Synergistic Antibacterial Effects of Chitosan-Caffeic Acid Conjugate against Antibiotic-Resistant Acne-Related Bacteria
Bibliographic record
Abstract
The object of this study was to discover an alternative therapeutic agent with fewer side effects against acne vulgaris, one of the most common skin diseases. Acne vulgaris is often associated with acne-related bacteria such as Propionibacterium acnes, Staphylococcus epidermidis, Staphylococcus aureus, and Pseudomonas aeruginosa. Some of these bacteria exhibit a resistance against commercial antibiotics that have been used in the treatment of acne vulgaris (tetracycline, erythromycin, and lincomycin). In the current study, we tested in vitro antibacterial effect of chitosan-phytochemical conjugates on acne-related bacteria. Three chitosan-phytochemical conjugates used in this study exhibited stronger antibacterial activity than that of chitosan (unmodified control). Chitosan-caffeic acid conjugate (CCA) showed the highest antibacterial effect on acne-related bacteria along with minimum inhibitory concentration (MIC; 8 to 256 μg/mL). Additionally, the MIC values of antibiotics against antibiotic-resistant P. acnes and P. aeruginosa strains were dramatically reduced in combination with CCA, suggesting that CCA would restore the antibacterial activity of the antibiotics. The analysis of fractional inhibitory concentration (FIC) indices clearly revealed a synergistic antibacterial effect of CCA with antibiotics. Thus, the median sum of FIC (∑FIC) values against the antibiotic-resistant bacterial strains ranged from 0.375 to 0.533 in the combination mode of CCA and antibiotics. The results of the present study suggested a potential possibility of chitosan-phytochemical conjugates in the control of infections related to acne vulgaris.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".