Phytochemical Composition, Gas Chromatography-Mass Spectrometric (GC-MS) Analysis and Anti-Bacterial Activity of Ethanol Leaf-Extract of Ageratum conyzoides
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it