The antimicrobial potential of traditional remedies of Indigenous Peoples from Canada against MRSA planktonic and biofilm bacteria in wound-infection mimetic conditions
Bibliographic record
Abstract
Abstract Methicillin-resistant Staphylococcus aureus (MRSA) is the leading cause of wound infections, often progressing into serious invasive bloodstream infections. MRSA disproportionately affects Indigenous peoples in Canada with higher rates of skin and wound infections, an example of persistent gaps in health outcomes between Indigenous and non-Indigenous peoples precipitated by the legacy of colonialism. Conversely, Indigenous peoples have long used natural remedies for infections and other diseases; however, their knowledge was rarely considered for modern medicine. The stagnant antibiotic discovery pipeline and alarming rise of resistance to current antibiotics prompted us to turn to Indigenous medicine as an untapped source of antimicrobials. As such, we collected and prepared 85 extracts of medicinal plants of value to Indigenous Peoples spanning the Canadian Prairies. We explored the antimicrobial potential of these extracts against MRSA under wound infection-mimetic conditions compared to culture media typically used to study bacterial antibiotic responses and biofilms but not adequately representative of infection sites. We identified extracts with MRSA growth inhibitory [e.g., bergamot, dock, gaillardia, and dandelion extracts] and biofilm prevention and eradication [e.g., gumweed extracts] activities. Extracts, including those of chokecherry, hoary puccoon, and Northern bedstraw, were only active under wound infection-mimetic conditions, highlighting the relevance of antibiotic discovery under host-relevant conditions. Testing growth inhibitory extracts against an S. aureus cross-resistance platform suggested that they act through mechanisms likely distinct from known antibiotic classes. Together, through an interdisciplinary partnership leveraging Western approaches and traditional Indigenous knowledge, we identified plant extracts with promising antimicrobial potential for drug-resistant MRSA wound infections.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".