Antimicrobial Screening of Phytochemicals Produced by Albertan Invasive Weeds
Bibliographic record
Abstract
Antibiotic resistance has rendered many clinically-used antibiotics ineffective, creating an urgent need for new antimicrobial agents. Phytochemicals (secondary metabolites produced by plants) are produced in response to environmental stressors, and can inhibit the growth of bacteria, fungi and surrounding plants. Therefore, these phytochemicals offer an alternative source of antimicrobial compounds. The diversity and abundance of phytochemicals produced by plants can increase during the invasion of new habitats, making invasive weeds strong candidates for antimicrobial discovery. Despite this increase in phytochemical production, invasive plant species are often overlooked in favour of medicinal and edible plants, and few studies have characterized their antimicrobial activity. In this research, we used successive Soxhlet extractions with hexane, ethyl acetate, and methanol to extract the phytochemicals from Albertan invasive weed species collected from the Edmonton area. Using Kirby-Bauer disk diffusion assays, extracts were assessed for their ability to inhibit the growth of tester bacterial species including Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa, and Staphylococcus aureus, which represent a range of common pathogens and bacterial types. Preliminary characterizations of extracts from multiple plant species, including common tansy (Tanacetum vulgare) and woolly burdock (Arctium tomentosum), showed promising inhibitory activity against several bacterial species, warranting further investigation. This study provides a starting point for further bioactivity and chemical characterizations of Albertan invasive weeds and highlights these invasive plant species as potential leads for the development of new antimicrobial treatments. Department: Biological Sciences Faculty Mentors: Dr. Kimberley Harcombe and Dr. Tina Bott
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".