Image_5_Effect of chemical modifications of tannins on their antimicrobial and antibiofilm effect against Gram-negative and Gram-positive bacteria.PDF
Bibliographic record
Abstract
Background<p>Tannins have demonstrated antibacterial and antibiofilm activity, but there are still unknown aspects on how the chemical properties of tannins affect their biological properties. We are interested in understanding how to modulate the antibiofilm activity of tannins and in delineating the relationship between chemical determinants and antibiofilm activity.</p>Materials and methods<p>The effect of five different naturally acquired tannins and their chemical derivatives on biofilm formation and planktonic growth of Salmonella Typhimurium, Pseudomonas aeruginosa, Escherichia coli and Staphylococcus aureus was determined in the Calgary biofilm device.</p>Results<p>Most of the unmodified tannins exhibited specific antibiofilm activity against the assayed bacteria. The chemical modifications were found to alter the antibiofilm activity level and spectrum of the tannins. A positive charge introduced by derivatization with higher amounts of ammonium groups shifted the anti-biofilm spectrum toward Gram-negative bacteria, and derivatization with lower amounts of ammonium groups and acidifying derivatization shifted the spectrum toward Gram-positive bacteria. Furthermore, the quantity of phenolic OH-groups per molecule was found to have a weak impact on the anti-biofilm activity of the tannins.</p>Conclusion<p>We were able to modulate the antibiofilm activity of several tannins by specific chemical modifications, providing a first approach for fine tuning of their activity and antibacterial spectrum.</p>
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".