Antioxidant and Anti-Bacterial Activity of Medicinal Plant Leda (Eucalyptus deglupta Blume) Extract
Bibliographic record
Abstract
Microbial infection is a prevalent global ailment, accounting for approximately 85% of deaths caused by infectious diseases such as diarrhea, acute respiratory infections, measles, AIDS, tuberculosis, and diarrhea.Therapeutic agents are a specific type of antimicrobial agents used to treat infections.Antimicrobial chemicals are compounds that can inhibit the growth or kill microbes.Leda (Eucalyptus deglupta Blume) is a medicinal plant with potential as a safe antimicrobial.The Kaili people residing near the Lore Lindu National Park have long used it to treat chronic diseases.This research utilized the ethanol extraction method.The phytochemical analysis included tests for alkaloids, triterpenoids, steroids, saponins, phenols, flavonoids, and quinones.The antioxidant activity was measured using the free radical capture method with 1,1-diphenyl-2-picrylhydrazyl (DPPH).The anti-bacterial test employed the agar disc diffusion method.The goal of this study was to investigate the antioxidant and anti-bacterial properties of the Leda medicinal plant extract.The results revealed the presence of alkaloid compounds, flavonoids, saponins, tannins, terpenoids, and carotenoids in the Leda extract.The extract from Leda leaves exhibited potential as a natural antioxidant (IC50 value of 120.31 ppm).The inhibition zone ranges of the Leda extract against the growth of Escherichia coli and Staphylococcus aureus bacteria were 12.67 ± 0.5 mm -19.77 ± 0.58 mm and 19.97 ± 0.76 mm -13.63 ± 0.29 mm, respectively.Therefore, Leda extract can be considered a potent antimicrobial agent.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".