Effect of chemical modifications of tannins on their antibiofilm effect against Gram-negative and Gram-positive bacteria
Bibliographic record
Abstract
Abstract (1) Background Tannins have demonstrated antibacterial and antibiofilm activity, but the mechanisms of action are not completely elucidated. We are interested in understanding how to modulate the antibiofilm activity of tannins and in delineating the relationship between chemical determinants and antibiofilm activity. (2) Materials and methods 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. (3) Results 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, with the positive charge introducing C 3 NMe 3 Cl-0.5 derivatization shifting the anti-biofilm spectrum towards Gram-negative bacteria and C 3 NMe 3 Cl-0.1 and the acidifying CH 3 COOH derivatization shifting the spectrum towards Gram-positive bacteria. Also, the quantity of phenolic-OH groups per molecule has a weak impact on the anti-biofilm activity of the tannins. (4) Conclusions 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 spectrum.
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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 | 0.000 | 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 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".