Screening phytochemical extracts of invasive Albertan weeds for anti-biofilm properties
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
Biofilms, complex bacterial communities encased in a secreted matrix, are highly pervasive and problematic in health care settings. This lifestyle allows bacteria to anchor in a particular location and provides high intrinsic antibiotic resistance to the biofilm community, rendering biofilm infections difficult to remove once established. Effective anti-biofilm compounds for clinical use are lacking and are urgently needed. Plants may be an excellent natural source of anti-biofilm compounds given the prevalence of phytochemicals used as a defence mechanism. However, despite their rich phytochemical diversity, plants, and invasive weeds especially, have remained understudied for their anti-biofilm properties. In this study, we screened phytochemical extracts from invasive Albertan weeds for anti-biofilm properties against Escherichia coli, Bacillus licheniformis, Pseudomonas aeruginosa, and Staphylococcus aureus biofilms by testing the capability of the plant extracts to inhibit biofilm formation. Single-species adherent biofilms were grown in liquid culture in 96-well microtiter plates, and were exposed to increasing concentrations of a single plant extract during biofilm formation. To quantify total biofilm biomass, biofilms were stained with crystal violet and absorbance was measured at 595 nm. Preliminary analysis identified novel anti-biofilm activity in tested extracts, including an ethyl acetate extract from Leafy Spurge flowers and leaves. This work will contribute to existing knowledge of phytochemicals with anti-biofilm properties, which can be further developed into therapeutic treatments. In addition, this project provides preliminary identification of these compounds in invasive Albertan weeds, laying the groundwork for future studies on these extracts. Department: Biology Faculty Mentor: Dr. Kimberley Harcombe
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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 | 0.001 |
| 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 it