Antibacterial activity of the human host defence peptide LL-37 and selected synthetic cationic lipids against bacteria associated with oral and upper respiratory tract infections
Bibliographic record
Abstract
OBJECTIVES: We aim to develop antibacterial peptide mimics resistant to protease degradation, with broad-spectrum activity at sites of infection. METHODS: The bactericidal activities of LL-37, ceragenins CSA-13, CSA-90 and CSA-92 and the spermine-conjugated dexamethasone derivative D2S were evaluated using MIC and MBC measurements. Gingival fibroblast counting, interleukin-8 (IL-8) and lactate dehydrogenase (LDH) release from keratinocytes (HaCat) were used to determine effects on cell growth, pro-inflammatory response and toxicity. RESULTS: All tested cationic lipids showed stronger bactericidal activity than LL-37. Incubation of Staphylococcus aureus with half the MIC of LL-37 led to the appearance of bacteria resistant to its bactericidal effects, but identical incubations with CSA-13 or D2S did not produce resistant bacteria. Cathelicidin LL-37 significantly increased the total number of gingival fibroblasts, but ceragenins and D2S did not alter gingival fibroblast growth. Cationic lipids showed no toxicity to HaCat cells at concentrations resulting in bacterial killing. CONCLUSIONS: These data suggest that cationic lipids such as ceragenins warrant further testing as potential novel antibacterial agents.
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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".