereas in P. aeruginosa and S. epidermidis ATCC 12228 are 1.250 ppm. The MBC values for E. coli, P. aeruginosa, and S. epidermidis ATCC 12228 were 5,000 ppm. Based on this result known S. macrocarpa Blume is not potential as antibacterial, but potential as biopesticide according to toxicity result.
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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 | 0.001 |
| 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) | 0.002 | 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".