Phytochemical Composition, Gas Chromatography-Mass Spectrometric (GC-MS) Analysis and Anti-Bacterial Activity of Ethanol Leaf-Extract of Ageratum conyzoides
Bibliographic record
Abstract
The study was designed to evaluate the phytochemical composition, GC-MS analysis and antibacterial activity of ethanol leaf-extract of Ageratum conyzoides . The phytochemical and antibacterial activity of Ageratum conyzoides leaf extract were carried out using standard methods while the GC-MS analysis was done using gas chromatography-mass spectrometric method. The result of phytochemical analysis revealed the presence of alkaloids, tannins, saponins, glycoside, flavonoids, resins, terpenoids and phenol. The result of GC-MS analysis showed the presence of 23 chemical constituents which include: 5-(1-methylidene)-1,3- methylidenecyclopentane (14.6%), nonane (18.2%), propan-2-ylcyclohexane(8.9%), (1-methylethyl) benzene (9.1%) and hexanoic acid (4.3%) as the major chemical constituents. The susceptibility test of the ethanol leafextract against septic wounds organism, showed higher value of 41.00 mm zone of inhibition on Staphylococcus , 26.00mm on Escherichia coli , 25.00mm on Klebsiella , 23.00mm on Streptococcus and low value of 20. 00mm on Pseudomonas after the antimicrobial analysis test on the organisms. This indicates that A. conyzoides is rich in bioactive compounds and sensitive to organisms of septic wounds and could be used for treatment/cure of diseases.
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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".