Antibacterial and synergic effects of gallic acid-<i>grafted</i>-chitosan with β-lactams against methicillin-resistant <i>Staphylococcus aureus</i> (MRSA)
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is spreading worldwide, emphasizing the need to search for new antibiotics. The anti-MRSA activities of gallic acid-grafted-chitosans (GA-g-chitosans) were investigated against 2 MRSA standards and 10 MRSA clinical isolates by determining the minimum inhibitory concentrations (MICs). GA-g-chitosan (I), which has the highest gallic acid content, exhibited the strongest anti-MRSA activities, with MICs of 32-64 μg/mL. A time-kill investigation revealed that GA-g-chitosan (I) exhibited a bactericidal effect at twice the MIC, also demonstrating good thermal and pH stability. Investigation of cell envelope integrity showed the release of intracellular components with an increasing absorbance value at 260 nm, indicating cell envelope damage caused by the GA-g-chitosan (I), which was further confirmed by transmission electron microscopy. When GA-g-chitosans were combined with β-lactams, including ampicillin and penicillin, synergistic effects were observed on the 2 standard MRSA strains and on the 10 clinical isolates, with fractional inhibitory indices ranging from 0.125 to 0.625. In the time-kill dynamic confirmation test, synergistic bactericidal effects were observed for the combinations of GA-g-chitosans with β-lactams, and over 4.0 log CFU/mL reductions were observed after 24 h when combination treatment was used. These results may prove GA-g-chitosans to be a potent agent when combined with ampicillin and penicillin for the elimination of MRSA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| 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 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".