Antibacterial activity of freshwater green microalgae isolated from water bodies near abandoned mine sites in Ontario, Canada
Bibliographic record
Abstract
Progress of modern medicine relies on the discovery of new antibiotics. The increasing threat of antibiotic resistant bacteria, leading to an increase in morbidity and mortality of patients previously considered low risk, has highlighted the serious need for the expansion of antibiotic research and development. Historically, natural products have been the most successful source of antibiotics as they have complex and unique chemical structures and modes of action. Since most available antibiotics are originally a result of the secondary metabolism of bacteria and fungi, microorganisms from diverse environments capable of producing secondary metabolites have been and currently are being investigated for antibacterial production. Green microalgae are eukaryotic microorganisms that can be found in a very wide range of habitats, including extreme environments. These microorganisms are known to produce a series of commercially valuable compounds as a result of their secondary metabolism. The central aim of this thesis was to determine the potential of green microalgae as antibiotic producers collected (bioprospected) from water bodies near abandoned mine sites in Ontario, Canada. These water bodies exhibited a variety of chemical profiles, including high metal concentrations and low pH. Forty species of green microalgae were subsequently isolated and their extracts tested against various bacteria. The findings showed that 37.5% of these microalgae produced antibacterial compounds that seem to specifically inhibit the growth of Gram-positive bacteria, in particular the opportunistic pathogen Staphylococcus aureus. This was a higher success rate than any previous study on green microalgae. In addition, the evaluation of crude extracts of Chlamydomonas sp., the most common isolated species, demonstrated variation in antibacterial activity during cell growth. The highest antibacterial activity from this species was found in the exponential phase. Furthermore, green microalgal extracts exhibiting antibacterial activity also decreased the cell viability of malignant cells, particularly the rapidly dividing human ovarian carcinoma A2780 cells. However, the extracts did not decrease the cell viability of non-malignant cells. Taken together, the results of this thesis reveal that freshwater green microalgae from water bodies near abandoned mine sites are potential sources of antibacterial compounds against Grampositive bacteria and should be further investigated against rapidly dividing malignant cells.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".