Antibiofilm and antibacterial effects of specific chitosan molecules on Staphylococcus aureus isolates associated with bovine mastitis
Bibliographic record
Abstract
Staphylococcus aureus is one of the major pathogens causing bovine intramammary infections (IMIs) and mastitis. Mastitis is the primary cause for the use of antibiotics in dairy farms but therapeutic failure is often observed. One of the reasons for the lack of effectiveness of antibiotic therapy despite the observed susceptibility of bacterial isolates in vitro are bacterial biofilms. In this study, we used chitosan of well-defined molecular weight (0.4-0.6, 1.3, 2.6 and 4.0 kDa) and investigated their antibiofilm and antibacterial activities in in vitro and in vivo models related to S. aureus IMIs. A chitosan of at least 6 units of glucosamine was necessary for maximum antibacterial activity. The 2.6 and 4.0 kDa forms were able to prevent biofilm production by the biofilm hyperproducer strain S. aureus 2117 and a bovine MRSA (methicillin-resistant S. aureus). The intramammary administration of the 2.6 kDa chitosan showed no adverse effects in mice or in cows, as opposed to the slight inflammatory effect observed in mammary glands with the 4.0 kDa derivative. The 2.6 kDa chitosan killed bacteria embedded in pre-established biofilms in a dose-dependent manner with a >3 log10 reduction in CFU at 4 mg/ml. Also, the 2.6 kDa chitosan could prevent the persistence of the internalized MRSA into the mammary epithelial cell line MAC-T. An in vitro checkerboard assay showed that the 2.6 kDa chitosan produced a synergy with the macrolide class of antibiotics (e.g., tilmicosin) and reduced the MIC of both molecules by 2-8 times. Finally, the intramammary administration of the 2.6 kDa chitosan alone (P<0.01) or in combination with tilmicosin (P<0.0001) reduced the colonization of mammary glands in a murine IMI model. Our results suggest that the use of chitosan alone or in combination with a low dose of a macrolide could help reduce antibiotic use in dairy farms.
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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) | 0.000 | 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 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".