Potential of Seaweed Gracilaria sp. As inhibitors of Escherichia coli, Clostridium perfringens and Stapylococcus aureus
Bibliographic record
Abstract
Abstract The problem of resistance and infectious pathogenicity of bacteria to humans is new at now. The search for alternative new drug compounds from seaweed bioactive content as one of the new antibacterial sources. The purpose of this research is to utilizeGracilaria sp. on the antibacterial effectiveness ofEscerichia coli,Clostridium perfringensandStapylococcus aureusThe method used in an experimental laboratory. Extraction was done by maceration with n-hexane, ethyl acetate and ethanol. Test antibacterial activity by agar diffusion method. Phytochemical screening based on discoloration. Analysis of bacterial cell leakage based on spectrophotometer results. Yields of 8.08% (ethanol), 5.47% (ethyl acetate) and 1.10% (hexane). Phytochemical screening results contain 6 secondary metabolite compounds in the ethanol and hexane treatment and 7 compounds in the ethyl acetate treatment. The best activity test results on ethyl acetate solvent with inhibition zone of 33.54 mm (Esherichia coli), 24.12 mm (Clostridium perfringens) and 29.14 mm (Stapylococcus aureus). MIC value at 0.51%. The absorbance obtained was 0.178 to 1.898 at a wavelength of 260 nm and 0.149 to 1,328 at a wavelength of 280 nm.
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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 | 0.001 | 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".